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'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( _snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1_00 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , ):
'''simple docstring'''
if audio_length_in_s is None:
snake_case: List[str] = self.unet.config.sample_size / self.unet.config.sample_rate
snake_case: Dict = audio_length_in_s * self.unet.config.sample_rate
snake_case: List[str] = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
F""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
snake_case: Union[str, Any] = int(SCREAMING_SNAKE_CASE__ )
if sample_size % down_scale_factor != 0:
snake_case: List[str] = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
' process.' )
snake_case: Dict = int(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = next(iter(self.unet.parameters() ) ).dtype
snake_case: Tuple = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and len(SCREAMING_SNAKE_CASE__ ) != batch_size:
raise ValueError(
F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__ )}, but requested an effective batch"""
F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
snake_case: Union[str, Any] = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=self.device , dtype=SCREAMING_SNAKE_CASE__ )
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ , device=audio.device )
snake_case: Tuple = self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
snake_case: Optional[Any] = self.unet(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).sample
# 2. compute previous image: x_t -> t_t-1
snake_case: Any = self.scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
snake_case: List[Any] = audio.clamp(-1 , 1 ).float().cpu().numpy()
snake_case: Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE__ ) | 713 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: List[str] = getattr(__A , __A )
if weight_type is not None:
snake_case: Optional[int] = getattr(__A , __A ).shape
else:
snake_case: Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Optional[int] = value
elif weight_type == "weight_g":
snake_case: List[str] = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: Optional[Any] = value
else:
snake_case: int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: List[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case: Dict = None
for name, value in fairseq_dict.items():
snake_case: Tuple = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
elif name.split('.' )[0] == "proj":
snake_case: List[Any] = fairseq_model.proj
snake_case: int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: int = True
if "*" in mapped_key:
snake_case: List[str] = name.split(__A )[0].split('.' )[-2]
snake_case: Dict = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Tuple = 'weight_g'
elif "weight_v" in name:
snake_case: int = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
snake_case: List[Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCAmelCase_ ( __A : List[str] , __A : List[Any] , __A : int , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: Tuple = name.split('.' )
snake_case: Any = int(items[0] )
snake_case: Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case , snake_case: List[Any] = emb.weight.shape
snake_case: Optional[int] = nn.Linear(__A , __A , bias=__A )
snake_case: Any = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
with open(__A , 'r' , encoding='utf-8' ) as f:
snake_case: List[Any] = f.readlines()
snake_case: Any = [line.split(' ' )[0] for line in lines]
snake_case: int = len(__A )
snake_case: Dict = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(__A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : int , __A : str , ):
'''simple docstring'''
snake_case: Union[str, Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: str = SpeechaTextaConfig.from_pretrained(
__A , vocab_size=__A , decoder_layers=__A , do_stable_layer_norm=__A )
snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
snake_case: List[Any] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case: Optional[Any] = WavaVecaModel(__A )
snake_case: Any = recursively_load_weights_wavaveca(model.encoder , __A )
snake_case: Union[str, Any] = SpeechaTextaForCausalLM(__A )
snake_case , snake_case: Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__A )
# set output linear layer
unexpected_keys.remove('embed_out' )
snake_case: str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
snake_case: int = SpeechEncoderDecoderModel(encoder=__A , decoder=__A )
snake_case: List[Any] = False
# add projection layer
snake_case: Union[str, Any] = nn.Parameter(projection_layer.weight )
snake_case: Union[str, Any] = nn.Parameter(projection_layer.bias )
snake_case: List[Any] = create_vocab_dict(__A )
with open(os.path.join(__A , 'vocab.json' ) , 'w' ) as fp:
json.dump(__A , __A )
snake_case: Union[str, Any] = SpeechaTextaTokenizer(os.path.join(__A , 'vocab.json' ) )
tokenizer.save_pretrained(__A )
snake_case: Tuple = hf_wavavec.config.to_dict()
snake_case: int = tokenizer.pad_token_id
snake_case: Dict = tokenizer.bos_token_id
snake_case: Optional[int] = tokenizer.eos_token_id
snake_case: Dict = 'speech_to_text_2'
snake_case: Optional[Any] = 'wav2vec2'
snake_case: Tuple = SpeechEncoderDecoderConfig.from_dict(__A )
hf_wavavec.save_pretrained(__A )
feature_extractor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
) | 692 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( __A : int , __A : str ):
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def lowerCAmelCase_ ( __A : Dict , __A : List[str] , __A : Tuple ):
'''simple docstring'''
snake_case: Tuple = tmp_path / 'cache'
snake_case: str = {'text': 'string'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case: Dict = TextDatasetReader(lowerCAmelCase__ , cache_dir=lowerCAmelCase__ , keep_in_memory=lowerCAmelCase__ ).read()
_check_text_dataset(lowerCAmelCase__ , lowerCAmelCase__ )
@pytest.mark.parametrize(
'features' , [
None,
{'text': 'string'},
{'text': 'int32'},
{'text': 'float32'},
] , )
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : List[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: str = tmp_path / 'cache'
snake_case: Tuple = {'text': 'string'}
snake_case: List[str] = features.copy() if features else default_expected_features
snake_case: Dict = (
Features({feature: Value(lowerCAmelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case: Optional[Any] = TextDatasetReader(lowerCAmelCase__ , features=lowerCAmelCase__ , cache_dir=lowerCAmelCase__ ).read()
_check_text_dataset(lowerCAmelCase__ , lowerCAmelCase__ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def lowerCAmelCase_ ( __A : int , __A : Optional[int] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = tmp_path / 'cache'
snake_case: Tuple = {'text': 'string'}
snake_case: int = TextDatasetReader(lowerCAmelCase__ , cache_dir=lowerCAmelCase__ , split=lowerCAmelCase__ ).read()
_check_text_dataset(lowerCAmelCase__ , lowerCAmelCase__ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def lowerCAmelCase_ ( __A : Optional[int] , __A : Optional[Any] , __A : Tuple ):
'''simple docstring'''
if issubclass(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case: Any = text_path
elif issubclass(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case: Any = [text_path]
snake_case: str = tmp_path / 'cache'
snake_case: int = {'text': 'string'}
snake_case: Union[str, Any] = TextDatasetReader(lowerCAmelCase__ , cache_dir=lowerCAmelCase__ ).read()
_check_text_dataset(lowerCAmelCase__ , lowerCAmelCase__ )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : int=("train",) ):
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ )
for split in splits:
snake_case: Union[str, Any] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def lowerCAmelCase_ ( __A : Optional[int] , __A : Any , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = tmp_path / 'cache'
snake_case: Tuple = {'text': 'string'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case: Union[str, Any] = TextDatasetReader({'train': text_path} , cache_dir=lowerCAmelCase__ , keep_in_memory=lowerCAmelCase__ ).read()
_check_text_datasetdict(lowerCAmelCase__ , lowerCAmelCase__ )
@pytest.mark.parametrize(
'features' , [
None,
{'text': 'string'},
{'text': 'int32'},
{'text': 'float32'},
] , )
def lowerCAmelCase_ ( __A : str , __A : Union[str, Any] , __A : Tuple ):
'''simple docstring'''
snake_case: Optional[int] = tmp_path / 'cache'
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
snake_case: Dict = {'text': 'string'}
snake_case: Union[str, Any] = features.copy() if features else default_expected_features
snake_case: Union[str, Any] = (
Features({feature: Value(lowerCAmelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case: List[Any] = TextDatasetReader({'train': text_path} , features=lowerCAmelCase__ , cache_dir=lowerCAmelCase__ ).read()
_check_text_datasetdict(lowerCAmelCase__ , lowerCAmelCase__ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] , __A : Any ):
'''simple docstring'''
if split:
snake_case: Optional[int] = {split: text_path}
else:
snake_case: Any = 'train'
snake_case: str = {'train': text_path, 'test': text_path}
snake_case: Optional[int] = tmp_path / 'cache'
snake_case: Tuple = {'text': 'string'}
snake_case: Tuple = TextDatasetReader(lowerCAmelCase__ , cache_dir=lowerCAmelCase__ ).read()
_check_text_datasetdict(lowerCAmelCase__ , lowerCAmelCase__ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() ) | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }') | 692 | 0 |
'''simple docstring'''
import math
import os
import sys
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case: Dict = """"""
try:
with open(__A , 'rb' ) as binary_file:
snake_case: List[Any] = binary_file.read()
for dat in data:
snake_case: Optional[int] = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] , __A : Optional[int] , __A : List[str] ):
'''simple docstring'''
lexicon.pop(__A )
snake_case: str = last_match_id
if math.loga(__A ).is_integer():
for curr_key in lexicon:
snake_case: Any = """0""" + lexicon[curr_key]
snake_case: str = bin(__A )[2:]
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: List[str] = {"""0""": """0""", """1""": """1"""}
snake_case: List[Any] = """""", """"""
snake_case: Dict = len(__A )
for i in range(len(__A ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
snake_case: Any = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(__A , __A , __A , __A )
index += 1
snake_case: Optional[int] = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
snake_case: Tuple = lexicon[curr_string]
result += last_match_id
return result
def lowerCAmelCase_ ( __A : int , __A : Optional[Any] ):
'''simple docstring'''
snake_case: int = os.path.getsize(__A )
snake_case: List[Any] = bin(__A )[2:]
snake_case: Optional[int] = len(__A )
return "0" * (length_length - 1) + file_length_binary + compressed
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Dict ):
'''simple docstring'''
snake_case: int = 8
try:
with open(__A , 'wb' ) as opened_file:
snake_case: Optional[Any] = [
to_write[i : i + byte_length]
for i in range(0 , len(__A ) , __A )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(__A , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def lowerCAmelCase_ ( __A : int , __A : int ):
'''simple docstring'''
snake_case: Optional[Any] = read_file_binary(__A )
snake_case: List[Any] = compress_data(__A )
snake_case: List[str] = add_file_length(__A , __A )
write_file_binary(__A , __A )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2]) | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__UpperCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
snake_case: List[Any] = k.replace(__A , __A )
return k
def lowerCAmelCase_ ( __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[int] = BigBirdPegasusConfig(**__A )
snake_case: List[Any] = BigBirdPegasusForConditionalGeneration(__A )
snake_case: Any = torch_model.state_dict()
snake_case: Any = {}
# separating decoder weights
snake_case: Optional[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
snake_case: Any = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
snake_case: List[str] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Any = DECODER_PATTERNS
snake_case: int = rename_state_dict_key(__A , __A )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: Optional[Any] = v.T
snake_case: Any = torch.from_numpy(__A )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
snake_case: List[Any] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Union[str, Any] = REMAINING_PATTERNS
snake_case: str = rename_state_dict_key(__A , __A )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: int = v.T
snake_case: Any = torch.from_numpy(__A )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
snake_case: str = mapping['model.embed_positions.weight']
snake_case: Any = mapping.pop('model.embed_positions.weight' )
snake_case , snake_case: Union[str, Any] = torch_model.load_state_dict(__A , strict=__A )
snake_case: Optional[int] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Tuple = tf.train.list_variables(__A )
snake_case: str = {}
snake_case: List[str] = ['global_step']
for name, shape in tqdm(__A , desc='converting tf checkpoint to dict' ):
snake_case: str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case: Any = tf.train.load_variable(__A , __A )
snake_case: Optional[int] = array
return tf_weights
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict ):
'''simple docstring'''
snake_case: int = get_tf_weights_as_numpy(__A )
snake_case: int = convert_bigbird_pegasus(__A , __A )
torch_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 692 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class SCREAMING_SNAKE_CASE ( a__ , a__ ):
'''simple docstring'''
__UpperCamelCase = "swin"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**_A )
snake_case: str = image_size
snake_case: List[str] = patch_size
snake_case: str = num_channels
snake_case: Optional[Any] = embed_dim
snake_case: Optional[int] = depths
snake_case: Any = len(_A )
snake_case: Union[str, Any] = num_heads
snake_case: Dict = window_size
snake_case: Dict = mlp_ratio
snake_case: Tuple = qkv_bias
snake_case: Any = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: int = drop_path_rate
snake_case: int = hidden_act
snake_case: Tuple = use_absolute_embeddings
snake_case: List[Any] = layer_norm_eps
snake_case: Optional[Any] = initializer_range
snake_case: Optional[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Union[str, Any] = int(embed_dim * 2 ** (len(_A ) - 1) )
snake_case: Optional[Any] = ['stem'] + [F"""stage{idx}""" for idx in range(1 , len(_A ) + 1 )]
snake_case: Tuple = get_aligned_output_features_output_indices(
out_features=_A , out_indices=_A , stage_names=self.stage_names )
class SCREAMING_SNAKE_CASE ( a__ ):
'''simple docstring'''
__UpperCamelCase = version.parse("1.11" )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return 1E-4 | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case: int = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case: Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__UpperCAmelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 692 | 0 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = "docs/source/en/_toctree.yml"
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: Union[str, Any] = defaultdict(_lowerCamelCase )
snake_case: Dict = []
snake_case: List[Any] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(_lowerCamelCase )
snake_case: str = new_doc_list
snake_case: Optional[int] = [key for key, value in counts.items() if value > 1]
snake_case: Optional[int] = []
for duplicate_key in duplicates:
snake_case: Optional[Any] = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(_lowerCamelCase ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
snake_case: Optional[int] = sorted(_lowerCamelCase , key=lambda __A : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_lowerCamelCase ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(_lowerCamelCase )
# Sort
return overview_doc
def lowerCAmelCase_ ( __A : str=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding='utf-8' ) as f:
snake_case: List[Any] = yaml.safe_load(f.read() )
# Get to the API doc
snake_case: Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
snake_case: List[Any] = content[api_idx]["sections"]
# Then to the model doc
snake_case: List[Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
snake_case: Tuple = api_doc[scheduler_idx]["sections"]
snake_case: int = clean_doc_toc(_lowerCamelCase )
snake_case: List[Any] = False
if new_scheduler_doc != scheduler_doc:
snake_case: List[Any] = True
if overwrite:
snake_case: Tuple = new_scheduler_doc
if diff:
if overwrite:
snake_case: Optional[int] = api_doc
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def lowerCAmelCase_ ( __A : Union[str, Any]=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding='utf-8' ) as f:
snake_case: Optional[Any] = yaml.safe_load(f.read() )
# Get to the API doc
snake_case: Tuple = 0
while content[api_idx]["title"] != "API":
api_idx += 1
snake_case: Any = content[api_idx]["sections"]
# Then to the model doc
snake_case: Any = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
snake_case: Any = False
snake_case: int = api_doc[pipeline_idx]["sections"]
snake_case: Tuple = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
snake_case: str = pipeline_doc["section"]
snake_case: Optional[int] = clean_doc_toc(_lowerCamelCase )
if overwrite:
snake_case: Any = new_sub_pipeline_doc
new_pipeline_docs.append(_lowerCamelCase )
# sort overall pipeline doc
snake_case: Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
snake_case: Tuple = True
if overwrite:
snake_case: Union[str, Any] = new_pipeline_docs
if diff:
if overwrite:
snake_case: int = api_doc
with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__UpperCAmelCase = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite) | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = tempfile.mkdtemp()
snake_case: Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
snake_case: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
snake_case: Optional[int] = {
'do_resize': True,
'size': {'height': 2_24, 'width': 2_24},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'do_convert_rgb': True,
}
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
snake_case: Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer()
snake_case: Union[str, Any] = self.get_image_processor()
snake_case: List[str] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
snake_case: List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
snake_case: Any = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
snake_case: Union[str, Any] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_image_processor()
snake_case: Tuple = self.get_tokenizer()
snake_case: Optional[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.prepare_image_inputs()
snake_case: List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
snake_case: Dict = processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_image_processor()
snake_case: Optional[int] = self.get_tokenizer()
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Union[str, Any] = processor(text=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Tuple = self.prepare_image_inputs()
snake_case: Any = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.get_image_processor()
snake_case: str = self.get_tokenizer()
snake_case: Union[str, Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case: int = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = 'Alexandra，T-shirt的价格是15便士。'
snake_case: List[Any] = self.prepare_image_inputs()
snake_case: Dict = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 692 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
__UpperCAmelCase = "\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {'f1': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results['f1'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results['f1'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n >>> print(round(results['f1'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'f1': array([0.8, 0. , 0. ])}\n"
__UpperCAmelCase = "\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32' ) ),
'references': datasets.Sequence(datasets.Value('int32' ) ),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html'] , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="binary" , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: int = fa_score(
lowerCamelCase_ , lowerCamelCase_ , labels=lowerCamelCase_ , pos_label=lowerCamelCase_ , average=lowerCamelCase_ , sample_weight=lowerCamelCase_ )
return {"f1": float(lowerCamelCase_ ) if score.size == 1 else score} | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "swinv2"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: int = image_size
snake_case: Union[str, Any] = patch_size
snake_case: List[str] = num_channels
snake_case: Tuple = embed_dim
snake_case: str = depths
snake_case: Any = len(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = num_heads
snake_case: Optional[int] = window_size
snake_case: Any = mlp_ratio
snake_case: Optional[int] = qkv_bias
snake_case: Union[str, Any] = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: Dict = drop_path_rate
snake_case: List[str] = hidden_act
snake_case: int = use_absolute_embeddings
snake_case: Any = layer_norm_eps
snake_case: Dict = initializer_range
snake_case: List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Tuple = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
snake_case: Union[str, Any] = (0, 0, 0, 0) | 692 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = field(default="automatic-speech-recognition" , metadata={"include_in_asdict_even_if_is_default": True} )
__UpperCamelCase = Features({"audio": Audio()} )
__UpperCamelCase = Features({"transcription": Value("string" )} )
__UpperCamelCase = "audio"
__UpperCamelCase = "transcription"
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.audio_column not in features:
raise ValueError(F"""Column {self.audio_column} is not present in features.""" )
if not isinstance(features[self.audio_column] , __UpperCamelCase ):
raise ValueError(F"""Column {self.audio_column} is not an Audio type.""" )
snake_case: List[str] = copy.deepcopy(self )
snake_case: str = self.input_schema.copy()
snake_case: int = features[self.audio_column]
snake_case: List[str] = input_schema
return task_template
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 719 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = graph
# mapping node to its parent in resulting breadth first tree
snake_case: str = {}
snake_case: Optional[int] = source_vertex
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = {self.source_vertex}
snake_case: Tuple = None
snake_case: Optional[Any] = [self.source_vertex] # first in first out queue
while queue:
snake_case: List[Any] = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(snake_case_ )
snake_case: str = vertex
queue.append(snake_case_ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
snake_case: Union[str, Any] = self.parent.get(snake_case_ )
if target_vertex_parent is None:
snake_case: Union[str, Any] = (
F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(snake_case_ )
return self.shortest_path(snake_case_ ) + F"""->{target_vertex}"""
if __name__ == "__main__":
__UpperCAmelCase = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo")) | 720 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = question_encoder
snake_case: Union[str, Any] = generator
snake_case: Optional[int] = self.question_encoder
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case: int = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
snake_case: str = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
snake_case: Dict = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.question_encoder
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.generator
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "longest" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
snake_case: Optional[Any] = self.current_tokenizer.model_max_length
snake_case: int = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case: Any = self.current_tokenizer.model_max_length
snake_case: List[str] = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: Dict = labels['input_ids']
return model_inputs | 692 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__UpperCAmelCase = '''src/transformers'''
__UpperCAmelCase = '''docs/source/en'''
__UpperCAmelCase = '''.'''
def lowerCAmelCase_ ( __A : Any , __A : List[str] , __A : Optional[int] ):
'''simple docstring'''
with open(__UpperCamelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
snake_case: int = f.readlines()
# Find the start prompt.
snake_case: int = 0
while not lines[start_index].startswith(__UpperCamelCase ):
start_index += 1
start_index += 1
snake_case: Union[str, Any] = start_index
while not lines[end_index].startswith(__UpperCamelCase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
__UpperCAmelCase = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__UpperCAmelCase = re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__UpperCAmelCase = re.compile(R"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__UpperCAmelCase = re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: int = re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' , __UpperCamelCase )
return [m.group(0 ) for m in matches]
def lowerCAmelCase_ ( __A : int , __A : int ):
'''simple docstring'''
snake_case: Dict = 2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase )
snake_case: Tuple = (width - text_length) // 2
snake_case: Any = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: List[Any] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
snake_case: Optional[Any] = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
snake_case: List[str] = {name: config.replace('Config' , '' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
snake_case: Dict = collections.defaultdict(__UpperCamelCase )
snake_case: List[Any] = collections.defaultdict(__UpperCamelCase )
snake_case: str = collections.defaultdict(__UpperCamelCase )
snake_case: str = collections.defaultdict(__UpperCamelCase )
snake_case: Dict = collections.defaultdict(__UpperCamelCase )
# Let's lookup through all transformers object (once).
for attr_name in dir(__UpperCamelCase ):
snake_case: Union[str, Any] = None
if attr_name.endswith('Tokenizer' ):
snake_case: Dict = slow_tokenizers
snake_case: Any = attr_name[:-9]
elif attr_name.endswith('TokenizerFast' ):
snake_case: Optional[Any] = fast_tokenizers
snake_case: str = attr_name[:-13]
elif _re_tf_models.match(__UpperCamelCase ) is not None:
snake_case: List[Any] = tf_models
snake_case: Tuple = _re_tf_models.match(__UpperCamelCase ).groups()[0]
elif _re_flax_models.match(__UpperCamelCase ) is not None:
snake_case: Dict = flax_models
snake_case: Tuple = _re_flax_models.match(__UpperCamelCase ).groups()[0]
elif _re_pt_models.match(__UpperCamelCase ) is not None:
snake_case: List[Any] = pt_models
snake_case: Any = _re_pt_models.match(__UpperCamelCase ).groups()[0]
if lookup_dict is not None:
while len(__UpperCamelCase ) > 0:
if attr_name in model_name_to_prefix.values():
snake_case: Any = True
break
# Try again after removing the last word in the name
snake_case: Tuple = """""".join(camel_case_split(__UpperCamelCase )[:-1] )
# Let's build that table!
snake_case: Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
snake_case: str = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
snake_case: str = [len(__UpperCamelCase ) + 2 for c in columns]
snake_case: Dict = max([len(__UpperCamelCase ) for name in model_names] ) + 2
# Build the table per se
snake_case: List[Any] = """|""" + """|""".join([_center_text(__UpperCamelCase , __UpperCamelCase ) for c, w in zip(__UpperCamelCase , __UpperCamelCase )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths] ) + "|\n"
snake_case: List[Any] = {True: """✅""", False: """❌"""}
for name in model_names:
snake_case: Union[str, Any] = model_name_to_prefix[name]
snake_case: Optional[int] = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__UpperCamelCase , __UpperCamelCase ) for l, w in zip(__UpperCamelCase , __UpperCamelCase )] ) + "|\n"
return table
def lowerCAmelCase_ ( __A : Any=False ):
'''simple docstring'''
snake_case: Optional[int] = _find_text_in_file(
filename=os.path.join(__UpperCamelCase , 'index.md' ) , start_prompt='<!--This table is updated automatically from the auto modules' , end_prompt='<!-- End table-->' , )
snake_case: int = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__UpperCamelCase , 'index.md' ) , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__UpperCAmelCase = parser.parse_args()
check_model_table(args.fix_and_overwrite) | 721 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'mock-s3-bucket'
snake_case: int = f"""s3://{mock_bucket}"""
snake_case: Any = extract_path_from_uri(__A )
assert dataset_path.startswith('s3://' ) is False
snake_case: Union[str, Any] = './local/path'
snake_case: Union[str, Any] = extract_path_from_uri(__A )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = is_remote_filesystem(__A )
assert is_remote is True
snake_case: int = fsspec.filesystem('file' )
snake_case: int = is_remote_filesystem(__A )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , __A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : str , __A : Optional[Any] , __A : List[str] , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
snake_case: Optional[int] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case: str = f"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=__A )
assert isinstance(__A , __A )
snake_case: Any = os.path.basename(__A )
snake_case: int = expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(__A , 'r' , encoding='utf-8' ) as f, open(__A , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def lowerCAmelCase_ ( __A : Any , __A : int , __A : int ):
'''simple docstring'''
snake_case: List[str] = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
snake_case: str = compressed_file_paths[protocol]
snake_case: Dict = 'dataset.jsonl'
snake_case: Optional[Any] = f"""{protocol}://{member_file_path}::{compressed_file_path}"""
snake_case , *snake_case: List[Any] = fsspec.get_fs_token_paths(__A )
assert fs.isfile(__A )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : List[Any] ):
'''simple docstring'''
snake_case: Tuple = hf_api.dataset_info(__A , token=__A )
snake_case: List[str] = HfFileSystem(repo_info=__A , token=__A )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(__A ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__A , __A , clobber=__A )
with pytest.warns(__A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__A ) == 1
assert (
str(warning_info[0].message )
== f"""A filesystem protocol was already set for {protocol} and will be overwritten."""
) | 692 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: int = 3_84
if "tiny" in model_name:
snake_case: List[Any] = [3, 3, 9, 3]
snake_case: Optional[int] = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
snake_case: Optional[Any] = [3, 3, 27, 3]
snake_case: int = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
snake_case: List[str] = [3, 3, 27, 3]
snake_case: List[Any] = [1_28, 2_56, 5_12, 10_24]
snake_case: List[Any] = 5_12
if "large" in model_name:
snake_case: int = [3, 3, 27, 3]
snake_case: Union[str, Any] = [1_92, 3_84, 7_68, 15_36]
snake_case: List[Any] = 7_68
if "xlarge" in model_name:
snake_case: str = [3, 3, 27, 3]
snake_case: int = [2_56, 5_12, 10_24, 20_48]
snake_case: int = 10_24
# set label information
snake_case: Union[str, Any] = 1_50
snake_case: str = 'huggingface/label-files'
snake_case: Tuple = 'ade20k-id2label.json'
snake_case: List[str] = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
snake_case: str = {int(snake_case__ ): v for k, v in idalabel.items()}
snake_case: Optional[Any] = {v: k for k, v in idalabel.items()}
snake_case: str = ConvNextConfig(
depths=snake_case__ , hidden_sizes=snake_case__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
snake_case: str = UperNetConfig(
backbone_config=snake_case__ , auxiliary_in_channels=snake_case__ , num_labels=snake_case__ , idalabel=snake_case__ , labelaid=snake_case__ , )
return config
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: Tuple = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""") )
if i > 0:
rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( __A : str , __A : Optional[int] , __A : Any ):
'''simple docstring'''
snake_case: Any = dct.pop(snake_case__ )
snake_case: Dict = val
def lowerCAmelCase_ ( __A : Any , __A : str , __A : Tuple ):
'''simple docstring'''
snake_case: List[str] = {
'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
}
snake_case: int = model_name_to_url[model_name]
snake_case: str = torch.hub.load_state_dict_from_url(snake_case__ , map_location='cpu' )['state_dict']
snake_case: Tuple = get_upernet_config(snake_case__ )
snake_case: Dict = UperNetForSemanticSegmentation(snake_case__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
snake_case: Tuple = state_dict.pop(snake_case__ )
if "bn" in key:
snake_case: List[str] = key.replace('bn' , 'batch_norm' )
snake_case: Dict = val
# rename keys
snake_case: Dict = create_rename_keys(snake_case__ )
for src, dest in rename_keys:
rename_key(snake_case__ , snake_case__ , snake_case__ )
model.load_state_dict(snake_case__ )
# verify on image
snake_case: str = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
snake_case: Any = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw ).convert('RGB' )
snake_case: Any = SegformerImageProcessor()
snake_case: Union[str, Any] = processor(snake_case__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
snake_case: Tuple = model(snake_case__ )
if model_name == "upernet-convnext-tiny":
snake_case: Dict = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] )
elif model_name == "upernet-convnext-small":
snake_case: int = torch.tensor(
[[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]] )
elif model_name == "upernet-convnext-base":
snake_case: str = torch.tensor(
[[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]] )
elif model_name == "upernet-convnext-large":
snake_case: str = torch.tensor(
[[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]] )
elif model_name == "upernet-convnext-xlarge":
snake_case: List[str] = torch.tensor(
[[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , snake_case__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case__ )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(snake_case__ )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-convnext-tiny",
type=str,
choices=[F'upernet-convnext-{size}' for size in ["tiny", "small", "base", "large", "xlarge"]],
help="Name of the ConvNext UperNet model you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
__UpperCAmelCase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 700 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__UpperCamelCase = field(
default=1024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the training data."} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A csv or a json file containing the test data."} )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
snake_case: str = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case: Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case: Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case: str = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
snake_case: Tuple = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case: Optional[int] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case: Tuple = data_args.train_file.split('.' )[-1]
snake_case: Union[str, Any] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case: Union[str, Any] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
snake_case: List[Any] = load_dataset('csv' , data_files=__A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case: Optional[Any] = load_dataset('json' , data_files=__A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case: Tuple = raw_datasets['train'].features['label'].names
snake_case: List[str] = len(__A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case: List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
snake_case: Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case: int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case: Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case: Optional[Any] = {'Refused': 0, 'Entailed': 1}
snake_case: List[Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case: List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__A : Any ):
# Tokenize the texts
def _convert_table_text_to_pandas(__A : Dict ):
snake_case: str = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
snake_case: List[str] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case: str = examples['statement']
snake_case: int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
snake_case: List[Any] = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A )
snake_case: List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
snake_case: int = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case: List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case: Tuple = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case: Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case: Optional[int] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
snake_case: str = raw_datasets['test']
if data_args.max_predict_samples is not None:
snake_case: List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
snake_case: int = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
snake_case: List[str] = np.argmax(__A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case: str = default_data_collator
elif training_args.fpaa:
snake_case: List[str] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
snake_case: List[Any] = None
# Initialize our Trainer
snake_case: List[str] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
snake_case: str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: Optional[Any] = last_checkpoint
snake_case: Union[str, Any] = trainer.train(resume_from_checkpoint=__A )
snake_case: List[Any] = train_result.metrics
snake_case: List[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
snake_case: Optional[Any] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , __A )
trainer.save_metrics('train' , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case: Dict = trainer.evaluate(eval_dataset=__A )
snake_case: Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
snake_case: Dict = min(__A , len(__A ) )
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case: Optional[int] = predict_dataset.remove_columns('label' )
snake_case: str = trainer.predict(__A , metric_key_prefix='predict' ).predictions
snake_case: Any = np.argmax(__A , axis=1 )
snake_case: int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(__A , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(__A ):
snake_case: int = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
snake_case: Optional[int] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 692 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , SCREAMING_SNAKE_CASE__ = 7_68 , ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = nn.Parameter(torch.zeros(1 , SCREAMING_SNAKE_CASE__ ) )
snake_case: List[str] = nn.Parameter(torch.ones(1 , SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Optional[Any] = nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ ) )
snake_case: Optional[Any] = nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ ) )
return self
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = (embeds - self.mean) * 1.0 / self.std
return embeds
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = (embeds * self.std) + self.mean
return embeds | 701 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : float = 0.1 ):
'''simple docstring'''
snake_case: Optional[int] = 3
snake_case: int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__A )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Optional[int] , __A : Dict , __A : List[Any] , __A : Union[str, Any] , __A : List[Any] , __A : List[str] ):
'''simple docstring'''
for attribute in key.split('.' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
snake_case: List[str] = 'lm_head'
snake_case: Any = getattr(__A , __A )
if weight_type is not None:
snake_case: Tuple = getattr(__A , __A ).shape
else:
snake_case: Dict = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Union[str, Any] = value
elif weight_type == "weight_g":
snake_case: int = value
elif weight_type == "weight_v":
snake_case: Optional[Any] = value
elif weight_type == "bias":
snake_case: str = value
else:
snake_case: Optional[int] = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : str , __A : Dict , __A : Union[str, Any] ):
'''simple docstring'''
snake_case: Dict = []
snake_case: Tuple = fairseq_model.state_dict()
snake_case: Optional[Any] = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
snake_case: int = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
else:
for key, mapped_key in MAPPING.items():
snake_case: Any = 'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: Tuple = True
if "*" in mapped_key:
snake_case: Tuple = name.split(__A )[0].split('.' )[-2]
snake_case: str = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Any = 'weight_g'
elif "weight_v" in name:
snake_case: List[Any] = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case: Union[str, Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : List[Any] , __A : Union[str, Any] , __A : Optional[int] , __A : int ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: List[Any] = name.split('.' )
snake_case: Tuple = int(items[0] )
snake_case: Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: List[str] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
@torch.no_grad()
def lowerCAmelCase_ ( __A : str , __A : List[str] , __A : Any=None , __A : Any=None , __A : Dict=True ):
'''simple docstring'''
if config_path is not None:
snake_case: List[str] = UniSpeechConfig.from_pretrained(__A )
else:
snake_case: Union[str, Any] = UniSpeechConfig()
if is_finetuned:
if dict_path:
snake_case: str = Dictionary.load_from_json(__A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case: Tuple = target_dict.pad_index
snake_case: int = target_dict.bos_index
snake_case: Any = target_dict.eos_index
snake_case: str = len(target_dict.symbols )
snake_case: Optional[Any] = os.path.join(__A , 'vocab.json' )
if not os.path.isdir(__A ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__A ) )
return
os.makedirs(__A , exist_ok=__A )
snake_case: str = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case: Union[str, Any] = 42
snake_case: Union[str, Any] = 43
with open(__A , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(__A , __A )
snake_case: Union[str, Any] = WavaVecaPhonemeCTCTokenizer(
__A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=__A , )
snake_case: Union[str, Any] = True if config.feat_extract_norm == 'layer' else False
snake_case: Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case: Tuple = WavaVecaProcessor(feature_extractor=__A , tokenizer=__A )
processor.save_pretrained(__A )
snake_case: Optional[Any] = UniSpeechForCTC(__A )
else:
snake_case: str = UniSpeechForPreTraining(__A )
if is_finetuned:
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] ), 'w2v_path': checkpoint_path} )
else:
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case: List[Any] = model[0].eval()
recursively_load_weights(__A , __A , __A )
hf_unispeech.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__UpperCAmelCase = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 702 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] ) | 692 | 0 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
) | 703 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "layer_norm" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = only_cross_attention
snake_case: Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
snake_case: Tuple = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case: List[str] = AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case: str = AdaLayerNormZero(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=SCREAMING_SNAKE_CASE__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case: Tuple = (
AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , upcast_attention=SCREAMING_SNAKE_CASE__ , ) # is self-attn if encoder_hidden_states is none
else:
snake_case: int = None
snake_case: Tuple = None
# 3. Feed-forward
snake_case: Union[str, Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = FeedForward(SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , final_dropout=SCREAMING_SNAKE_CASE__ )
# let chunk size default to None
snake_case: Any = None
snake_case: Any = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = chunk_size
snake_case: str = dim
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
if self.use_ada_layer_norm:
snake_case: Optional[int] = self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case , snake_case , snake_case , snake_case , snake_case: int = self.norma(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=hidden_states.dtype )
else:
snake_case: List[str] = self.norma(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case: List[str] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if self.use_ada_layer_norm_zero:
snake_case: Tuple = gate_msa.unsqueeze(1 ) * attn_output
snake_case: List[str] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case: Dict = (
self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: List[str] = attn_output + hidden_states
# 3. Feed-forward
snake_case: str = self.norma(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: str = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case: List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case: Optional[Any] = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE__ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case: int = self.ff(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case: Tuple = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 4 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: int = int(dim * mult )
snake_case: Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case: int = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if activation_fn == "gelu-approximate":
snake_case: Optional[Any] = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , approximate='tanh' )
elif activation_fn == "geglu":
snake_case: List[Any] = GEGLU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif activation_fn == "geglu-approximate":
snake_case: Optional[int] = ApproximateGELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE__ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for module in self.net:
snake_case: Optional[int] = module(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "none" ):
'''simple docstring'''
super().__init__()
snake_case: Optional[int] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = approximate
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.proj(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = self.gelu(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = nn.Linear(SCREAMING_SNAKE_CASE__ , dim_out * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case: int = self.proj(SCREAMING_SNAKE_CASE__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = self.proj(SCREAMING_SNAKE_CASE__ )
return x * torch.sigmoid(1.7_02 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Optional[Any] = nn.Embedding(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = nn.SiLU()
snake_case: Union[str, Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , embedding_dim * 2 )
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case: Dict = torch.chunk(SCREAMING_SNAKE_CASE__ , 2 )
snake_case: str = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.SiLU()
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , 6 * embedding_dim , bias=SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ , eps=1E-6 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case: str = emb.chunk(6 , dim=1 )
snake_case: Dict = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 1E-5 ):
'''simple docstring'''
super().__init__()
snake_case: str = num_groups
snake_case: str = eps
if act_fn is None:
snake_case: Dict = None
else:
snake_case: List[str] = get_activation(SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , out_dim * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.act:
snake_case: Optional[Any] = self.act(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.linear(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = emb[:, :, None, None]
snake_case , snake_case: List[Any] = emb.chunk(2 , dim=1 )
snake_case: Any = F.group_norm(SCREAMING_SNAKE_CASE__ , self.num_groups , eps=self.eps )
snake_case: Optional[int] = x * (1 + scale) + shift
return x | 692 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( __A : Dict="ro" , __A : str="en" , __A : List[Any]="wmt16" , __A : str=None ):
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('run pip install datasets' )
snake_case: Any = f"""{src_lang}-{tgt_lang}"""
print(f"""Converting {dataset}-{pair}""" )
snake_case: str = datasets.load_dataset(_lowerCamelCase , _lowerCamelCase )
if save_dir is None:
snake_case: str = f"""{dataset}-{pair}"""
snake_case: List[Any] = Path(_lowerCamelCase )
save_dir.mkdir(exist_ok=_lowerCamelCase )
for split in ds.keys():
print(f"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
snake_case: Union[str, Any] = "val" if split == "validation" else split
snake_case: Optional[int] = save_dir.joinpath(f"""{fn}.source""" )
snake_case: Tuple = save_dir.joinpath(f"""{fn}.target""" )
snake_case: Optional[Any] = src_path.open('w+' )
snake_case: str = tgt_path.open('w+' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
snake_case: Optional[Any] = x["translation"]
src_fp.write(ex[src_lang] + '\n' )
tgt_fp.write(ex[tgt_lang] + '\n' )
print(f"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 704 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = RoCBertTokenizer
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = filter_non_english
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: Any = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
snake_case: List[Any] = {}
snake_case: List[str] = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = i
snake_case: Union[str, Any] = i
snake_case: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
snake_case: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: Dict = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
snake_case: Union[str, Any] = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: str = i
snake_case: Optional[int] = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
snake_case: int = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def _UpperCamelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
snake_case: List[str] = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , return_token_type_ids=SCREAMING_SNAKE_CASE__ , return_offsets_mapping=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , )
snake_case: Optional[int] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__ , 'do_lower_case' ) else False
snake_case: int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = ['的', '人', '有']
snake_case: Any = ''.join(SCREAMING_SNAKE_CASE__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = True
snake_case: List[Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = False
snake_case: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: int = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that only the first Chinese character is not preceded by "##".
snake_case: Union[str, Any] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: int = tokenizer.encode('你好' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Any = tokenizer.encode('你是谁' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Dict = '你好，你是谁'
snake_case: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.encode_plus(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = IFInpaintingPipeline
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {"latents"}
def _UpperCamelCase ( self ):
'''simple docstring'''
return self._get_dummy_components()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
'''simple docstring'''
if str(_a ).startswith('mps' ):
snake_case: Optional[Any] = torch.manual_seed(_a )
else:
snake_case: Tuple = torch.Generator(device=_a ).manual_seed(_a )
snake_case: Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(_a ) ).to(_a )
snake_case: Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_a ) ).to(_a )
snake_case: str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def _UpperCamelCase ( self ):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_save_load_local()
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , ) | 705 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCAmelCase = 6378137.0
__UpperCAmelCase = 6356752.314245
__UpperCAmelCase = 6_378_137
def lowerCAmelCase_ ( __A : float , __A : float , __A : float , __A : float ):
'''simple docstring'''
snake_case: Optional[Any] = (AXIS_A - AXIS_B) / AXIS_A
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: Tuple = radians(__A )
snake_case: Tuple = radians(__A )
# Equation
snake_case: List[Any] = sin((phi_a - phi_a) / 2 )
snake_case: Dict = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
snake_case: Union[str, Any] = sqrt(sin_sq_phi + (cos(__A ) * cos(__A ) * sin_sq_lambda) )
return 2 * RADIUS * asin(__A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
__UpperCAmelCase = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = DPRContextEncoderTokenizer
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = DPRQuestionEncoderTokenizer
__UpperCAmelCase = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
__UpperCAmelCase = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
__UpperCAmelCase = R"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(__a )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __call__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
a_ , padding=a_ , truncation=a_ , max_length=a_ , return_tensors=a_ , return_attention_mask=a_ , **a_ , )
elif titles is None or texts is None:
snake_case: Optional[int] = titles if texts is None else texts
return super().__call__(
a_ , a_ , padding=a_ , truncation=a_ , max_length=a_ , return_tensors=a_ , return_attention_mask=a_ , **a_ , )
snake_case: List[Any] = titles if not isinstance(a_ , a_ ) else [titles]
snake_case: Optional[Any] = texts if not isinstance(a_ , a_ ) else [texts]
snake_case: List[Any] = len(a_ )
snake_case: Union[str, Any] = questions if not isinstance(a_ , a_ ) else [questions] * n_passages
assert len(a_ ) == len(
a_ ), F"""There should be as many titles than texts but got {len(a_ )} titles and {len(a_ )} texts."""
snake_case: int = super().__call__(a_ , a_ , padding=a_ , truncation=a_ )["""input_ids"""]
snake_case: List[Any] = super().__call__(a_ , add_special_tokens=a_ , padding=a_ , truncation=a_ )["""input_ids"""]
snake_case: List[str] = {
"""input_ids""": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(a_ , a_ )
]
}
if return_attention_mask is not False:
snake_case: int = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
snake_case: str = attention_mask
return self.pad(a_ , padding=a_ , max_length=a_ , return_tensors=a_ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 16 , SCREAMING_SNAKE_CASE__ = 64 , SCREAMING_SNAKE_CASE__ = 4 , ):
'''simple docstring'''
snake_case: Optional[Any] = reader_input["""input_ids"""]
snake_case: Optional[int] = reader_output[:3]
snake_case: Union[str, Any] = len(a_ )
snake_case: Any = sorted(range(a_ ) , reverse=a_ , key=relevance_logits.__getitem__ )
snake_case: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
snake_case: int = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
snake_case: Dict = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
snake_case: Any = sequence_ids.index(self.pad_token_id )
else:
snake_case: Optional[Any] = len(a_ )
snake_case: List[str] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=a_ , top_spans=a_ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=a_ , start_index=a_ , end_index=a_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(a_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: int = []
for start_index, start_score in enumerate(a_ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
snake_case: Tuple = sorted(a_ , key=lambda SCREAMING_SNAKE_CASE__ : x[1] , reverse=a_ )
snake_case: Optional[int] = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
snake_case: int = end_index - start_index + 1
assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(a_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__a )
class SCREAMING_SNAKE_CASE ( __a , __a ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = READER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = READER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = ["input_ids", "attention_mask"]
__UpperCamelCase = DPRReaderTokenizer | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 692 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
def wrapper(*__A : List[Any] , **__A : Optional[int] ):
snake_case: List[Any] = timeit.default_timer()
snake_case: Optional[Any] = func(*__snake_case , **__snake_case )
snake_case: Dict = timeit.default_timer() - starttime
return delta
snake_case: Union[str, Any] = func.__name__
return wrapper
def lowerCAmelCase_ ( __A : Tuple , __A : Optional[int]=1_00 , __A : Optional[int]=None ):
'''simple docstring'''
snake_case: int = []
snake_case: Optional[int] = seq_shapes or {}
for i in range(__snake_case ):
snake_case: int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(__snake_case , _ArrayXD ):
snake_case: Tuple = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(__snake_case , datasets.Value ):
if v.dtype == "string":
snake_case: List[str] = 'The small grey turtle was surprisingly fast when challenged.'
else:
snake_case: Optional[Any] = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(__snake_case , datasets.Sequence ):
while isinstance(__snake_case , datasets.Sequence ):
snake_case: Optional[int] = v.feature
snake_case: Dict = seq_shapes[k]
snake_case: Optional[int] = np.random.rand(*__snake_case ).astype(v.dtype )
snake_case: str = data
dummy_data.append((i, example) )
return dummy_data
def lowerCAmelCase_ ( __A : Dict , __A : List[str] , __A : int=1_00 , __A : Tuple=None ):
'''simple docstring'''
snake_case: Optional[int] = generate_examples(__snake_case , num_examples=__snake_case , seq_shapes=__snake_case )
with ArrowWriter(features=__snake_case , path=__snake_case ) as writer:
for key, record in dummy_data:
snake_case: Tuple = features.encode_example(__snake_case )
writer.write(__snake_case )
snake_case , snake_case: int = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.""" )
snake_case: str = datasets.Dataset.from_file(filename=__snake_case , info=datasets.DatasetInfo(features=__snake_case ) )
return dataset | 707 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
snake_case: Tuple = model.config
snake_case: str = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
snake_case: Optional[Any] = MBartConfig(
is_decoder=__A , is_encoder_decoder=__A , add_cross_attention=__A , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__A , add_final_layer_norm=__A , )
return encoder_config, decoder_config
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if "encoder.model" in name:
snake_case: Optional[Any] = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
snake_case: str = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
snake_case: Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
snake_case: Optional[int] = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
snake_case: Tuple = 'encoder.' + name
if "attn.proj" in name:
snake_case: Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
snake_case: Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
snake_case: Union[str, Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
snake_case: Dict = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
snake_case: List[str] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
snake_case: Dict = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
snake_case: Dict = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
snake_case: int = 'encoder.layernorm.bias'
return name
def lowerCAmelCase_ ( __A : List[Any] , __A : Optional[Any] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case: List[Any] = orig_state_dict.pop(__A )
if "qkv" in key:
snake_case: Union[str, Any] = key.split('.' )
snake_case: Optional[Any] = int(key_split[3] )
snake_case: Any = int(key_split[5] )
snake_case: Union[str, Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case: Union[str, Any] = val[:dim, :]
snake_case: Any = val[dim : dim * 2, :]
snake_case: List[str] = val[-dim:, :]
else:
snake_case: str = val[:dim]
snake_case: Union[str, Any] = val[dim : dim * 2]
snake_case: List[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
snake_case: Optional[int] = val
return orig_state_dict
def lowerCAmelCase_ ( __A : List[Any] , __A : Any=None , __A : List[str]=False ):
'''simple docstring'''
snake_case: str = DonutModel.from_pretrained(__A ).eval()
# load HuggingFace model
snake_case , snake_case: Optional[Any] = get_configs(__A )
snake_case: Optional[int] = DonutSwinModel(__A )
snake_case: Tuple = MBartForCausalLM(__A )
snake_case: Optional[Any] = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
snake_case: Optional[int] = original_model.state_dict()
snake_case: Optional[int] = convert_state_dict(__A , __A )
model.load_state_dict(__A )
# verify results on scanned document
snake_case: Union[str, Any] = load_dataset('hf-internal-testing/example-documents' )
snake_case: str = dataset['test'][0]['image'].convert('RGB' )
snake_case: Optional[int] = XLMRobertaTokenizerFast.from_pretrained(__A , from_slow=__A )
snake_case: Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
snake_case: Dict = DonutProcessor(__A , __A )
snake_case: Optional[Any] = processor(__A , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
snake_case: int = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
snake_case: Optional[Any] = 'When is the coffee break?'
snake_case: Optional[int] = task_prompt.replace('{user_input}' , __A )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
snake_case: Dict = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
snake_case: str = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
snake_case: str = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
snake_case: int = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
snake_case: Optional[Any] = 'hello world'
else:
raise ValueError('Model name not supported' )
snake_case: Optional[int] = original_model.decoder.tokenizer(__A , add_special_tokens=__A , return_tensors='pt' )[
'input_ids'
]
snake_case: Any = original_model.encoder.model.patch_embed(__A )
snake_case , snake_case: Dict = model.encoder.embeddings(__A )
assert torch.allclose(__A , __A , atol=1E-3 )
# verify encoder hidden states
snake_case: Tuple = original_model.encoder(__A )
snake_case: List[str] = model.encoder(__A ).last_hidden_state
assert torch.allclose(__A , __A , atol=1E-2 )
# verify decoder hidden states
snake_case: List[Any] = original_model(__A , __A , __A ).logits
snake_case: List[Any] = model(__A , decoder_input_ids=__A ).logits
assert torch.allclose(__A , __A , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
__UpperCAmelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 692 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.2 , SCREAMING_SNAKE_CASE__=0.2 ):
'''simple docstring'''
snake_case: Optional[int] = bp_numa
snake_case: int = bp_numa
snake_case: Optional[int] = bp_numa
snake_case: List[str] = conva_get[:2]
snake_case: str = conva_get[2]
snake_case: Tuple = size_pa
snake_case: Union[str, Any] = rate_w
snake_case: Optional[Any] = rate_t
snake_case: str = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
snake_case: Dict = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
snake_case: List[Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
snake_case: Union[str, Any] = -2 * np.random.rand(self.conva[1] ) + 1
snake_case: Union[str, Any] = -2 * np.random.rand(self.num_bpa ) + 1
snake_case: List[Any] = -2 * np.random.rand(self.num_bpa ) + 1
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = {
"""num_bp1""": self.num_bpa,
"""num_bp2""": self.num_bpa,
"""num_bp3""": self.num_bpa,
"""conv1""": self.conva,
"""step_conv1""": self.step_conva,
"""size_pooling1""": self.size_poolinga,
"""rate_weight""": self.rate_weight,
"""rate_thre""": self.rate_thre,
"""w_conv1""": self.w_conva,
"""wkj""": self.wkj,
"""vji""": self.vji,
"""thre_conv1""": self.thre_conva,
"""thre_bp2""": self.thre_bpa,
"""thre_bp3""": self.thre_bpa,
}
with open(lowercase_ , 'wb' ) as f:
pickle.dump(lowercase_ , lowercase_ )
print(F"""Model saved： {save_path}""" )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
with open(lowercase_ , 'rb' ) as f:
snake_case: List[Any] = pickle.load(lowercase_ ) # noqa: S301
snake_case: Any = model_dic.get('conv1' )
conv_get.append(model_dic.get('step_conv1' ) )
snake_case: Tuple = model_dic.get('size_pooling1' )
snake_case: Tuple = model_dic.get('num_bp1' )
snake_case: Dict = model_dic.get('num_bp2' )
snake_case: Tuple = model_dic.get('num_bp3' )
snake_case: List[str] = model_dic.get('rate_weight' )
snake_case: Optional[int] = model_dic.get('rate_thre' )
# create model instance
snake_case: List[str] = CNN(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# modify model parameter
snake_case: int = model_dic.get('w_conv1' )
snake_case: str = model_dic.get('wkj' )
snake_case: List[Any] = model_dic.get('vji' )
snake_case: Union[str, Any] = model_dic.get('thre_conv1' )
snake_case: Union[str, Any] = model_dic.get('thre_bp2' )
snake_case: List[str] = model_dic.get('thre_bp3' )
return conv_ins
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-1 * x ))
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return round(lowercase_ , 3 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = convs[0]
snake_case: Tuple = convs[1]
snake_case: Tuple = np.shape(lowercase_ )[0]
# get the data slice of original image data, data_focus
snake_case: Dict = []
for i_focus in range(0 , size_data - size_conv + 1 , lowercase_ ):
for j_focus in range(0 , size_data - size_conv + 1 , lowercase_ ):
snake_case: Optional[int] = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(lowercase_ )
# calculate the feature map of every single kernel, and saved as list of matrix
snake_case: List[Any] = []
snake_case: int = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(lowercase_ ):
snake_case: Any = []
for i_focus in range(len(lowercase_ ) ):
snake_case: List[Any] = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(lowercase_ ) )
snake_case: Optional[int] = np.asmatrix(lowercase_ ).reshape(
lowercase_ , lowercase_ )
data_featuremap.append(lowercase_ )
# expanding the data slice to One dimenssion
snake_case: Optional[Any] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(lowercase_ ) )
snake_case: Tuple = np.asarray(lowercase_ )
return focus_list, data_featuremap
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="average_pool" ):
'''simple docstring'''
snake_case: int = len(featuremaps[0] )
snake_case: Optional[int] = int(size_map / size_pooling )
snake_case: Dict = []
for i_map in range(len(lowercase_ ) ):
snake_case: Any = featuremaps[i_map]
snake_case: List[Any] = []
for i_focus in range(0 , lowercase_ , lowercase_ ):
for j_focus in range(0 , lowercase_ , lowercase_ ):
snake_case: List[Any] = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(lowercase_ ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(lowercase_ ) )
snake_case: int = np.asmatrix(lowercase_ ).reshape(lowercase_ , lowercase_ )
featuremap_pooled.append(lowercase_ )
return featuremap_pooled
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = []
for i in range(len(lowercase_ ) ):
snake_case: List[str] = np.shape(data[i] )
snake_case: Optional[Any] = data[i].reshape(1 , shapes[0] * shapes[1] )
snake_case: int = data_listed.getA().tolist()[0]
data_expanded.extend(lowercase_ )
snake_case: Union[str, Any] = np.asarray(lowercase_ )
return data_expanded
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = np.asarray(lowercase_ )
snake_case: Optional[Any] = np.shape(lowercase_ )
snake_case: Tuple = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = []
snake_case: List[str] = 0
for i_map in range(lowercase_ ):
snake_case: Any = np.ones((size_map, size_map) )
for i in range(0 , lowercase_ , lowercase_ ):
for j in range(0 , lowercase_ , lowercase_ ):
snake_case: List[str] = pd_pool[
i_pool
]
snake_case: Tuple = i_pool + 1
snake_case: Dict = np.multiply(
lowercase_ , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(lowercase_ )
return pd_all
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=bool ):
'''simple docstring'''
print('----------------------Start Training-------------------------' )
print((' - - Shape: Train_Data ', np.shape(lowercase_ )) )
print((' - - Shape: Teach_Data ', np.shape(lowercase_ )) )
snake_case: Tuple = 0
snake_case: Optional[Any] = []
snake_case: str = 1_00_00
while rp < n_repeat and mse >= error_accuracy:
snake_case: Any = 0
print(F"""-------------Learning Time {rp}--------------""" )
for p in range(len(lowercase_ ) ):
# print('------------Learning Image: %d--------------'%p)
snake_case: Any = np.asmatrix(datas_train[p] )
snake_case: Optional[int] = np.asarray(datas_teach[p] )
snake_case: List[Any] = self.convolute(
lowercase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case: List[Any] = self.pooling(lowercase_ , self.size_poolinga )
snake_case: List[str] = np.shape(lowercase_ )
snake_case: Dict = self._expand(lowercase_ )
snake_case: Tuple = data_bp_input
snake_case: Union[str, Any] = np.dot(lowercase_ , self.vji.T ) - self.thre_bpa
snake_case: Tuple = self.sig(lowercase_ )
snake_case: Tuple = np.dot(lowercase_ , self.wkj.T ) - self.thre_bpa
snake_case: Tuple = self.sig(lowercase_ )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
snake_case: int = np.multiply(
(data_teach - bp_outa) , np.multiply(lowercase_ , (1 - bp_outa) ) )
snake_case: str = np.multiply(
np.dot(lowercase_ , self.wkj ) , np.multiply(lowercase_ , (1 - bp_outa) ) )
snake_case: List[Any] = np.dot(lowercase_ , self.vji )
snake_case: Optional[int] = pd_i_all / (self.size_poolinga * self.size_poolinga)
snake_case: Tuple = pd_conva_pooled.T.getA().tolist()
snake_case: Any = self._calculate_gradient_from_pool(
lowercase_ , lowercase_ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
snake_case: Dict = self._expand_mat(pd_conva_all[k_conv] )
snake_case: Union[str, Any] = self.rate_weight * np.dot(lowercase_ , lowercase_ )
snake_case: str = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
snake_case: Optional[Any] = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
snake_case: Optional[int] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
snake_case: int = self.vji + pd_j_all.T * bp_outa * self.rate_weight
snake_case: List[Any] = self.thre_bpa - pd_k_all * self.rate_thre
snake_case: Union[str, Any] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
snake_case: Any = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
snake_case: Dict = rp + 1
snake_case: Any = error_count / patterns
all_mse.append(lowercase_ )
def draw_error():
snake_case: str = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(lowercase_ , '+-' )
plt.plot(lowercase_ , 'r--' )
plt.xlabel('Learning Times' )
plt.ylabel('All_mse' )
plt.grid(lowercase_ , alpha=0.5 )
plt.show()
print('------------------Training Complished---------------------' )
print((' - - Training epoch: ', rp, F""" - - Mse: {mse:.6f}""") )
if draw_e:
draw_error()
return mse
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[Any] = []
print('-------------------Start Testing-------------------------' )
print((' - - Shape: Test_Data ', np.shape(lowercase_ )) )
for p in range(len(lowercase_ ) ):
snake_case: Any = np.asmatrix(datas_test[p] )
snake_case: Any = self.convolute(
lowercase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case: List[str] = self.pooling(lowercase_ , self.size_poolinga )
snake_case: List[Any] = self._expand(lowercase_ )
snake_case: str = data_bp_input
snake_case: int = bp_outa * self.vji.T - self.thre_bpa
snake_case: Union[str, Any] = self.sig(lowercase_ )
snake_case: Tuple = bp_outa * self.wkj.T - self.thre_bpa
snake_case: Any = self.sig(lowercase_ )
produce_out.extend(bp_outa.getA().tolist() )
snake_case: List[Any] = [list(map(self.do_round , lowercase_ ) ) for each in produce_out]
return np.asarray(lowercase_ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: str = np.asmatrix(lowercase_ )
snake_case: Optional[int] = self.convolute(
lowercase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case: Dict = self.pooling(lowercase_ , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass | 708 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
snake_case: Union[str, Any] = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 1_28,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 1_42,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , x.transpose() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = np.random.randn(3 , 4 )
snake_case: Optional[Any] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(3 , 4 , 5 )
snake_case: Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Dict = np.random.randn(3 , 4 , 5 )
snake_case: str = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Optional[int] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Optional[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) )
snake_case: Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: str = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: List[str] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.squeeze(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: List[str] = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
snake_case: List[str] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: int = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = np.random.randn(1 , 3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(1 , 3 , 4 )
snake_case: List[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Tuple = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Tuple = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Any = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = np.random.randn(3 , 4 )
snake_case: int = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.asarray(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) ) ) | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( __A : list ):
'''simple docstring'''
if len(_lowercase ) == 0:
return []
snake_case: Optional[Any] = min(_lowercase ), max(_lowercase )
snake_case: Union[str, Any] = int(max_value - min_value ) + 1
snake_case: list[list] = [[] for _ in range(_lowercase )]
for i in my_list:
buckets[int(i - min_value )].append(_lowercase )
return [v for bucket in buckets for v in sorted(_lowercase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 709 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__UpperCAmelCase = logging.get_logger(__name__)
# General docstring
__UpperCAmelCase = "PoolFormerConfig"
# Base docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = [1, 512, 7, 7]
# Image classification docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = "tabby, tabby cat"
__UpperCAmelCase = [
"sail/poolformer_s12",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowerCAmelCase_ ( __A : Tuple , __A : float = 0.0 , __A : bool = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
snake_case: Union[str, Any] = 1 - drop_prob
snake_case: List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
snake_case: List[Any] = keep_prob + torch.rand(__A , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
snake_case: Any = input.div(__A ) * random_tensor
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = drop_prob
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return drop_path(SCREAMING_SNAKE_CASE__ , self.drop_prob , self.training )
def _UpperCamelCase ( self ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = patch_size if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (patch_size, patch_size)
snake_case: List[str] = stride if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (stride, stride)
snake_case: Union[str, Any] = padding if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (padding, padding)
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = norm_layer(SCREAMING_SNAKE_CASE__ ) if norm_layer else nn.Identity()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.projection(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.norm(SCREAMING_SNAKE_CASE__ )
return embeddings
class SCREAMING_SNAKE_CASE ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.AvgPoolad(SCREAMING_SNAKE_CASE__ , stride=1 , padding=pool_size // 2 , count_include_pad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.pool(SCREAMING_SNAKE_CASE__ ) - hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: str = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ )
if isinstance(config.hidden_act , SCREAMING_SNAKE_CASE__ ):
snake_case: Tuple = ACTaFN[config.hidden_act]
else:
snake_case: int = config.hidden_act
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.act_fn(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.drop(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: str = self.drop(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = PoolFormerPooling(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerOutput(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
# Useful for training neural nets
snake_case: Union[str, Any] = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ ) if drop_path > 0.0 else nn.Identity()
snake_case: Optional[Any] = config.use_layer_scale
if config.use_layer_scale:
snake_case: Any = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.use_layer_scale:
snake_case: str = self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Dict = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
snake_case: str = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = ()
snake_case: Dict = self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Union[str, Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
snake_case: Any = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = (output,) + outputs
return outputs
else:
snake_case: Optional[Any] = self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) ) )
# First residual connection
snake_case: Union[str, Any] = pooling_output + hidden_states
snake_case: List[Any] = ()
# Second residual connection inside the PoolFormerOutput block
snake_case: List[str] = self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: Dict = hidden_states + layer_output
snake_case: Optional[Any] = (output,) + outputs
return outputs
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = config
# stochastic depth decay rule
snake_case: List[Any] = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
snake_case: Union[str, Any] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
snake_case: List[Any] = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
# Transformer blocks
snake_case: str = []
snake_case: int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
snake_case: List[str] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
SCREAMING_SNAKE_CASE__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__ ) )
snake_case: Tuple = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True ):
'''simple docstring'''
snake_case: str = () if output_hidden_states else None
snake_case: Dict = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
snake_case , snake_case: Dict = layers
# Get patch embeddings from hidden_states
snake_case: int = embedding_layer(SCREAMING_SNAKE_CASE__ )
# Send the embeddings through the blocks
for _, blk in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = blk(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = layer_outputs[0]
if output_hidden_states:
snake_case: List[str] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = PoolFormerConfig
__UpperCamelCase = "poolformer"
__UpperCamelCase = "pixel_values"
__UpperCamelCase = True
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE__ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = value
__UpperCAmelCase = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
__UpperCAmelCase = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n"
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = config
snake_case: Tuple = PoolFormerEncoder(SCREAMING_SNAKE_CASE__ )
# Initialize weights and apply final processing
self.post_init()
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case: List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
snake_case: Optional[Any] = self.encoder(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: List[Any] = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Any = nn.Linear(config.hidden_size , config.hidden_size )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.dense(SCREAMING_SNAKE_CASE__ )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = config.num_labels
snake_case: str = PoolFormerModel(SCREAMING_SNAKE_CASE__ )
# Final norm
snake_case: int = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
snake_case: Dict = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
snake_case: Optional[Any] = self.poolformer(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: Any = outputs[0]
snake_case: str = self.classifier(self.norm(SCREAMING_SNAKE_CASE__ ).mean([-2, -1] ) )
snake_case: Any = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case: Tuple = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case: Dict = 'single_label_classification'
else:
snake_case: List[str] = 'multi_label_classification'
if self.config.problem_type == "regression":
snake_case: Union[str, Any] = MSELoss()
if self.num_labels == 1:
snake_case: List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case: int = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.config.problem_type == "single_label_classification":
snake_case: Union[str, Any] = CrossEntropyLoss()
snake_case: Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case: int = BCEWithLogitsLoss()
snake_case: Optional[int] = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not return_dict:
snake_case: str = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states ) | 692 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowerCAmelCase_ ( __A : Optional[Any] ): # picklable for multiprocessing
'''simple docstring'''
return x.sum()
def lowerCAmelCase_ ( __A : Optional[Any] ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = 42
__UpperCamelCase = 42
class SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = {}
snake_case: Optional[Any] = []
snake_case: Union[str, Any] = 1
snake_case: Any = [1, 2]
snake_case: int = {'a': 1, 'b': 2}
snake_case: Dict = {'a': [1, 2], 'b': [3, 4]}
snake_case: int = {'a': {'1': 1}, 'b': 2}
snake_case: str = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
snake_case: List[str] = {}
snake_case: Optional[Any] = []
snake_case: str = 2
snake_case: Union[str, Any] = [2, 3]
snake_case: Optional[int] = {'a': 2, 'b': 3}
snake_case: List[Any] = {'a': [2, 3], 'b': [4, 5]}
snake_case: Optional[Any] = {'a': {'1': 2}, 'b': 3}
snake_case: List[Any] = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
snake_case: Optional[Any] = 2
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
snake_case: int = {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
snake_case: int = {'a': 2, 'b': 0, 'c': 2}
snake_case: Optional[Any] = {
'a': np.eye(2 ).astype(__lowerCAmelCase ),
'b': np.zeros(3 ).astype(__lowerCAmelCase ),
'c': np.ones(2 ).astype(__lowerCAmelCase ),
}
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase , num_proc=__lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(__lowerCAmelCase ): # can't pickle a local lambda
map_nested(lambda SCREAMING_SNAKE_CASE__ : x + 1 , __lowerCAmelCase , num_proc=__lowerCAmelCase )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = {'a': 1, 'b': 2}
snake_case: Union[str, Any] = {'a': 3, 'b': 4}
snake_case: Optional[Any] = {'a': 5, 'b': 6}
snake_case: Optional[int] = sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) ) , __lowerCAmelCase )
def _UpperCamelCase ( self ):
'''simple docstring'''
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = "bar"
snake_case: str = Foo()
self.assertEqual(foo.my_attr , 'bar' )
with temporary_assignment(__lowerCAmelCase , 'my_attr' , 'BAR' ):
self.assertEqual(foo.my_attr , 'BAR' )
self.assertEqual(foo.my_attr , 'bar' )
@pytest.mark.parametrize(
'iterable_length, num_proc, expected_num_proc' , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowerCAmelCase_ ( __A : Any , __A : str , __A : str ):
'''simple docstring'''
with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
snake_case: Union[str, Any] = {f"""{i}""": i for i in range(UpperCAmelCase__ )}
snake_case: str = map_nested(lambda __A : x + 10 , UpperCAmelCase__ , num_proc=UpperCAmelCase__ , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
'''simple docstring'''
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
import tensorflow as tf
from tensorflow.keras import layers
snake_case: Optional[Any] = layers.Dense(2 )
def gen_random_output():
snake_case: List[Any] = tf.random.uniform((1, 3) )
return model(__lowerCAmelCase ).numpy()
with temp_seed(42 , set_tensorflow=__lowerCAmelCase ):
snake_case: Tuple = gen_random_output()
with temp_seed(42 , set_tensorflow=__lowerCAmelCase ):
snake_case: List[Any] = gen_random_output()
snake_case: Optional[Any] = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
import torch
def gen_random_output():
snake_case: Optional[int] = torch.nn.Linear(3 , 2 )
snake_case: Dict = torch.rand(1 , 3 )
return model(__lowerCAmelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=__lowerCAmelCase ):
snake_case: Tuple = gen_random_output()
with temp_seed(42 , set_pytorch=__lowerCAmelCase ):
snake_case: int = gen_random_output()
snake_case: Optional[int] = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def _UpperCamelCase ( self ):
'''simple docstring'''
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
snake_case: Tuple = gen_random_output()
with temp_seed(42 ):
snake_case: Optional[int] = gen_random_output()
snake_case: Any = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize('input_data' , [{}] )
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
snake_case: List[Any] = NestedDataStructure(UpperCAmelCase__ ).data
assert output_data == input_data
@pytest.mark.parametrize(
'data, expected_output' , [
({}, []),
([], []),
('foo', ['foo']),
(['foo', 'bar'], ['foo', 'bar']),
([['foo', 'bar']], ['foo', 'bar']),
([[['foo'], ['bar']]], ['foo', 'bar']),
([[['foo'], 'bar']], ['foo', 'bar']),
({'a': 1, 'b': 2}, [1, 2]),
({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
({'a': {'1': 1}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
] , )
def lowerCAmelCase_ ( __A : Tuple , __A : Any ):
'''simple docstring'''
snake_case: Tuple = NestedDataStructure(UpperCAmelCase__ ).flatten()
assert output == expected_output
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: List[Any] = A(x=1 , y='foobar' )
snake_case: List[Any] = {'x': 1, 'y': 'foobar'}
assert asdict(UpperCAmelCase__ ) == expected_output
snake_case: List[str] = {'a': {'b': A(x=10 , y='foo' )}, 'c': [A(x=20 , y='bar' )]}
snake_case: Optional[int] = {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
assert asdict(UpperCAmelCase__ ) == expected_output
with pytest.raises(UpperCAmelCase__ ):
asdict([1, A(x=10 , y='foo' )] )
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
return text.split()
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowerCAmelCase_ ( ):
'''simple docstring'''
with Pool(2 ) as pool:
snake_case: str = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(UpperCAmelCase__ ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
snake_case: List[Any] = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(UpperCAmelCase__ ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
snake_case: Tuple = []
for yield_time, content in iflatmap_unordered(
UpperCAmelCase__ , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(UpperCAmelCase__ )
assert out.count('a' ) == 2
assert out.count('b' ) == 2
assert len(UpperCAmelCase__ ) == 4 | 710 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __A : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case: Any = cst_fwd.get(__A , np.inf )
snake_case: int = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
snake_case: Union[str, Any] = new_cost_f
snake_case: Tuple = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
snake_case: List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[Any] = -1
snake_case: Any = set()
snake_case: str = set()
snake_case: int = {source: 0}
snake_case: Dict = {destination: 0}
snake_case: int = {source: None}
snake_case: Union[str, Any] = {destination: None}
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: Tuple = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
snake_case , snake_case: List[str] = queue_forward.get()
visited_forward.add(__A )
snake_case , snake_case: int = queue_backward.get()
visited_backward.add(__A )
snake_case: str = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
snake_case: Optional[Any] = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
snake_case: Any = shortest_distance
return shortest_path_distance
__UpperCAmelCase = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
__UpperCAmelCase = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
'''simple docstring'''
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case: Union[str, Any] = [tuple(UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else key for key in keys]
snake_case: Union[str, Any] = Counter(UpperCAmelCase_ )
snake_case: List[Any] = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
snake_case: Any = super().construct_mapping(UpperCAmelCase_ , deep=UpperCAmelCase_ )
self._check_no_duplicates_on_constructed_node(UpperCAmelCase_ )
return mapping
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case: Optional[int] = full_content[1:].index('---' ) + 1
snake_case: Optional[int] = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_snake_case )
class SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
with open(UpperCAmelCase_ , encoding='utf-8' ) as readme_file:
snake_case , snake_case: Union[str, Any] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(UpperCAmelCase_ )
else:
return cls()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if path.exists():
with open(UpperCAmelCase_ , encoding='utf-8' ) as readme_file:
snake_case: Optional[Any] = readme_file.read()
else:
snake_case: str = None
snake_case: Dict = self._to_readme(UpperCAmelCase_ )
with open(UpperCAmelCase_ , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(UpperCAmelCase_ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if readme_content is not None:
snake_case , snake_case: str = _split_yaml_from_readme(UpperCAmelCase_ )
snake_case: List[Any] = '---\n' + self.to_yaml_string() + '---\n' + content
else:
snake_case: Dict = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = yaml.load(UpperCAmelCase_ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case: Dict = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**UpperCAmelCase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=UpperCAmelCase_ , allow_unicode=UpperCAmelCase_ , encoding='utf-8' , ).decode('utf-8' )
__UpperCAmelCase = {
"image-classification": [],
"translation": [],
"image-segmentation": [],
"fill-mask": [],
"automatic-speech-recognition": [],
"token-classification": [],
"sentence-similarity": [],
"audio-classification": [],
"question-answering": [],
"summarization": [],
"zero-shot-classification": [],
"table-to-text": [],
"feature-extraction": [],
"other": [],
"multiple-choice": [],
"text-classification": [],
"text-to-image": [],
"text2text-generation": [],
"zero-shot-image-classification": [],
"tabular-classification": [],
"tabular-regression": [],
"image-to-image": [],
"tabular-to-text": [],
"unconditional-image-generation": [],
"text-retrieval": [],
"text-to-speech": [],
"object-detection": [],
"audio-to-audio": [],
"text-generation": [],
"conversational": [],
"table-question-answering": [],
"visual-question-answering": [],
"image-to-text": [],
"reinforcement-learning": [],
"voice-activity-detection": [],
"time-series-forecasting": [],
"document-question-answering": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__UpperCAmelCase = ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
ap.add_argument("readme_filepath")
__UpperCAmelCase = ap.parse_args()
__UpperCAmelCase = Path(args.readme_filepath)
__UpperCAmelCase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath) | 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "▁"
__UpperCAmelCase = {"vocab_file": "sentencepiece.bpe.model"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__UpperCAmelCase = {
"facebook/xglm-564M": 2_048,
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
snake_case: Optional[Any] = 7
snake_case: List[str] = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
snake_case: str = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
snake_case: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
snake_case: int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case: Tuple = 1
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case: Optional[Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
snake_case: Union[str, Any] = len(self.sp_model )
snake_case: str = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
snake_case: List[Any] = self.__dict__.copy()
snake_case: Union[str, Any] = None
snake_case: Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case: Union[str, Any] = {}
snake_case: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
snake_case: Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: int = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case: Dict = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = ''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ' ' ).strip()
return out_string
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case: List[str] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as fi:
snake_case: int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,) | 692 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
if "resnet-50" in model_name:
snake_case: str = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
snake_case: str = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
snake_case: int = DetrConfig(use_timm_backbone=_lowercase , backbone_config=_lowercase )
# set label attributes
snake_case: Dict = '''panoptic''' in model_name
if is_panoptic:
snake_case: str = 2_50
else:
snake_case: Any = 91
snake_case: Optional[int] = '''huggingface/label-files'''
snake_case: Dict = '''coco-detection-id2label.json'''
snake_case: Tuple = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='dataset' ) , 'r' ) )
snake_case: int = {int(_lowercase ): v for k, v in idalabel.items()}
snake_case: str = idalabel
snake_case: Optional[int] = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[Any] = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
f"""encoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
f"""decoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
) )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
) )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def lowerCAmelCase_ ( __A : List[Any] , __A : str , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = state_dict.pop(_lowercase )
snake_case: List[str] = val
def lowerCAmelCase_ ( __A : Any , __A : Dict=False ):
'''simple docstring'''
snake_case: Tuple = ''''''
if is_panoptic:
snake_case: List[Any] = '''detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case: int = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
snake_case: List[str] = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case: Any = in_proj_weight[:2_56, :]
snake_case: int = in_proj_bias[:2_56]
snake_case: Any = in_proj_weight[2_56:5_12, :]
snake_case: Union[str, Any] = in_proj_bias[2_56:5_12]
snake_case: int = in_proj_weight[-2_56:, :]
snake_case: int = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
snake_case: Optional[int] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
snake_case: str = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case: Optional[int] = in_proj_weight[:2_56, :]
snake_case: Tuple = in_proj_bias[:2_56]
snake_case: Union[str, Any] = in_proj_weight[2_56:5_12, :]
snake_case: int = in_proj_bias[2_56:5_12]
snake_case: List[str] = in_proj_weight[-2_56:, :]
snake_case: Optional[Any] = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
snake_case: Dict = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
snake_case: Any = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
snake_case: Union[str, Any] = in_proj_weight_cross_attn[:2_56, :]
snake_case: int = in_proj_bias_cross_attn[:2_56]
snake_case: Tuple = in_proj_weight_cross_attn[2_56:5_12, :]
snake_case: List[Any] = in_proj_bias_cross_attn[2_56:5_12]
snake_case: Union[str, Any] = in_proj_weight_cross_attn[-2_56:, :]
snake_case: Any = in_proj_bias_cross_attn[-2_56:]
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case: Dict = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __A : Dict , __A : List[Any]=None , __A : Dict=False ):
'''simple docstring'''
snake_case: str = get_detr_config(_lowercase )
# load original model from torch hub
snake_case: Optional[int] = {
'''detr-resnet-50''': '''detr_resnet50''',
'''detr-resnet-101''': '''detr_resnet101''',
}
logger.info(f"""Converting model {model_name}...""" )
snake_case: str = torch.hub.load('facebookresearch/detr' , model_name_to_original_name[model_name] , pretrained=_lowercase ).eval()
snake_case: int = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(_lowercase ):
if is_panoptic:
snake_case: List[str] = '''detr.''' + src
rename_key(_lowercase , _lowercase , _lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowercase , is_panoptic=_lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case: Optional[Any] = '''detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
snake_case: str = state_dict.pop(_lowercase )
snake_case: Any = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case: Optional[Any] = state_dict.pop(_lowercase )
snake_case: Optional[int] = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
snake_case: Optional[Any] = state_dict.pop(_lowercase )
snake_case: Union[str, Any] = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
snake_case: Optional[Any] = state_dict.pop(_lowercase )
snake_case: List[str] = val
# finally, create HuggingFace model and load state dict
snake_case: List[str] = DetrForSegmentation(_lowercase ) if is_panoptic else DetrForObjectDetection(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# verify our conversion on an image
snake_case: List[Any] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
snake_case: List[str] = DetrImageProcessor(format=_lowercase )
snake_case: List[str] = processor(images=prepare_img() , return_tensors='pt' )
snake_case: Optional[int] = encoding['''pixel_values''']
snake_case: List[str] = detr(_lowercase )
snake_case: List[Any] = model(_lowercase )
assert torch.allclose(outputs.logits , original_outputs['pred_logits'] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs['pred_boxes'] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['pred_masks'] , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
processor.save_pretrained(_lowercase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(f"""nielsr/{model_name}""" )
processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
__UpperCAmelCase = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 712 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
return getitem, k
def lowerCAmelCase_ ( __A : Any , __A : Optional[int] ):
'''simple docstring'''
return setitem, k, v
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
return delitem, k
def lowerCAmelCase_ ( __A : str , __A : int , *__A : Tuple ):
'''simple docstring'''
try:
return fun(__A , *__A ), None
except Exception as e:
return None, e
__UpperCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__UpperCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
snake_case: List[Any] = HashMap(initial_block_size=4 )
snake_case: List[Any] = {}
for _, (fun, *args) in enumerate(__A ):
snake_case , snake_case: Optional[int] = _run_operation(__A , __A , *__A )
snake_case , snake_case: str = _run_operation(__A , __A , *__A )
assert my_res == py_res
assert str(__A ) == str(__A )
assert set(__A ) == set(__A )
assert len(__A ) == len(__A )
assert set(my.items() ) == set(py.items() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
def is_public(__A : str ) -> bool:
return not name.startswith('_' )
snake_case: Dict = {name for name in dir({} ) if is_public(__A )}
snake_case: List[str] = {name for name in dir(HashMap() ) if is_public(__A )}
assert dict_public_names > hash_public_names | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwiftFormerForImageClassification",
"SwiftFormerModel",
"SwiftFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 713 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: List[str] = getattr(__A , __A )
if weight_type is not None:
snake_case: Optional[int] = getattr(__A , __A ).shape
else:
snake_case: Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Optional[int] = value
elif weight_type == "weight_g":
snake_case: List[str] = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: Optional[Any] = value
else:
snake_case: int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: List[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case: Dict = None
for name, value in fairseq_dict.items():
snake_case: Tuple = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
elif name.split('.' )[0] == "proj":
snake_case: List[Any] = fairseq_model.proj
snake_case: int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: int = True
if "*" in mapped_key:
snake_case: List[str] = name.split(__A )[0].split('.' )[-2]
snake_case: Dict = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Tuple = 'weight_g'
elif "weight_v" in name:
snake_case: int = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
snake_case: List[Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCAmelCase_ ( __A : List[str] , __A : List[Any] , __A : int , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: Tuple = name.split('.' )
snake_case: Any = int(items[0] )
snake_case: Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case , snake_case: List[Any] = emb.weight.shape
snake_case: Optional[int] = nn.Linear(__A , __A , bias=__A )
snake_case: Any = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
with open(__A , 'r' , encoding='utf-8' ) as f:
snake_case: List[Any] = f.readlines()
snake_case: Any = [line.split(' ' )[0] for line in lines]
snake_case: int = len(__A )
snake_case: Dict = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(__A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : int , __A : str , ):
'''simple docstring'''
snake_case: Union[str, Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: str = SpeechaTextaConfig.from_pretrained(
__A , vocab_size=__A , decoder_layers=__A , do_stable_layer_norm=__A )
snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
snake_case: List[Any] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case: Optional[Any] = WavaVecaModel(__A )
snake_case: Any = recursively_load_weights_wavaveca(model.encoder , __A )
snake_case: Union[str, Any] = SpeechaTextaForCausalLM(__A )
snake_case , snake_case: Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__A )
# set output linear layer
unexpected_keys.remove('embed_out' )
snake_case: str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
snake_case: int = SpeechEncoderDecoderModel(encoder=__A , decoder=__A )
snake_case: List[Any] = False
# add projection layer
snake_case: Union[str, Any] = nn.Parameter(projection_layer.weight )
snake_case: Union[str, Any] = nn.Parameter(projection_layer.bias )
snake_case: List[Any] = create_vocab_dict(__A )
with open(os.path.join(__A , 'vocab.json' ) , 'w' ) as fp:
json.dump(__A , __A )
snake_case: Union[str, Any] = SpeechaTextaTokenizer(os.path.join(__A , 'vocab.json' ) )
tokenizer.save_pretrained(__A )
snake_case: Tuple = hf_wavavec.config.to_dict()
snake_case: int = tokenizer.pad_token_id
snake_case: Dict = tokenizer.bos_token_id
snake_case: Optional[int] = tokenizer.eos_token_id
snake_case: Dict = 'speech_to_text_2'
snake_case: Optional[Any] = 'wav2vec2'
snake_case: Tuple = SpeechEncoderDecoderConfig.from_dict(__A )
hf_wavavec.save_pretrained(__A )
feature_extractor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
) | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : Any ): # noqa: E741
'''simple docstring'''
snake_case: Any = len(__A )
snake_case: List[Any] = 0
snake_case: Optional[Any] = [0] * n
snake_case: Union[str, Any] = [False] * n
snake_case: Union[str, Any] = [False] * n
def dfs(__A : Dict , __A : str , __A : Any , __A : List[str] ):
if parent == root:
out_edge_count += 1
snake_case: List[str] = True
snake_case: str = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
snake_case: Optional[int] = dfs(__A , __A , __A , __A )
snake_case: List[str] = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
snake_case: Any = True
# AP found via cycle
if at == low[to]:
snake_case: Dict = True
else:
snake_case: Any = min(low[at] , __A )
return out_edge_count
for i in range(__A ):
if not visited[i]:
snake_case: Optional[int] = 0
snake_case: Tuple = dfs(__A , __A , -1 , __A )
snake_case: Any = out_edge_count > 1
for x in range(len(__A ) ):
if is_art[x] is True:
print(__A )
# Adjacency list of graph
__UpperCAmelCase = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data) | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }') | 692 | 0 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
__UpperCAmelCase = logging.getLogger(__name__)
def lowerCAmelCase_ ( __A : Optional[int]=2 , __A : Union[str, Any]=3 , __A : Any=16 , __A : Any = 10 , __A : Union[str, Any] = 2 ):
'''simple docstring'''
def get_dataset(__A : Any ):
snake_case: Tuple = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(SCREAMING_SNAKE_CASE_ , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
snake_case: Union[str, Any] = get_dataset(SCREAMING_SNAKE_CASE_ )
snake_case: Any = get_dataset(SCREAMING_SNAKE_CASE_ )
snake_case: Dict = DataLoader(SCREAMING_SNAKE_CASE_ , shuffle=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , num_workers=4 )
snake_case: Dict = DataLoader(SCREAMING_SNAKE_CASE_ , shuffle=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , num_workers=4 )
return (train_dataloader, valid_dataloader)
def lowerCAmelCase_ ( __A : str , __A : Optional[Any] , __A : Optional[int] , __A : Any , __A : List[Any] , __A : Optional[Any]=None ):
'''simple docstring'''
snake_case: Optional[Any] = []
for epoch in range(SCREAMING_SNAKE_CASE_ ):
# Train quickly
model.train()
for batch in dataloader:
snake_case , snake_case: Dict = batch
snake_case: Tuple = model(SCREAMING_SNAKE_CASE_ )
snake_case: Dict = torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
accelerator.backward(SCREAMING_SNAKE_CASE_ )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
super().__init__()
snake_case: Optional[Any] = nn.Parameter(torch.randn(1 ) )
snake_case: Dict = nn.Parameter(torch.randn(1 ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return x * self.a + self.b
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
snake_case: Dict = DummyModel()
snake_case: Optional[Any] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case , snake_case: int = dummy_dataloaders()
snake_case: int = ProjectConfiguration(total_limit=1 , project_dir=__lowercase , automatic_checkpoint_naming=__lowercase )
# Train baseline
snake_case: List[str] = Accelerator(project_config=__lowercase )
snake_case , snake_case , snake_case , snake_case: Any = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def _UpperCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
snake_case: Optional[Any] = DummyModel()
snake_case: Optional[int] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case , snake_case: Optional[int] = dummy_dataloaders()
# Train baseline
snake_case: int = Accelerator()
snake_case , snake_case , snake_case , snake_case: Dict = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase )
# Save initial
snake_case: Union[str, Any] = os.path.join(__lowercase , 'initial' )
accelerator.save_state(__lowercase )
((snake_case) , (snake_case)): Optional[int] = model.a.item(), model.b.item()
snake_case: List[str] = optimizer.state_dict()
snake_case: int = train(3 , __lowercase , __lowercase , __lowercase , __lowercase )
((snake_case) , (snake_case)): Dict = model.a.item(), model.b.item()
snake_case: Optional[Any] = optimizer.state_dict()
# Train partially
set_seed(42 )
snake_case: int = DummyModel()
snake_case: Optional[Any] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case , snake_case: str = dummy_dataloaders()
snake_case: Optional[Any] = Accelerator()
snake_case , snake_case , snake_case , snake_case: Any = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase )
accelerator.load_state(__lowercase )
((snake_case) , (snake_case)): Dict = model.a.item(), model.b.item()
snake_case: Dict = optimizer.state_dict()
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
snake_case: Dict = train(2 , __lowercase , __lowercase , __lowercase , __lowercase )
# Save everything
snake_case: str = os.path.join(__lowercase , 'checkpoint' )
accelerator.save_state(__lowercase )
# Load everything back in and make sure all states work
accelerator.load_state(__lowercase )
test_rands += train(1 , __lowercase , __lowercase , __lowercase , __lowercase )
((snake_case) , (snake_case)): Dict = model.a.item(), model.b.item()
snake_case: Tuple = optimizer.state_dict()
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
def _UpperCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
snake_case: Optional[int] = DummyModel()
snake_case: Optional[Any] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case , snake_case: Optional[Any] = dummy_dataloaders()
snake_case: str = ProjectConfiguration(automatic_checkpoint_naming=__lowercase )
# Train baseline
snake_case: List[str] = Accelerator(project_dir=__lowercase , project_config=__lowercase )
snake_case , snake_case , snake_case , snake_case: Union[str, Any] = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase )
# Save initial
accelerator.save_state()
((snake_case) , (snake_case)): Tuple = model.a.item(), model.b.item()
snake_case: Tuple = optimizer.state_dict()
snake_case: Optional[Any] = train(3 , __lowercase , __lowercase , __lowercase , __lowercase )
((snake_case) , (snake_case)): Union[str, Any] = model.a.item(), model.b.item()
snake_case: Dict = optimizer.state_dict()
# Train partially
set_seed(42 )
snake_case: List[str] = DummyModel()
snake_case: str = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case , snake_case: int = dummy_dataloaders()
snake_case: str = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=__lowercase )
snake_case: Any = Accelerator(project_dir=__lowercase , project_config=__lowercase )
snake_case , snake_case , snake_case , snake_case: str = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase )
accelerator.load_state(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_0' ) )
((snake_case) , (snake_case)): str = model.a.item(), model.b.item()
snake_case: Dict = optimizer.state_dict()
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
snake_case: Union[str, Any] = train(2 , __lowercase , __lowercase , __lowercase , __lowercase )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_1' ) )
test_rands += train(1 , __lowercase , __lowercase , __lowercase , __lowercase )
((snake_case) , (snake_case)): Tuple = model.a.item(), model.b.item()
snake_case: List[str] = optimizer.state_dict()
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = torch.tensor([1, 2, 3] )
snake_case: Optional[Any] = torch.tensor([2, 3, 4] )
snake_case: Any = DummyModel()
snake_case: Union[str, Any] = torch.optim.Adam(net.parameters() )
snake_case: Union[str, Any] = Accelerator()
with self.assertRaises(__lowercase ) as ve:
accelerator.register_for_checkpointing(__lowercase , __lowercase , __lowercase , __lowercase )
snake_case: int = str(ve.exception )
self.assertTrue('Item at index 0' in message )
self.assertTrue('Item at index 1' in message )
self.assertFalse('Item at index 2' in message )
self.assertFalse('Item at index 3' in message )
def _UpperCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
snake_case: Optional[int] = DummyModel()
snake_case: Dict = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
snake_case: List[Any] = torch.optim.lr_scheduler.StepLR(__lowercase , step_size=1 , gamma=0.99 )
snake_case , snake_case: int = dummy_dataloaders()
snake_case: Optional[Any] = ProjectConfiguration(automatic_checkpoint_naming=__lowercase )
# Train baseline
snake_case: Optional[Any] = Accelerator(project_dir=__lowercase , project_config=__lowercase )
snake_case , snake_case , snake_case , snake_case , snake_case: Union[str, Any] = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
# Save initial
accelerator.save_state()
snake_case: Union[str, Any] = scheduler.state_dict()
train(3 , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
self.assertNotEqual(__lowercase , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_0' ) )
self.assertEqual(__lowercase , scheduler.state_dict() )
def _UpperCamelCase ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
snake_case: Optional[int] = DummyModel()
snake_case: List[Any] = ProjectConfiguration(automatic_checkpoint_naming=__lowercase , total_limit=2 )
# Train baseline
snake_case: List[Any] = Accelerator(project_dir=__lowercase , project_config=__lowercase )
snake_case: Union[str, Any] = accelerator.prepare(__lowercase )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_9' ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowercase , 'checkpoints' , 'checkpoint_10' ) ) )
@require_cuda
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(__lowercase , env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = "/tmp/accelerate/state_checkpointing"
__UpperCAmelCase = DummyModel()
__UpperCAmelCase = torch.optim.Adam(params=model.parameters(), lr=1E-3)
__UpperCAmelCase = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
__UpperCAmelCase , __UpperCAmelCase = dummy_dataloaders()
__UpperCAmelCase = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
__UpperCAmelCase = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
__UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
__UpperCAmelCase = group["params"][0].device
break
assert param_device.type == accelerator.device.type
__UpperCAmelCase = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
for group in optimizer.param_groups:
__UpperCAmelCase = group["params"][0].device
break
assert (
param_device.type == torch.device("cpu").type
), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
for group in optimizer.param_groups:
__UpperCAmelCase = group["params"][0].device
break
assert (
param_device.type == accelerator.device.type
), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone() | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__UpperCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
snake_case: List[Any] = k.replace(__A , __A )
return k
def lowerCAmelCase_ ( __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[int] = BigBirdPegasusConfig(**__A )
snake_case: List[Any] = BigBirdPegasusForConditionalGeneration(__A )
snake_case: Any = torch_model.state_dict()
snake_case: Any = {}
# separating decoder weights
snake_case: Optional[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
snake_case: Any = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
snake_case: List[str] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Any = DECODER_PATTERNS
snake_case: int = rename_state_dict_key(__A , __A )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: Optional[Any] = v.T
snake_case: Any = torch.from_numpy(__A )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
snake_case: List[Any] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Union[str, Any] = REMAINING_PATTERNS
snake_case: str = rename_state_dict_key(__A , __A )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: int = v.T
snake_case: Any = torch.from_numpy(__A )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
snake_case: str = mapping['model.embed_positions.weight']
snake_case: Any = mapping.pop('model.embed_positions.weight' )
snake_case , snake_case: Union[str, Any] = torch_model.load_state_dict(__A , strict=__A )
snake_case: Optional[int] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Tuple = tf.train.list_variables(__A )
snake_case: str = {}
snake_case: List[str] = ['global_step']
for name, shape in tqdm(__A , desc='converting tf checkpoint to dict' ):
snake_case: str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case: Any = tf.train.load_variable(__A , __A )
snake_case: Optional[int] = array
return tf_weights
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict ):
'''simple docstring'''
snake_case: int = get_tf_weights_as_numpy(__A )
snake_case: int = convert_bigbird_pegasus(__A , __A )
torch_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 692 | 0 |
'''simple docstring'''
from math import pow
def lowerCAmelCase_ ( __A : List[str] , __A : int , __A : List[str] , __A : Tuple , __A : str , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
snake_case: str = int(pow(_lowerCAmelCase , _lowerCAmelCase ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
snake_case: int = backtrack(
_lowerCAmelCase , _lowerCAmelCase , current_number + 1 , _lowerCAmelCase , _lowerCAmelCase )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
snake_case: Optional[int] = backtrack(
_lowerCAmelCase , _lowerCAmelCase , current_number + 1 , _lowerCAmelCase , _lowerCAmelCase )
return current_sum, solutions_count
def lowerCAmelCase_ ( __A : Tuple , __A : Union[str, Any] ):
'''simple docstring'''
if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
raise ValueError(
'Invalid input\n'
'needed_sum must be between 1 and 1000, power between 2 and 10.' )
return backtrack(_lowerCAmelCase , _lowerCAmelCase , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod() | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case: int = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case: Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__UpperCAmelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 692 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=18 , SCREAMING_SNAKE_CASE__=30 , SCREAMING_SNAKE_CASE__=4_00 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , SCREAMING_SNAKE_CASE__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , SCREAMING_SNAKE_CASE__=True , ):
'''simple docstring'''
snake_case: str = size if size is not None else {'height': 2_24, 'width': 2_24}
snake_case: Dict = crop_size if crop_size is not None else {'height': 18, 'width': 18}
snake_case: Tuple = parent
snake_case: List[str] = batch_size
snake_case: Tuple = num_channels
snake_case: List[Any] = image_size
snake_case: List[Any] = min_resolution
snake_case: List[Any] = max_resolution
snake_case: int = do_resize
snake_case: List[Any] = size
snake_case: Optional[Any] = do_center_crop
snake_case: int = crop_size
snake_case: Optional[int] = do_normalize
snake_case: List[str] = image_mean
snake_case: Optional[Any] = image_std
snake_case: Tuple = do_convert_rgb
def _UpperCamelCase ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
snake_case: str = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
snake_case: Tuple = []
for i in range(self.batch_size ):
snake_case , snake_case: Dict = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
snake_case: Optional[int] = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
snake_case: Union[str, Any] = [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( UpperCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE__ )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'size' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_convert_rgb' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 2_24, 'width': 2_24} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
snake_case: Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case: Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
snake_case: Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
snake_case: List[Any] = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case: str = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
# Test not batched input
snake_case: Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
snake_case: Optional[int] = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case: str = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
snake_case: List[str] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
snake_case: List[Any] = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( UpperCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 3
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'size' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , 'do_convert_rgb' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case: int = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
snake_case: Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
snake_case: str = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , ) | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = tempfile.mkdtemp()
snake_case: Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
snake_case: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
snake_case: Optional[int] = {
'do_resize': True,
'size': {'height': 2_24, 'width': 2_24},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'do_convert_rgb': True,
}
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
snake_case: Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer()
snake_case: Union[str, Any] = self.get_image_processor()
snake_case: List[str] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
snake_case: List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
snake_case: Any = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
snake_case: Union[str, Any] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_image_processor()
snake_case: Tuple = self.get_tokenizer()
snake_case: Optional[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.prepare_image_inputs()
snake_case: List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
snake_case: Dict = processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_image_processor()
snake_case: Optional[int] = self.get_tokenizer()
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Union[str, Any] = processor(text=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Tuple = self.prepare_image_inputs()
snake_case: Any = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.get_image_processor()
snake_case: str = self.get_tokenizer()
snake_case: Union[str, Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case: int = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = 'Alexandra，T-shirt的价格是15便士。'
snake_case: List[Any] = self.prepare_image_inputs()
snake_case: Dict = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure) | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "swinv2"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: int = image_size
snake_case: Union[str, Any] = patch_size
snake_case: List[str] = num_channels
snake_case: Tuple = embed_dim
snake_case: str = depths
snake_case: Any = len(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = num_heads
snake_case: Optional[int] = window_size
snake_case: Any = mlp_ratio
snake_case: Optional[int] = qkv_bias
snake_case: Union[str, Any] = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: Dict = drop_path_rate
snake_case: List[str] = hidden_act
snake_case: int = use_absolute_embeddings
snake_case: Any = layer_norm_eps
snake_case: Dict = initializer_range
snake_case: List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Tuple = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
snake_case: Union[str, Any] = (0, 0, 0, 0) | 692 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "CLIPImageProcessor"
__UpperCamelCase = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowerCAmelCase_ , )
snake_case: Dict = kwargs.pop('feature_extractor' )
snake_case: Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
def __call__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
snake_case: str = self.tokenizer(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
if images is not None:
snake_case: Tuple = self.image_processor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
if text is not None and images is not None:
snake_case: Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase_ ) , tensor_type=lowerCAmelCase_ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.tokenizer.model_input_names
snake_case: str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 719 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _UpperCamelCase ( *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__UpperCAmelCase = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = pipeline(
'document-question-answering' , model=__a , tokenizer=__a , image_processor=__a )
snake_case: List[Any] = INVOICE_URL
snake_case: str = list(zip(*apply_tesseract(load_image(__a ) , __a , '' ) ) )
snake_case: Optional[Any] = """What is the placebo?"""
snake_case: Tuple = [
{
"""image""": load_image(__a ),
"""question""": question,
},
{
"""image""": image,
"""question""": question,
},
{
"""image""": image,
"""question""": question,
"""word_boxes""": word_boxes,
},
]
return dqa_pipeline, examples
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = dqa_pipeline(__a , top_k=2 )
self.assertEqual(
__a , [
[
{'score': ANY(__a ), 'answer': ANY(__a ), 'start': ANY(__a ), 'end': ANY(__a )},
{'score': ANY(__a ), 'answer': ANY(__a ), 'start': ANY(__a ), 'end': ANY(__a )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
snake_case: Optional[int] = INVOICE_URL
snake_case: Optional[int] = """How many cats are there?"""
snake_case: Any = [
{"""score""": 0.00_01, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
{"""score""": 0.00_01, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
]
snake_case: Optional[int] = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(nested_simplify(__a , decimals=4 ) , __a )
snake_case: Union[str, Any] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(__a , decimals=4 ) , __a )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
snake_case: Union[str, Any] = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
snake_case: Tuple = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(__a , [] )
# We can optionnally pass directly the words and bounding boxes
snake_case: List[str] = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
snake_case: str = []
snake_case: int = []
snake_case: Dict = dqa_pipeline(image=__a , question=__a , words=__a , boxes=__a , top_k=2 )
self.assertEqual(__a , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
snake_case: Optional[Any] = INVOICE_URL
snake_case: Union[str, Any] = """What is the invoice number?"""
snake_case: Dict = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
snake_case: Optional[Any] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
snake_case: Tuple = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
[
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
snake_case: Union[str, Any] = INVOICE_URL
snake_case: Any = """What is the invoice number?"""
snake_case: Any = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
snake_case: int = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
snake_case: Optional[Any] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
[
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=__a )
snake_case: str = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=__a , revision='3dc6de3' , )
snake_case: Tuple = INVOICE_URL
snake_case: int = """What is the invoice number?"""
snake_case: List[str] = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
snake_case: Optional[int] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
snake_case: Union[str, Any] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
[
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
snake_case: str = list(zip(*apply_tesseract(load_image(__a ) , __a , '' ) ) )
# This model should also work if `image` is set to None
snake_case: Optional[Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=__a )
snake_case: int = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=__a , revision='3dc6de3' , max_seq_len=50 , )
snake_case: int = INVOICE_URL
snake_case: int = """What is the invoice number?"""
snake_case: List[Any] = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
snake_case: str = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
[
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
snake_case: List[str] = list(zip(*apply_tesseract(load_image(__a ) , __a , '' ) ) )
# This model should also work if `image` is set to None
snake_case: Dict = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(__a , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
@slow
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
snake_case: str = INVOICE_URL
snake_case: Any = """What is the invoice number?"""
snake_case: Tuple = dqa_pipeline(image=__a , question=__a , top_k=2 )
self.assertEqual(nested_simplify(__a , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass | 720 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = question_encoder
snake_case: Union[str, Any] = generator
snake_case: Optional[int] = self.question_encoder
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case: int = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
snake_case: str = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
snake_case: Dict = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.question_encoder
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.generator
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "longest" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
snake_case: Optional[Any] = self.current_tokenizer.model_max_length
snake_case: int = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case: Any = self.current_tokenizer.model_max_length
snake_case: List[str] = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: Dict = labels['input_ids']
return model_inputs | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int , __A : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1)) | 721 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'mock-s3-bucket'
snake_case: int = f"""s3://{mock_bucket}"""
snake_case: Any = extract_path_from_uri(__A )
assert dataset_path.startswith('s3://' ) is False
snake_case: Union[str, Any] = './local/path'
snake_case: Union[str, Any] = extract_path_from_uri(__A )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = is_remote_filesystem(__A )
assert is_remote is True
snake_case: int = fsspec.filesystem('file' )
snake_case: int = is_remote_filesystem(__A )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , __A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : str , __A : Optional[Any] , __A : List[str] , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
snake_case: Optional[int] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case: str = f"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=__A )
assert isinstance(__A , __A )
snake_case: Any = os.path.basename(__A )
snake_case: int = expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(__A , 'r' , encoding='utf-8' ) as f, open(__A , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def lowerCAmelCase_ ( __A : Any , __A : int , __A : int ):
'''simple docstring'''
snake_case: List[str] = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
snake_case: str = compressed_file_paths[protocol]
snake_case: Dict = 'dataset.jsonl'
snake_case: Optional[Any] = f"""{protocol}://{member_file_path}::{compressed_file_path}"""
snake_case , *snake_case: List[Any] = fsspec.get_fs_token_paths(__A )
assert fs.isfile(__A )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : List[Any] ):
'''simple docstring'''
snake_case: Tuple = hf_api.dataset_info(__A , token=__A )
snake_case: List[str] = HfFileSystem(repo_info=__A , token=__A )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(__A ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__A , __A , clobber=__A )
with pytest.warns(__A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__A ) == 1
assert (
str(warning_info[0].message )
== f"""A filesystem protocol was already set for {protocol} and will be overwritten."""
) | 692 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=[32, 64, 1_28] , SCREAMING_SNAKE_CASE__=[1, 2, 1] , SCREAMING_SNAKE_CASE__=[2, 2, 4] , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=2.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=["stage1", "stage2"] , SCREAMING_SNAKE_CASE__=[1, 2] , ):
'''simple docstring'''
snake_case: Tuple = parent
snake_case: str = batch_size
snake_case: str = image_size
snake_case: Union[str, Any] = patch_size
snake_case: Dict = num_channels
snake_case: List[Any] = embed_dim
snake_case: List[Any] = hidden_sizes
snake_case: Dict = depths
snake_case: Union[str, Any] = num_heads
snake_case: Dict = window_size
snake_case: List[Any] = mlp_ratio
snake_case: str = qkv_bias
snake_case: Dict = hidden_dropout_prob
snake_case: List[Any] = attention_probs_dropout_prob
snake_case: Optional[int] = drop_path_rate
snake_case: str = hidden_act
snake_case: List[Any] = use_absolute_embeddings
snake_case: List[Any] = patch_norm
snake_case: Union[str, Any] = layer_norm_eps
snake_case: Tuple = initializer_range
snake_case: Dict = is_training
snake_case: List[Any] = scope
snake_case: Any = use_labels
snake_case: Tuple = type_sequence_label_size
snake_case: Any = encoder_stride
snake_case: Union[str, Any] = out_features
snake_case: List[str] = out_indices
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case: Any = None
if self.use_labels:
snake_case: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case: List[Any] = self.get_config()
return config, pixel_values, labels
def _UpperCamelCase ( self ):
'''simple docstring'''
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = FocalNetModel(config=A__ )
model.to(A__ )
model.eval()
snake_case: int = model(A__ )
snake_case: Any = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case: int = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[Any] = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
snake_case: List[str] = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
snake_case: Optional[Any] = None
snake_case: int = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
snake_case: Any = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = FocalNetForMaskedImageModeling(config=A__ )
model.to(A__ )
model.eval()
snake_case: Tuple = model(A__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case: Union[str, Any] = 1
snake_case: Dict = FocalNetForMaskedImageModeling(A__ )
model.to(A__ )
model.eval()
snake_case: int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case: Optional[Any] = model(A__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.type_sequence_label_size
snake_case: Union[str, Any] = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
snake_case: List[str] = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case: Dict = 1
snake_case: List[str] = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
snake_case: Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case: Optional[int] = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case: Tuple = config_and_inputs
snake_case: Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __a , __a , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = FocalNetModelTester(self )
snake_case: Dict = ConfigTester(self , config_class=A__ , embed_dim=37 , has_text_modality=A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCamelCase ( self ):
'''simple docstring'''
return
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@unittest.skip(reason='FocalNet does not use inputs_embeds' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='FocalNet does not use feedforward chunking' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case: Any = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case: str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , nn.Linear ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case: Union[str, Any] = model_class(A__ )
snake_case: Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case: Tuple = [*signature.parameters.keys()]
snake_case: str = ['pixel_values']
self.assertListEqual(arg_names[:1] , A__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
snake_case: List[str] = model(**self._prepare_for_class(A__ , A__ ) )
snake_case: Dict = outputs.hidden_states
snake_case: str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(A__ ) , A__ )
# FocalNet has a different seq_length
snake_case: Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case: str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case: List[Any] = outputs.reshaped_hidden_states
self.assertEqual(len(A__ ) , A__ )
snake_case , snake_case , snake_case , snake_case: List[Any] = reshaped_hidden_states[0].shape
snake_case: int = (
reshaped_hidden_states[0].view(A__ , A__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: str = self.model_tester.prepare_config_and_inputs_for_common()
snake_case: Dict = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
snake_case: Optional[Any] = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case: Any = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case: Any = 3
snake_case: List[str] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case: Union[str, Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case: int = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case: Tuple = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
snake_case: List[str] = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case: Optional[Any] = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case: Dict = FocalNetModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Dict = self.model_tester.prepare_config_and_inputs_for_common()
snake_case: List[Any] = _config_zero_init(A__ )
for model_class in self.all_model_classes:
snake_case: int = model_class(config=A__ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny' ) if is_vision_available() else None
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny' ).to(A__ )
snake_case: List[str] = self.default_image_processor
snake_case: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case: Tuple = image_processor(images=A__ , return_tensors='pt' ).to(A__ )
# forward pass
with torch.no_grad():
snake_case: str = model(**A__ )
# verify the logits
snake_case: List[Any] = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case: List[Any] = torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 2_81 )
@require_torch
class SCREAMING_SNAKE_CASE ( __a , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (FocalNetBackbone,) if is_torch_available() else ()
__UpperCamelCase = FocalNetConfig
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = FocalNetModelTester(self ) | 700 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__UpperCamelCase = field(
default=1024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the training data."} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A csv or a json file containing the test data."} )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
snake_case: str = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case: Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case: Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case: str = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
snake_case: Tuple = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case: Optional[int] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case: Tuple = data_args.train_file.split('.' )[-1]
snake_case: Union[str, Any] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case: Union[str, Any] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
snake_case: List[Any] = load_dataset('csv' , data_files=__A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case: Optional[Any] = load_dataset('json' , data_files=__A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case: Tuple = raw_datasets['train'].features['label'].names
snake_case: List[str] = len(__A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case: List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
snake_case: Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case: int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case: Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case: Optional[Any] = {'Refused': 0, 'Entailed': 1}
snake_case: List[Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case: List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__A : Any ):
# Tokenize the texts
def _convert_table_text_to_pandas(__A : Dict ):
snake_case: str = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
snake_case: List[str] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case: str = examples['statement']
snake_case: int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
snake_case: List[Any] = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A )
snake_case: List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
snake_case: int = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case: List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case: Tuple = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case: Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case: Optional[int] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
snake_case: str = raw_datasets['test']
if data_args.max_predict_samples is not None:
snake_case: List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
snake_case: int = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
snake_case: List[str] = np.argmax(__A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case: str = default_data_collator
elif training_args.fpaa:
snake_case: List[str] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
snake_case: List[Any] = None
# Initialize our Trainer
snake_case: List[str] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
snake_case: str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: Optional[Any] = last_checkpoint
snake_case: Union[str, Any] = trainer.train(resume_from_checkpoint=__A )
snake_case: List[Any] = train_result.metrics
snake_case: List[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
snake_case: Optional[Any] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , __A )
trainer.save_metrics('train' , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case: Dict = trainer.evaluate(eval_dataset=__A )
snake_case: Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
snake_case: Dict = min(__A , len(__A ) )
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case: Optional[int] = predict_dataset.remove_columns('label' )
snake_case: str = trainer.predict(__A , metric_key_prefix='predict' ).predictions
snake_case: Any = np.argmax(__A , axis=1 )
snake_case: int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(__A , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(__A ):
snake_case: int = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
snake_case: Optional[int] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 692 | 0 |
'''simple docstring'''
import itertools
import math
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Optional[Any] = 2
while True:
if is_prime(_SCREAMING_SNAKE_CASE ):
yield num
num += 1
def lowerCAmelCase_ ( __A : Dict = 1_00_01 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , _SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
print(F'{solution() = }') | 701 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : float = 0.1 ):
'''simple docstring'''
snake_case: Optional[int] = 3
snake_case: int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__A )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "AutoImageProcessor"
__UpperCamelCase = "AutoTokenizer"
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(__UpperCamelCase , __UpperCamelCase )
snake_case: Optional[int] = self.image_processor
def __call__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
snake_case: Union[str, Any] = self.tokenizer(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if images is not None:
snake_case: Union[str, Any] = self.image_processor(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if text is not None and images is not None:
snake_case: Optional[int] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCamelCase ) , tensor_type=__UpperCamelCase )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ["input_ids", "attention_mask", "pixel_values"]
| 702 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] ) | 692 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__UpperCAmelCase = 250_004
__UpperCAmelCase = 250_020
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( a__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = MBartTokenizer
__UpperCamelCase = MBartTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
snake_case: str = MBartTokenizer(lowercase__ , keep_accents=lowercase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = MBartTokenizer(lowercase__ , keep_accents=lowercase__ )
snake_case: Dict = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowercase__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
snake_case: List[str] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowercase__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
snake_case: str = tokenizer.convert_tokens_to_ids(lowercase__ )
self.assertListEqual(
lowercase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case: Tuple = tokenizer.convert_ids_to_tokens(lowercase__ )
self.assertListEqual(
lowercase__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def _UpperCamelCase ( self ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case: Any = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = self.rust_tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
snake_case: List[Any] = self.tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
snake_case: Dict = tempfile.mkdtemp()
snake_case: int = tokenizer_r.save_pretrained(lowercase__ )
snake_case: Dict = tokenizer_p.save_pretrained(lowercase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
snake_case: Any = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(lowercase__ , lowercase__ )
# Checks everything loads correctly in the same way
snake_case: Tuple = tokenizer_r.from_pretrained(lowercase__ )
snake_case: List[str] = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase__ )
# Save tokenizer rust, legacy_format=True
snake_case: Tuple = tempfile.mkdtemp()
snake_case: List[str] = tokenizer_r.save_pretrained(lowercase__ , legacy_format=lowercase__ )
snake_case: Any = tokenizer_p.save_pretrained(lowercase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase__ , lowercase__ )
# Checks everything loads correctly in the same way
snake_case: Union[str, Any] = tokenizer_r.from_pretrained(lowercase__ )
snake_case: Dict = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
shutil.rmtree(lowercase__ )
# Save tokenizer rust, legacy_format=False
snake_case: List[str] = tempfile.mkdtemp()
snake_case: List[Any] = tokenizer_r.save_pretrained(lowercase__ , legacy_format=lowercase__ )
snake_case: str = tokenizer_p.save_pretrained(lowercase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case: str = tokenizer_r.from_pretrained(lowercase__ )
snake_case: Tuple = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
shutil.rmtree(lowercase__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = """facebook/mbart-large-en-ro"""
__UpperCamelCase = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
__UpperCamelCase = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
__UpperCamelCase = [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]
@classmethod
def _UpperCamelCase ( cls ):
'''simple docstring'''
snake_case: MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
snake_case: int = 1
return cls
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 25_00_20 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertIn(lowercase__ , self.tokenizer.all_special_ids )
snake_case: Dict = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
snake_case: str = self.tokenizer.decode(lowercase__ , skip_special_tokens=lowercase__ )
snake_case: Tuple = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase__ )
self.assertEqual(lowercase__ , lowercase__ )
self.assertNotIn(self.tokenizer.eos_token , lowercase__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , lowercase__ )
snake_case: str = 10
snake_case: Dict = self.tokenizer(lowercase__ , max_length=lowercase__ , truncation=lowercase__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowercase__ )
self.assertEqual(len(lowercase__ ) , lowercase__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_00_26, 25_00_01] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = tempfile.mkdtemp()
snake_case: Dict = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase__ )
snake_case: Tuple = MBartTokenizer.from_pretrained(lowercase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase__ )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowercase__ , return_tensors='pt' )
snake_case: Union[str, Any] = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase__ , truncation=lowercase__ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
snake_case: Optional[int] = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowercase__ , lowercase__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
snake_case: List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer(self.src_text , padding=lowercase__ , truncation=lowercase__ , max_length=3 , return_tensors='pt' )
snake_case: Tuple = self.tokenizer(
text_target=self.tgt_text , padding=lowercase__ , truncation=lowercase__ , max_length=10 , return_tensors='pt' )
snake_case: Any = targets['''input_ids''']
snake_case: Dict = shift_tokens_right(lowercase__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(lowercase__ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 30_34, 2, 25_00_04]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_00_01,
} , ) | 703 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "layer_norm" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = only_cross_attention
snake_case: Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
snake_case: Tuple = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case: List[str] = AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case: str = AdaLayerNormZero(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=SCREAMING_SNAKE_CASE__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case: Tuple = (
AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , upcast_attention=SCREAMING_SNAKE_CASE__ , ) # is self-attn if encoder_hidden_states is none
else:
snake_case: int = None
snake_case: Tuple = None
# 3. Feed-forward
snake_case: Union[str, Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = FeedForward(SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , final_dropout=SCREAMING_SNAKE_CASE__ )
# let chunk size default to None
snake_case: Any = None
snake_case: Any = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = chunk_size
snake_case: str = dim
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
if self.use_ada_layer_norm:
snake_case: Optional[int] = self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case , snake_case , snake_case , snake_case , snake_case: int = self.norma(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=hidden_states.dtype )
else:
snake_case: List[str] = self.norma(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case: List[str] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if self.use_ada_layer_norm_zero:
snake_case: Tuple = gate_msa.unsqueeze(1 ) * attn_output
snake_case: List[str] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case: Dict = (
self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: List[str] = attn_output + hidden_states
# 3. Feed-forward
snake_case: str = self.norma(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: str = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case: List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case: Optional[Any] = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE__ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case: int = self.ff(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case: Tuple = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 4 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: int = int(dim * mult )
snake_case: Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case: int = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if activation_fn == "gelu-approximate":
snake_case: Optional[Any] = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , approximate='tanh' )
elif activation_fn == "geglu":
snake_case: List[Any] = GEGLU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif activation_fn == "geglu-approximate":
snake_case: Optional[int] = ApproximateGELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE__ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for module in self.net:
snake_case: Optional[int] = module(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "none" ):
'''simple docstring'''
super().__init__()
snake_case: Optional[int] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = approximate
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.proj(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = self.gelu(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = nn.Linear(SCREAMING_SNAKE_CASE__ , dim_out * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case: int = self.proj(SCREAMING_SNAKE_CASE__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = self.proj(SCREAMING_SNAKE_CASE__ )
return x * torch.sigmoid(1.7_02 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Optional[Any] = nn.Embedding(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = nn.SiLU()
snake_case: Union[str, Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , embedding_dim * 2 )
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case: Dict = torch.chunk(SCREAMING_SNAKE_CASE__ , 2 )
snake_case: str = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.SiLU()
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , 6 * embedding_dim , bias=SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ , eps=1E-6 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case: str = emb.chunk(6 , dim=1 )
snake_case: Dict = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 1E-5 ):
'''simple docstring'''
super().__init__()
snake_case: str = num_groups
snake_case: str = eps
if act_fn is None:
snake_case: Dict = None
else:
snake_case: List[str] = get_activation(SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , out_dim * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.act:
snake_case: Optional[Any] = self.act(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.linear(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = emb[:, :, None, None]
snake_case , snake_case: List[Any] = emb.chunk(2 , dim=1 )
snake_case: Any = F.group_norm(SCREAMING_SNAKE_CASE__ , self.num_groups , eps=self.eps )
snake_case: Optional[int] = x * (1 + scale) + shift
return x | 692 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def lowerCAmelCase_ ( __A : Optional[Any] , __A : float = 0.0 , __A : float = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = RoCBertTokenizer
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = filter_non_english
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: Any = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
snake_case: List[Any] = {}
snake_case: List[str] = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = i
snake_case: Union[str, Any] = i
snake_case: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
snake_case: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: Dict = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
snake_case: Union[str, Any] = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: str = i
snake_case: Optional[int] = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
snake_case: int = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def _UpperCamelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
snake_case: List[str] = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , return_token_type_ids=SCREAMING_SNAKE_CASE__ , return_offsets_mapping=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , )
snake_case: Optional[int] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__ , 'do_lower_case' ) else False
snake_case: int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = ['的', '人', '有']
snake_case: Any = ''.join(SCREAMING_SNAKE_CASE__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = True
snake_case: List[Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = False
snake_case: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: int = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that only the first Chinese character is not preceded by "##".
snake_case: Union[str, Any] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: int = tokenizer.encode('你好' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Any = tokenizer.encode('你是谁' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Dict = '你好，你是谁'
snake_case: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.encode_plus(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
from math import sqrt
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: List[Any] = 0
for i in range(1 , int(sqrt(a_ ) + 1 ) ):
if n % i == 0 and i != sqrt(a_ ):
total += i + n // i
elif i == sqrt(a_ ):
total += i
return total - n
def lowerCAmelCase_ ( __A : int = 1_00_00 ):
'''simple docstring'''
snake_case: int = sum(
i
for i in range(1 , a_ )
if sum_of_divisors(sum_of_divisors(a_ ) ) == i and sum_of_divisors(a_ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip()))) | 705 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCAmelCase = 6378137.0
__UpperCAmelCase = 6356752.314245
__UpperCAmelCase = 6_378_137
def lowerCAmelCase_ ( __A : float , __A : float , __A : float , __A : float ):
'''simple docstring'''
snake_case: Optional[Any] = (AXIS_A - AXIS_B) / AXIS_A
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: Tuple = radians(__A )
snake_case: Tuple = radians(__A )
# Equation
snake_case: List[Any] = sin((phi_a - phi_a) / 2 )
snake_case: Dict = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
snake_case: Union[str, Any] = sqrt(sin_sq_phi + (cos(__A ) * cos(__A ) * sin_sq_lambda) )
return 2 * RADIUS * asin(__A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = graph
# mapping node to its parent in resulting breadth first tree
snake_case: Dict = {}
snake_case: int = source_vertex
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = {self.source_vertex}
snake_case: int = None
snake_case: List[Any] = [self.source_vertex] # first in first out queue
while queue:
snake_case: Tuple = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = vertex
queue.append(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
snake_case: Dict = self.parent.get(SCREAMING_SNAKE_CASE__ )
if target_vertex_parent is None:
snake_case: List[Any] = (
F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(SCREAMING_SNAKE_CASE__ )
return self.shortest_path(SCREAMING_SNAKE_CASE__ ) + F"""->{target_vertex}"""
if __name__ == "__main__":
__UpperCAmelCase = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo")) | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 692 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__UpperCAmelCase = datasets.utils.logging.get_logger(__name__)
__UpperCAmelCase = ["names", "prefix"]
__UpperCAmelCase = ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__UpperCAmelCase = ["encoding_errors", "on_bad_lines"]
__UpperCAmelCase = ["date_format"]
@dataclass
class SCREAMING_SNAKE_CASE ( datasets.BuilderConfig ):
'''simple docstring'''
__UpperCamelCase = ","
__UpperCamelCase = None
__UpperCamelCase = "infer"
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = None
__UpperCamelCase = "."
__UpperCamelCase = None
__UpperCamelCase = '"'
__UpperCamelCase = 0
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = 0
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = None
__UpperCamelCase = 1_0000
__UpperCamelCase = None
__UpperCamelCase = "strict"
__UpperCamelCase = "error"
__UpperCamelCase = None
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.delimiter is not None:
snake_case: str = self.delimiter
if self.column_names is not None:
snake_case: List[str] = self.column_names
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = {
"sep": self.sep,
"header": self.header,
"names": self.names,
"index_col": self.index_col,
"usecols": self.usecols,
"prefix": self.prefix,
"mangle_dupe_cols": self.mangle_dupe_cols,
"engine": self.engine,
"converters": self.converters,
"true_values": self.true_values,
"false_values": self.false_values,
"skipinitialspace": self.skipinitialspace,
"skiprows": self.skiprows,
"nrows": self.nrows,
"na_values": self.na_values,
"keep_default_na": self.keep_default_na,
"na_filter": self.na_filter,
"verbose": self.verbose,
"skip_blank_lines": self.skip_blank_lines,
"thousands": self.thousands,
"decimal": self.decimal,
"lineterminator": self.lineterminator,
"quotechar": self.quotechar,
"quoting": self.quoting,
"escapechar": self.escapechar,
"comment": self.comment,
"encoding": self.encoding,
"dialect": self.dialect,
"error_bad_lines": self.error_bad_lines,
"warn_bad_lines": self.warn_bad_lines,
"skipfooter": self.skipfooter,
"doublequote": self.doublequote,
"memory_map": self.memory_map,
"float_precision": self.float_precision,
"chunksize": self.chunksize,
"encoding_errors": self.encoding_errors,
"on_bad_lines": self.on_bad_lines,
"date_format": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , __a ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class SCREAMING_SNAKE_CASE ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__UpperCamelCase = CsvConfig
def _UpperCamelCase ( self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
snake_case: Dict = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__a , (str, list, tuple) ):
snake_case: Optional[Any] = data_files
if isinstance(__a , __a ):
snake_case: Dict = [files]
snake_case: List[Any] = [dl_manager.iter_files(__a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
snake_case: Tuple = []
for split_name, files in data_files.items():
if isinstance(__a , __a ):
snake_case: List[str] = [files]
snake_case: List[Any] = [dl_manager.iter_files(__a ) for file in files]
splits.append(datasets.SplitGenerator(name=__a , gen_kwargs={'files': files} ) )
return splits
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.config.features is not None:
snake_case: Optional[Any] = self.config.features.arrow_schema
if all(not require_storage_cast(__a ) for feature in self.config.features.values() ):
# cheaper cast
snake_case: Optional[Any] = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=__a )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
snake_case: str = table_cast(__a , __a )
return pa_table
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
snake_case: Optional[int] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(__a ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(__a ) ):
snake_case: str = pd.read_csv(__a , iterator=__a , dtype=__a , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(__a ):
snake_case: int = pa.Table.from_pandas(__a )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__a )
except ValueError as e:
logger.error(F"""Failed to read file \'{file}\' with error {type(__a )}: {e}""" )
raise | 707 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
snake_case: Tuple = model.config
snake_case: str = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
snake_case: Optional[Any] = MBartConfig(
is_decoder=__A , is_encoder_decoder=__A , add_cross_attention=__A , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__A , add_final_layer_norm=__A , )
return encoder_config, decoder_config
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if "encoder.model" in name:
snake_case: Optional[Any] = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
snake_case: str = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
snake_case: Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
snake_case: Optional[int] = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
snake_case: Tuple = 'encoder.' + name
if "attn.proj" in name:
snake_case: Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
snake_case: Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
snake_case: Union[str, Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
snake_case: Dict = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
snake_case: List[str] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
snake_case: Dict = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
snake_case: Dict = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
snake_case: int = 'encoder.layernorm.bias'
return name
def lowerCAmelCase_ ( __A : List[Any] , __A : Optional[Any] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case: List[Any] = orig_state_dict.pop(__A )
if "qkv" in key:
snake_case: Union[str, Any] = key.split('.' )
snake_case: Optional[Any] = int(key_split[3] )
snake_case: Any = int(key_split[5] )
snake_case: Union[str, Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case: Union[str, Any] = val[:dim, :]
snake_case: Any = val[dim : dim * 2, :]
snake_case: List[str] = val[-dim:, :]
else:
snake_case: str = val[:dim]
snake_case: Union[str, Any] = val[dim : dim * 2]
snake_case: List[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
snake_case: Optional[int] = val
return orig_state_dict
def lowerCAmelCase_ ( __A : List[Any] , __A : Any=None , __A : List[str]=False ):
'''simple docstring'''
snake_case: str = DonutModel.from_pretrained(__A ).eval()
# load HuggingFace model
snake_case , snake_case: Optional[Any] = get_configs(__A )
snake_case: Optional[int] = DonutSwinModel(__A )
snake_case: Tuple = MBartForCausalLM(__A )
snake_case: Optional[Any] = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
snake_case: Optional[int] = original_model.state_dict()
snake_case: Optional[int] = convert_state_dict(__A , __A )
model.load_state_dict(__A )
# verify results on scanned document
snake_case: Union[str, Any] = load_dataset('hf-internal-testing/example-documents' )
snake_case: str = dataset['test'][0]['image'].convert('RGB' )
snake_case: Optional[int] = XLMRobertaTokenizerFast.from_pretrained(__A , from_slow=__A )
snake_case: Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
snake_case: Dict = DonutProcessor(__A , __A )
snake_case: Optional[Any] = processor(__A , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
snake_case: int = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
snake_case: Optional[Any] = 'When is the coffee break?'
snake_case: Optional[int] = task_prompt.replace('{user_input}' , __A )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
snake_case: Dict = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
snake_case: str = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
snake_case: str = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
snake_case: int = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
snake_case: Optional[Any] = 'hello world'
else:
raise ValueError('Model name not supported' )
snake_case: Optional[int] = original_model.decoder.tokenizer(__A , add_special_tokens=__A , return_tensors='pt' )[
'input_ids'
]
snake_case: Any = original_model.encoder.model.patch_embed(__A )
snake_case , snake_case: Dict = model.encoder.embeddings(__A )
assert torch.allclose(__A , __A , atol=1E-3 )
# verify encoder hidden states
snake_case: Tuple = original_model.encoder(__A )
snake_case: List[str] = model.encoder(__A ).last_hidden_state
assert torch.allclose(__A , __A , atol=1E-2 )
# verify decoder hidden states
snake_case: List[Any] = original_model(__A , __A , __A ).logits
snake_case: List[Any] = model(__A , decoder_input_ids=__A ).logits
assert torch.allclose(__A , __A , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
__UpperCAmelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 692 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "BridgeTowerImageProcessor"
__UpperCamelCase = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(_snake_case , _snake_case )
def __call__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Dict = self.tokenizer(
text=_snake_case , add_special_tokens=_snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , stride=_snake_case , pad_to_multiple_of=_snake_case , return_token_type_ids=_snake_case , return_attention_mask=_snake_case , return_overflowing_tokens=_snake_case , return_special_tokens_mask=_snake_case , return_offsets_mapping=_snake_case , return_length=_snake_case , verbose=_snake_case , return_tensors=_snake_case , **_snake_case , )
# add pixel_values + pixel_mask
snake_case: Union[str, Any] = self.image_processor(
_snake_case , return_tensors=_snake_case , do_normalize=_snake_case , do_center_crop=_snake_case , **_snake_case )
encoding.update(_snake_case )
return encoding
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = self.tokenizer.model_input_names
snake_case: List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 708 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
snake_case: Union[str, Any] = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 1_28,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 1_42,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , x.transpose() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = np.random.randn(3 , 4 )
snake_case: Optional[Any] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(3 , 4 , 5 )
snake_case: Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Dict = np.random.randn(3 , 4 , 5 )
snake_case: str = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Optional[int] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Optional[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) )
snake_case: Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: str = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: List[str] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.squeeze(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: List[str] = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
snake_case: List[str] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: int = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = np.random.randn(1 , 3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(1 , 3 , 4 )
snake_case: List[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Tuple = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Tuple = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Any = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = np.random.randn(3 , 4 )
snake_case: int = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.asarray(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) ) ) | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : Any , __A : Dict ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(__A ) , __A )
return number - int(__A )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3)) | 709 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__UpperCAmelCase = logging.get_logger(__name__)
# General docstring
__UpperCAmelCase = "PoolFormerConfig"
# Base docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = [1, 512, 7, 7]
# Image classification docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = "tabby, tabby cat"
__UpperCAmelCase = [
"sail/poolformer_s12",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowerCAmelCase_ ( __A : Tuple , __A : float = 0.0 , __A : bool = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
snake_case: Union[str, Any] = 1 - drop_prob
snake_case: List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
snake_case: List[Any] = keep_prob + torch.rand(__A , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
snake_case: Any = input.div(__A ) * random_tensor
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = drop_prob
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return drop_path(SCREAMING_SNAKE_CASE__ , self.drop_prob , self.training )
def _UpperCamelCase ( self ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = patch_size if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (patch_size, patch_size)
snake_case: List[str] = stride if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (stride, stride)
snake_case: Union[str, Any] = padding if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (padding, padding)
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = norm_layer(SCREAMING_SNAKE_CASE__ ) if norm_layer else nn.Identity()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.projection(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.norm(SCREAMING_SNAKE_CASE__ )
return embeddings
class SCREAMING_SNAKE_CASE ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.AvgPoolad(SCREAMING_SNAKE_CASE__ , stride=1 , padding=pool_size // 2 , count_include_pad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.pool(SCREAMING_SNAKE_CASE__ ) - hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: str = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ )
if isinstance(config.hidden_act , SCREAMING_SNAKE_CASE__ ):
snake_case: Tuple = ACTaFN[config.hidden_act]
else:
snake_case: int = config.hidden_act
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.act_fn(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.drop(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: str = self.drop(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = PoolFormerPooling(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerOutput(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
# Useful for training neural nets
snake_case: Union[str, Any] = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ ) if drop_path > 0.0 else nn.Identity()
snake_case: Optional[Any] = config.use_layer_scale
if config.use_layer_scale:
snake_case: Any = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.use_layer_scale:
snake_case: str = self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Dict = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
snake_case: str = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = ()
snake_case: Dict = self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Union[str, Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
snake_case: Any = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = (output,) + outputs
return outputs
else:
snake_case: Optional[Any] = self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) ) )
# First residual connection
snake_case: Union[str, Any] = pooling_output + hidden_states
snake_case: List[Any] = ()
# Second residual connection inside the PoolFormerOutput block
snake_case: List[str] = self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: Dict = hidden_states + layer_output
snake_case: Optional[Any] = (output,) + outputs
return outputs
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = config
# stochastic depth decay rule
snake_case: List[Any] = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
snake_case: Union[str, Any] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
snake_case: List[Any] = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
# Transformer blocks
snake_case: str = []
snake_case: int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
snake_case: List[str] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
SCREAMING_SNAKE_CASE__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__ ) )
snake_case: Tuple = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True ):
'''simple docstring'''
snake_case: str = () if output_hidden_states else None
snake_case: Dict = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
snake_case , snake_case: Dict = layers
# Get patch embeddings from hidden_states
snake_case: int = embedding_layer(SCREAMING_SNAKE_CASE__ )
# Send the embeddings through the blocks
for _, blk in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = blk(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = layer_outputs[0]
if output_hidden_states:
snake_case: List[str] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = PoolFormerConfig
__UpperCamelCase = "poolformer"
__UpperCamelCase = "pixel_values"
__UpperCamelCase = True
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE__ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = value
__UpperCAmelCase = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
__UpperCAmelCase = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n"
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = config
snake_case: Tuple = PoolFormerEncoder(SCREAMING_SNAKE_CASE__ )
# Initialize weights and apply final processing
self.post_init()
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case: List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
snake_case: Optional[Any] = self.encoder(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: List[Any] = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Any = nn.Linear(config.hidden_size , config.hidden_size )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.dense(SCREAMING_SNAKE_CASE__ )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = config.num_labels
snake_case: str = PoolFormerModel(SCREAMING_SNAKE_CASE__ )
# Final norm
snake_case: int = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
snake_case: Dict = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
snake_case: Optional[Any] = self.poolformer(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: Any = outputs[0]
snake_case: str = self.classifier(self.norm(SCREAMING_SNAKE_CASE__ ).mean([-2, -1] ) )
snake_case: Any = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case: Tuple = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case: Dict = 'single_label_classification'
else:
snake_case: List[str] = 'multi_label_classification'
if self.config.problem_type == "regression":
snake_case: Union[str, Any] = MSELoss()
if self.num_labels == 1:
snake_case: List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case: int = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.config.problem_type == "single_label_classification":
snake_case: Union[str, Any] = CrossEntropyLoss()
snake_case: Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case: int = BCEWithLogitsLoss()
snake_case: Optional[int] = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not return_dict:
snake_case: str = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states ) | 692 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__UpperCAmelCase = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
logging.set_verbosity_info()
def lowerCAmelCase_ ( __A : Any , __A : Optional[int] , __A : Dict , __A : int=None ):
'''simple docstring'''
snake_case: Union[str, Any] = XLNetConfig.from_json_file(snake_case_ )
snake_case: List[str] = finetuning_task.lower() if finetuning_task is not None else """"""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}""" )
snake_case: str = finetuning_task
snake_case: int = GLUE_TASKS_NUM_LABELS[finetuning_task]
snake_case: int = XLNetForSequenceClassification(snake_case_ )
elif "squad" in finetuning_task:
snake_case: Optional[Any] = finetuning_task
snake_case: str = XLNetForQuestionAnswering(snake_case_ )
else:
snake_case: Optional[int] = XLNetLMHeadModel(snake_case_ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(snake_case_ , snake_case_ , snake_case_ )
# Save pytorch-model
snake_case: Union[str, Any] = os.path.join(snake_case_ , snake_case_ )
snake_case: Any = os.path.join(snake_case_ , snake_case_ )
print(f"""Save PyTorch model to {os.path.abspath(snake_case_ )}""" )
torch.save(model.state_dict() , snake_case_ )
print(f"""Save configuration file to {os.path.abspath(snake_case_ )}""" )
with open(snake_case_ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--xlnet_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained XLNet model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--finetuning_task",
default=None,
type=str,
help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
)
__UpperCAmelCase = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
) | 710 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __A : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case: Any = cst_fwd.get(__A , np.inf )
snake_case: int = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
snake_case: Union[str, Any] = new_cost_f
snake_case: Tuple = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
snake_case: List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[Any] = -1
snake_case: Any = set()
snake_case: str = set()
snake_case: int = {source: 0}
snake_case: Dict = {destination: 0}
snake_case: int = {source: None}
snake_case: Union[str, Any] = {destination: None}
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: Tuple = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
snake_case , snake_case: List[str] = queue_forward.get()
visited_forward.add(__A )
snake_case , snake_case: int = queue_backward.get()
visited_backward.add(__A )
snake_case: str = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
snake_case: Optional[Any] = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
snake_case: Any = shortest_distance
return shortest_path_distance
__UpperCAmelCase = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
__UpperCAmelCase = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: List[Any] = os.path.dirname(os.path.realpath(__UpperCamelCase ) )
snake_case: int = os.path.join(__UpperCamelCase , 'triangle.txt' )
with open(__UpperCamelCase ) as f:
snake_case: Dict = f.readlines()
snake_case: Optional[Any] = []
for line in triangle:
snake_case: Optional[Any] = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(__UpperCamelCase ) )
a.append(__UpperCamelCase )
for i in range(1 , len(__UpperCamelCase ) ):
for j in range(len(a[i] ) ):
snake_case: Optional[Any] = a[i - 1][j] if j != len(a[i - 1] ) else 0
snake_case: List[Any] = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(__UpperCamelCase , __UpperCamelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution()) | 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "▁"
__UpperCAmelCase = {"vocab_file": "sentencepiece.bpe.model"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__UpperCAmelCase = {
"facebook/xglm-564M": 2_048,
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
snake_case: Optional[Any] = 7
snake_case: List[str] = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
snake_case: str = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
snake_case: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
snake_case: int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case: Tuple = 1
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case: Optional[Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
snake_case: Union[str, Any] = len(self.sp_model )
snake_case: str = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
snake_case: List[Any] = self.__dict__.copy()
snake_case: Union[str, Any] = None
snake_case: Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case: Union[str, Any] = {}
snake_case: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
snake_case: Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: int = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case: Dict = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = ''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ' ' ).strip()
return out_string
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case: List[str] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as fi:
snake_case: int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,) | 692 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "convbert"
def __init__( self , SCREAMING_SNAKE_CASE__=3_05_22 , SCREAMING_SNAKE_CASE__=7_68 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=30_72 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=7_68 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=9 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
snake_case: Optional[int] = vocab_size
snake_case: List[str] = hidden_size
snake_case: List[str] = num_hidden_layers
snake_case: Tuple = num_attention_heads
snake_case: List[Any] = intermediate_size
snake_case: List[str] = hidden_act
snake_case: int = hidden_dropout_prob
snake_case: Dict = attention_probs_dropout_prob
snake_case: Dict = max_position_embeddings
snake_case: Union[str, Any] = type_vocab_size
snake_case: Optional[int] = initializer_range
snake_case: List[str] = layer_norm_eps
snake_case: Optional[int] = embedding_size
snake_case: Optional[int] = head_ratio
snake_case: List[Any] = conv_kernel_size
snake_case: Union[str, Any] = num_groups
snake_case: Dict = classifier_dropout
class SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case: List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] ) | 712 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
return getitem, k
def lowerCAmelCase_ ( __A : Any , __A : Optional[int] ):
'''simple docstring'''
return setitem, k, v
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
return delitem, k
def lowerCAmelCase_ ( __A : str , __A : int , *__A : Tuple ):
'''simple docstring'''
try:
return fun(__A , *__A ), None
except Exception as e:
return None, e
__UpperCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__UpperCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
snake_case: List[Any] = HashMap(initial_block_size=4 )
snake_case: List[Any] = {}
for _, (fun, *args) in enumerate(__A ):
snake_case , snake_case: Optional[int] = _run_operation(__A , __A , *__A )
snake_case , snake_case: str = _run_operation(__A , __A , *__A )
assert my_res == py_res
assert str(__A ) == str(__A )
assert set(__A ) == set(__A )
assert len(__A ) == len(__A )
assert set(my.items() ) == set(py.items() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
def is_public(__A : str ) -> bool:
return not name.startswith('_' )
snake_case: Dict = {name for name in dir({} ) if is_public(__A )}
snake_case: List[str] = {name for name in dir(HashMap() ) if is_public(__A )}
assert dict_public_names > hash_public_names | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : list ):
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9] | 713 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: List[str] = getattr(__A , __A )
if weight_type is not None:
snake_case: Optional[int] = getattr(__A , __A ).shape
else:
snake_case: Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Optional[int] = value
elif weight_type == "weight_g":
snake_case: List[str] = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: Optional[Any] = value
else:
snake_case: int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: List[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case: Dict = None
for name, value in fairseq_dict.items():
snake_case: Tuple = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
elif name.split('.' )[0] == "proj":
snake_case: List[Any] = fairseq_model.proj
snake_case: int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: int = True
if "*" in mapped_key:
snake_case: List[str] = name.split(__A )[0].split('.' )[-2]
snake_case: Dict = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Tuple = 'weight_g'
elif "weight_v" in name:
snake_case: int = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
snake_case: List[Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCAmelCase_ ( __A : List[str] , __A : List[Any] , __A : int , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: Tuple = name.split('.' )
snake_case: Any = int(items[0] )
snake_case: Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case , snake_case: List[Any] = emb.weight.shape
snake_case: Optional[int] = nn.Linear(__A , __A , bias=__A )
snake_case: Any = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
with open(__A , 'r' , encoding='utf-8' ) as f:
snake_case: List[Any] = f.readlines()
snake_case: Any = [line.split(' ' )[0] for line in lines]
snake_case: int = len(__A )
snake_case: Dict = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(__A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : int , __A : str , ):
'''simple docstring'''
snake_case: Union[str, Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: str = SpeechaTextaConfig.from_pretrained(
__A , vocab_size=__A , decoder_layers=__A , do_stable_layer_norm=__A )
snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
snake_case: List[Any] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case: Optional[Any] = WavaVecaModel(__A )
snake_case: Any = recursively_load_weights_wavaveca(model.encoder , __A )
snake_case: Union[str, Any] = SpeechaTextaForCausalLM(__A )
snake_case , snake_case: Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__A )
# set output linear layer
unexpected_keys.remove('embed_out' )
snake_case: str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
snake_case: int = SpeechEncoderDecoderModel(encoder=__A , decoder=__A )
snake_case: List[Any] = False
# add projection layer
snake_case: Union[str, Any] = nn.Parameter(projection_layer.weight )
snake_case: Union[str, Any] = nn.Parameter(projection_layer.bias )
snake_case: List[Any] = create_vocab_dict(__A )
with open(os.path.join(__A , 'vocab.json' ) , 'w' ) as fp:
json.dump(__A , __A )
snake_case: Union[str, Any] = SpeechaTextaTokenizer(os.path.join(__A , 'vocab.json' ) )
tokenizer.save_pretrained(__A )
snake_case: Tuple = hf_wavavec.config.to_dict()
snake_case: int = tokenizer.pad_token_id
snake_case: Dict = tokenizer.bos_token_id
snake_case: Optional[int] = tokenizer.eos_token_id
snake_case: Dict = 'speech_to_text_2'
snake_case: Optional[Any] = 'wav2vec2'
snake_case: Tuple = SpeechEncoderDecoderConfig.from_dict(__A )
hf_wavavec.save_pretrained(__A )
feature_extractor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
) | 692 | 0 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
@wraps(a__ )
def _inner_fn(*__A : Optional[int] , **__A : Tuple ):
warnings.warn(
(f"""\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.""") , a__ , )
return fn(*a__ , **a__ )
return _inner_fn | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }') | 692 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = inspect.getfile(accelerate.test_utils )
snake_case: str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
snake_case: List[Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_distributed_data_loop.py'] )
snake_case: Union[str, Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_ops.py'] )
@require_multi_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
print(F"""Found {torch.cuda.device_count()} devices.""" )
snake_case: Dict = ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A , env=os.environ.copy() )
@require_multi_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
print(F"""Found {torch.cuda.device_count()} devices.""" )
snake_case: str = ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
print(F"""Command: {cmd}""" )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A , env=os.environ.copy() )
@require_multi_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A , env=os.environ.copy() )
@require_multi_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only""" )
snake_case: List[str] = ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='0,1' ):
execute_subprocess_async(__A , env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = Accelerator()
__UpperCAmelCase = (accelerator.state.process_index + 2, 10)
__UpperCAmelCase = torch.randint(0, 10, shape).to(accelerator.device)
__UpperCAmelCase = ""
__UpperCAmelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
__UpperCAmelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
__UpperCAmelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg) | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__UpperCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
snake_case: List[Any] = k.replace(__A , __A )
return k
def lowerCAmelCase_ ( __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[int] = BigBirdPegasusConfig(**__A )
snake_case: List[Any] = BigBirdPegasusForConditionalGeneration(__A )
snake_case: Any = torch_model.state_dict()
snake_case: Any = {}
# separating decoder weights
snake_case: Optional[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
snake_case: Any = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
snake_case: List[str] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Any = DECODER_PATTERNS
snake_case: int = rename_state_dict_key(__A , __A )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: Optional[Any] = v.T
snake_case: Any = torch.from_numpy(__A )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
snake_case: List[Any] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Union[str, Any] = REMAINING_PATTERNS
snake_case: str = rename_state_dict_key(__A , __A )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: int = v.T
snake_case: Any = torch.from_numpy(__A )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
snake_case: str = mapping['model.embed_positions.weight']
snake_case: Any = mapping.pop('model.embed_positions.weight' )
snake_case , snake_case: Union[str, Any] = torch_model.load_state_dict(__A , strict=__A )
snake_case: Optional[int] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Tuple = tf.train.list_variables(__A )
snake_case: str = {}
snake_case: List[str] = ['global_step']
for name, shape in tqdm(__A , desc='converting tf checkpoint to dict' ):
snake_case: str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case: Any = tf.train.load_variable(__A , __A )
snake_case: Optional[int] = array
return tf_weights
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict ):
'''simple docstring'''
snake_case: int = get_tf_weights_as_numpy(__A )
snake_case: int = convert_bigbird_pegasus(__A , __A )
torch_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 692 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__UpperCAmelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lowerCAmelCase_ ( __A : Any , __A : List[Any] , __A : Optional[int] , __A : int , __A : Optional[Any] , __A : str , __A : List[str] , __A : str=False , ):
'''simple docstring'''
output_path.parent.mkdir(parents=_UpperCAmelCase , exist_ok=_UpperCAmelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_UpperCAmelCase , _UpperCAmelCase , f=output_path.as_posix() , input_names=_UpperCAmelCase , output_names=_UpperCAmelCase , dynamic_axes=_UpperCAmelCase , do_constant_folding=_UpperCAmelCase , use_external_data_format=_UpperCAmelCase , enable_onnx_checker=_UpperCAmelCase , opset_version=_UpperCAmelCase , )
else:
export(
_UpperCAmelCase , _UpperCAmelCase , f=output_path.as_posix() , input_names=_UpperCAmelCase , output_names=_UpperCAmelCase , dynamic_axes=_UpperCAmelCase , do_constant_folding=_UpperCAmelCase , opset_version=_UpperCAmelCase , )
@torch.no_grad()
def lowerCAmelCase_ ( __A : int , __A : Dict , __A : Optional[Any] , __A : str = False ):
'''simple docstring'''
snake_case: int = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
snake_case: Tuple = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
snake_case: Dict = 'cpu'
snake_case: str = Path(_UpperCAmelCase )
# VAE DECODER
snake_case: List[str] = AutoencoderKL.from_pretrained(model_path + '/vae' )
snake_case: List[Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
snake_case: Tuple = vae_decoder.decode
onnx_export(
_UpperCAmelCase , model_args=(
torch.randn(1 , _UpperCAmelCase , 25 , 25 ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_UpperCAmelCase , )
del vae_decoder
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
__UpperCAmelCase = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX") | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case: int = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case: Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__UpperCAmelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 692 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Optional[Any]=False ):
'''simple docstring'''
snake_case: str = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case: int = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def lowerCAmelCase_ ( __A : str , __A : Dict , __A : Optional[Any]=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
snake_case: Optional[int] = ''
else:
snake_case: List[str] = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case: List[str] = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
snake_case: Union[str, Any] = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case: Any = in_proj_weight[
: config.hidden_size, :
]
snake_case: Tuple = in_proj_bias[: config.hidden_size]
snake_case: Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case: Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case: List[Any] = in_proj_weight[
-config.hidden_size :, :
]
snake_case: Optional[int] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
snake_case: Optional[int] = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(__A , __A )
def lowerCAmelCase_ ( __A : Tuple , __A : str , __A : Optional[Any] ):
'''simple docstring'''
snake_case: List[str] = dct.pop(__A )
snake_case: List[str] = val
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Tuple = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case: Optional[Any] = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __A : Tuple , __A : Union[str, Any] , __A : Dict=True ):
'''simple docstring'''
snake_case: Optional[int] = ViTConfig()
# patch_size
if model_name[-1] == "8":
snake_case: Optional[int] = 8
# set labels if required
if not base_model:
snake_case: Any = 10_00
snake_case: Tuple = 'huggingface/label-files'
snake_case: str = 'imagenet-1k-id2label.json'
snake_case: int = json.load(open(hf_hub_download(__A , __A , repo_type='dataset' ) , 'r' ) )
snake_case: Any = {int(__A ): v for k, v in idalabel.items()}
snake_case: Tuple = idalabel
snake_case: Optional[Any] = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
snake_case: str = 3_84
snake_case: Dict = 15_36
snake_case: Tuple = 12
snake_case: Dict = 6
# load original model from torch hub
snake_case: List[str] = torch.hub.load('facebookresearch/dino:main' , __A )
original_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case: List[Any] = original_model.state_dict()
if base_model:
remove_classification_head_(__A )
snake_case: Any = create_rename_keys(__A , base_model=__A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
read_in_q_k_v(__A , __A , __A )
# load HuggingFace model
if base_model:
snake_case: Union[str, Any] = ViTModel(__A , add_pooling_layer=__A ).eval()
else:
snake_case: Optional[Any] = ViTForImageClassification(__A ).eval()
model.load_state_dict(__A )
# Check outputs on an image, prepared by ViTImageProcessor
snake_case: Tuple = ViTImageProcessor()
snake_case: List[Any] = image_processor(images=prepare_img() , return_tensors='pt' )
snake_case: List[Any] = encoding['pixel_values']
snake_case: Optional[Any] = model(__A )
if base_model:
snake_case: List[Any] = original_model(__A )
assert torch.allclose(__A , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
snake_case: str = original_model(__A )
assert logits.shape == outputs.logits.shape
assert torch.allclose(__A , outputs.logits , atol=1E-3 )
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
__UpperCAmelCase = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model) | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = tempfile.mkdtemp()
snake_case: Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
snake_case: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
snake_case: Optional[int] = {
'do_resize': True,
'size': {'height': 2_24, 'width': 2_24},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'do_convert_rgb': True,
}
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
snake_case: Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer()
snake_case: Union[str, Any] = self.get_image_processor()
snake_case: List[str] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
snake_case: List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
snake_case: Any = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
snake_case: Union[str, Any] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_image_processor()
snake_case: Tuple = self.get_tokenizer()
snake_case: Optional[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.prepare_image_inputs()
snake_case: List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
snake_case: Dict = processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_image_processor()
snake_case: Optional[int] = self.get_tokenizer()
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Union[str, Any] = processor(text=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Tuple = self.prepare_image_inputs()
snake_case: Any = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.get_image_processor()
snake_case: str = self.get_tokenizer()
snake_case: Union[str, Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case: int = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = 'Alexandra，T-shirt的价格是15便士。'
snake_case: List[Any] = self.prepare_image_inputs()
snake_case: Dict = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 692 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
__UpperCAmelCase = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: int = torch.load(_lowercase , map_location='cpu' )
return sd
def lowerCAmelCase_ ( __A : Dict , __A : Any , __A : str=rename_keys_prefix ):
'''simple docstring'''
snake_case: int = OrderedDict()
snake_case: List[str] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case: str = key
for name_pair in rename_keys_prefix:
snake_case: str = new_key.replace(name_pair[0] , name_pair[1] )
snake_case: Dict = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case: Tuple = new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Optional[int] ):
'''simple docstring'''
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case: List[Any] = 'pretraining'
if "vcr" in checkpoint_path:
snake_case: Dict = {'visual_embedding_dim': 5_12}
elif "vqa_advanced" in checkpoint_path:
snake_case: Any = {'visual_embedding_dim': 20_48}
elif "vqa" in checkpoint_path:
snake_case: List[str] = {'visual_embedding_dim': 20_48}
elif "nlvr" in checkpoint_path:
snake_case: str = {'visual_embedding_dim': 10_24}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case: int = {'visual_embedding_dim': 5_12}
snake_case: Dict = 'multichoice'
elif "vqa_advanced" in checkpoint_path:
snake_case: int = {'visual_embedding_dim': 20_48}
snake_case: str = 'vqa_advanced'
elif "vqa" in checkpoint_path:
snake_case: List[Any] = {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
snake_case: Tuple = 'vqa'
elif "nlvr" in checkpoint_path:
snake_case: Optional[Any] = {
'visual_embedding_dim': 10_24,
'num_labels': 2,
}
snake_case: List[Any] = 'nlvr'
snake_case: Optional[Any] = VisualBertConfig(**_lowercase )
# Load State Dict
snake_case: str = load_state_dict(_lowercase )
snake_case: Dict = get_new_dict(_lowercase , _lowercase )
if model_type == "pretraining":
snake_case: Dict = VisualBertForPreTraining(_lowercase )
elif model_type == "vqa":
snake_case: int = VisualBertForQuestionAnswering(_lowercase )
elif model_type == "nlvr":
snake_case: Dict = VisualBertForVisualReasoning(_lowercase )
elif model_type == "multichoice":
snake_case: Any = VisualBertForMultipleChoice(_lowercase )
model.load_state_dict(_lowercase )
# Save Checkpoints
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
__UpperCAmelCase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path) | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "swinv2"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: int = image_size
snake_case: Union[str, Any] = patch_size
snake_case: List[str] = num_channels
snake_case: Tuple = embed_dim
snake_case: str = depths
snake_case: Any = len(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = num_heads
snake_case: Optional[int] = window_size
snake_case: Any = mlp_ratio
snake_case: Optional[int] = qkv_bias
snake_case: Union[str, Any] = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: Dict = drop_path_rate
snake_case: List[str] = hidden_act
snake_case: int = use_absolute_embeddings
snake_case: Any = layer_norm_eps
snake_case: Dict = initializer_range
snake_case: List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Tuple = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
snake_case: Union[str, Any] = (0, 0, 0, 0) | 692 | 0 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__UpperCAmelCase = "sshleifer/bart-tiny-random"
__UpperCAmelCase = "patrickvonplaten/t5-tiny-random"
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return AutoConfig.from_pretrained(_lowercase )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = create_student_by_copying_alternating_layers(_lowercase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = create_student_by_copying_alternating_layers(_lowercase , tempfile.mkdtemp() , e=1 , d=_lowercase )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = create_student_by_copying_alternating_layers(_lowercase , tempfile.mkdtemp() , e=1 , d=_lowercase )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = create_student_by_copying_alternating_layers(_lowercase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _UpperCamelCase ( self ):
'''simple docstring'''
with self.assertRaises(_lowercase ):
create_student_by_copying_alternating_layers(_lowercase , tempfile.mkdtemp() , e=_lowercase , d=_lowercase )
| 719 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = '''linear'''
__UpperCamelCase = '''cosine'''
__UpperCamelCase = '''cosine_with_restarts'''
__UpperCamelCase = '''polynomial'''
__UpperCamelCase = '''constant'''
__UpperCamelCase = '''constant_with_warmup'''
__UpperCamelCase = '''piecewise_constant'''
def lowerCAmelCase_ ( __A : Optimizer , __A : int = -1 ):
'''simple docstring'''
return LambdaLR(lowercase_ , lambda __A : 1 , last_epoch=lowercase_ )
def lowerCAmelCase_ ( __A : Optimizer , __A : int , __A : int = -1 ):
'''simple docstring'''
def lr_lambda(__A : int ):
if current_step < num_warmup_steps:
return float(lowercase_ ) / float(max(1.0 , lowercase_ ) )
return 1.0
return LambdaLR(lowercase_ , lowercase_ , last_epoch=lowercase_ )
def lowerCAmelCase_ ( __A : Optimizer , __A : str , __A : int = -1 ):
'''simple docstring'''
snake_case: List[Any] = {}
snake_case: List[str] = step_rules.split(',' )
for rule_str in rule_list[:-1]:
snake_case: Union[str, Any] = rule_str.split(':' )
snake_case: List[Any] = int(lowercase_ )
snake_case: Union[str, Any] = float(lowercase_ )
snake_case: Optional[int] = value
snake_case: Dict = float(rule_list[-1] )
def create_rules_function(__A : str , __A : Union[str, Any] ):
def rule_func(__A : int ) -> float:
snake_case: Optional[Any] = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(lowercase_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
snake_case: Optional[Any] = create_rules_function(lowercase_ , lowercase_ )
return LambdaLR(lowercase_ , lowercase_ , last_epoch=lowercase_ )
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Union[str, Any] , __A : Tuple , __A : Optional[int]=-1 ):
'''simple docstring'''
def lr_lambda(__A : int ):
if current_step < num_warmup_steps:
return float(lowercase_ ) / float(max(1 , lowercase_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(lowercase_ , lowercase_ , lowercase_ )
def lowerCAmelCase_ ( __A : Optimizer , __A : int , __A : int , __A : float = 0.5 , __A : int = -1 ):
'''simple docstring'''
def lr_lambda(__A : Dict ):
if current_step < num_warmup_steps:
return float(lowercase_ ) / float(max(1 , lowercase_ ) )
snake_case: List[Any] = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(lowercase_ ) * 2.0 * progress )) )
return LambdaLR(lowercase_ , lowercase_ , lowercase_ )
def lowerCAmelCase_ ( __A : Optimizer , __A : int , __A : int , __A : int = 1 , __A : int = -1 ):
'''simple docstring'''
def lr_lambda(__A : str ):
if current_step < num_warmup_steps:
return float(lowercase_ ) / float(max(1 , lowercase_ ) )
snake_case: Union[str, Any] = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(lowercase_ ) * progress) % 1.0) )) )
return LambdaLR(lowercase_ , lowercase_ , lowercase_ )
def lowerCAmelCase_ ( __A : Optional[int] , __A : List[str] , __A : int , __A : Dict=1E-7 , __A : List[str]=1.0 , __A : Tuple=-1 ):
'''simple docstring'''
snake_case: Any = optimizer.defaults["""lr"""]
if not (lr_init > lr_end):
raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(__A : int ):
if current_step < num_warmup_steps:
return float(lowercase_ ) / float(max(1 , lowercase_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
snake_case: Optional[int] = lr_init - lr_end
snake_case: Optional[int] = num_training_steps - num_warmup_steps
snake_case: Any = 1 - (current_step - num_warmup_steps) / decay_steps
snake_case: str = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(lowercase_ , lowercase_ , lowercase_ )
__UpperCAmelCase = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCAmelCase_ ( __A : Union[str, SchedulerType] , __A : Optimizer , __A : Optional[str] = None , __A : Optional[int] = None , __A : Optional[int] = None , __A : int = 1 , __A : float = 1.0 , __A : int = -1 , ):
'''simple docstring'''
snake_case: Dict = SchedulerType(lowercase_ )
snake_case: int = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(lowercase_ , last_epoch=lowercase_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(lowercase_ , step_rules=lowercase_ , last_epoch=lowercase_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(lowercase_ , num_warmup_steps=lowercase_ , last_epoch=lowercase_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
lowercase_ , num_warmup_steps=lowercase_ , num_training_steps=lowercase_ , num_cycles=lowercase_ , last_epoch=lowercase_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
lowercase_ , num_warmup_steps=lowercase_ , num_training_steps=lowercase_ , power=lowercase_ , last_epoch=lowercase_ , )
return schedule_func(
lowercase_ , num_warmup_steps=lowercase_ , num_training_steps=lowercase_ , last_epoch=lowercase_ ) | 720 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = question_encoder
snake_case: Union[str, Any] = generator
snake_case: Optional[int] = self.question_encoder
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case: int = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
snake_case: str = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
snake_case: Dict = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.question_encoder
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.generator
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "longest" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
snake_case: Optional[Any] = self.current_tokenizer.model_max_length
snake_case: int = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case: Any = self.current_tokenizer.model_max_length
snake_case: List[str] = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: Dict = labels['input_ids']
return model_inputs | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
"tokenization_convbert": ["ConvBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["ConvBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvBertForMaskedLM",
"ConvBertForMultipleChoice",
"ConvBertForQuestionAnswering",
"ConvBertForSequenceClassification",
"ConvBertForTokenClassification",
"ConvBertLayer",
"ConvBertModel",
"ConvBertPreTrainedModel",
"load_tf_weights_in_convbert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFConvBertForMaskedLM",
"TFConvBertForMultipleChoice",
"TFConvBertForQuestionAnswering",
"TFConvBertForSequenceClassification",
"TFConvBertForTokenClassification",
"TFConvBertLayer",
"TFConvBertModel",
"TFConvBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 721 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'mock-s3-bucket'
snake_case: int = f"""s3://{mock_bucket}"""
snake_case: Any = extract_path_from_uri(__A )
assert dataset_path.startswith('s3://' ) is False
snake_case: Union[str, Any] = './local/path'
snake_case: Union[str, Any] = extract_path_from_uri(__A )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = is_remote_filesystem(__A )
assert is_remote is True
snake_case: int = fsspec.filesystem('file' )
snake_case: int = is_remote_filesystem(__A )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , __A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : str , __A : Optional[Any] , __A : List[str] , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
snake_case: Optional[int] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case: str = f"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=__A )
assert isinstance(__A , __A )
snake_case: Any = os.path.basename(__A )
snake_case: int = expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(__A , 'r' , encoding='utf-8' ) as f, open(__A , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def lowerCAmelCase_ ( __A : Any , __A : int , __A : int ):
'''simple docstring'''
snake_case: List[str] = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
snake_case: str = compressed_file_paths[protocol]
snake_case: Dict = 'dataset.jsonl'
snake_case: Optional[Any] = f"""{protocol}://{member_file_path}::{compressed_file_path}"""
snake_case , *snake_case: List[Any] = fsspec.get_fs_token_paths(__A )
assert fs.isfile(__A )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : List[Any] ):
'''simple docstring'''
snake_case: Tuple = hf_api.dataset_info(__A , token=__A )
snake_case: List[str] = HfFileSystem(repo_info=__A , token=__A )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(__A ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__A , __A , clobber=__A )
with pytest.warns(__A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__A ) == 1
assert (
str(warning_info[0].message )
== f"""A filesystem protocol was already set for {protocol} and will be overwritten."""
) | 692 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: str = str(_lowerCamelCase )
snake_case: List[str] = [n]
for i in range(1 , len(_lowerCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if len(str(_lowerCamelCase ) ) > 3:
if not is_prime(int(str(_lowerCamelCase )[-3:] ) ) or not is_prime(int(str(_lowerCamelCase )[:3] ) ):
return False
return True
def lowerCAmelCase_ ( __A : int = 11 ):
'''simple docstring'''
snake_case: Any = []
snake_case: Dict = 13
while len(_lowerCamelCase ) != count:
if validate(_lowerCamelCase ):
snake_case: Optional[int] = list_truncated_nums(_lowerCamelCase )
if all(is_prime(_lowerCamelCase ) for i in list_nums ):
list_truncated_primes.append(_lowerCamelCase )
num += 2
return list_truncated_primes
def lowerCAmelCase_ ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(F'{sum(compute_truncated_primes(11)) = }') | 700 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__UpperCamelCase = field(
default=1024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the training data."} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A csv or a json file containing the test data."} )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
snake_case: str = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case: Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case: Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case: str = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
snake_case: Tuple = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case: Optional[int] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case: Tuple = data_args.train_file.split('.' )[-1]
snake_case: Union[str, Any] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case: Union[str, Any] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
snake_case: List[Any] = load_dataset('csv' , data_files=__A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case: Optional[Any] = load_dataset('json' , data_files=__A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case: Tuple = raw_datasets['train'].features['label'].names
snake_case: List[str] = len(__A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case: List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
snake_case: Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case: int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case: Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case: Optional[Any] = {'Refused': 0, 'Entailed': 1}
snake_case: List[Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case: List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__A : Any ):
# Tokenize the texts
def _convert_table_text_to_pandas(__A : Dict ):
snake_case: str = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
snake_case: List[str] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case: str = examples['statement']
snake_case: int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
snake_case: List[Any] = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A )
snake_case: List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
snake_case: int = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case: List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case: Tuple = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case: Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case: Optional[int] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
snake_case: str = raw_datasets['test']
if data_args.max_predict_samples is not None:
snake_case: List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
snake_case: int = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
snake_case: List[str] = np.argmax(__A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case: str = default_data_collator
elif training_args.fpaa:
snake_case: List[str] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
snake_case: List[Any] = None
# Initialize our Trainer
snake_case: List[str] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
snake_case: str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: Optional[Any] = last_checkpoint
snake_case: Union[str, Any] = trainer.train(resume_from_checkpoint=__A )
snake_case: List[Any] = train_result.metrics
snake_case: List[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
snake_case: Optional[Any] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , __A )
trainer.save_metrics('train' , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case: Dict = trainer.evaluate(eval_dataset=__A )
snake_case: Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
snake_case: Dict = min(__A , len(__A ) )
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case: Optional[int] = predict_dataset.remove_columns('label' )
snake_case: str = trainer.predict(__A , metric_key_prefix='predict' ).predictions
snake_case: Any = np.argmax(__A , axis=1 )
snake_case: int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(__A , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(__A ):
snake_case: int = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
snake_case: Optional[int] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 692 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
__UpperCamelCase = """albert"""
def __init__( self , SCREAMING_SNAKE_CASE__=3_00_00 , SCREAMING_SNAKE_CASE__=1_28 , SCREAMING_SNAKE_CASE__=40_96 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=64 , SCREAMING_SNAKE_CASE__=1_63_84 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="gelu_new" , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="absolute" , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=3 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: int = vocab_size
snake_case: Dict = embedding_size
snake_case: Tuple = hidden_size
snake_case: int = num_hidden_layers
snake_case: List[str] = num_hidden_groups
snake_case: str = num_attention_heads
snake_case: List[Any] = inner_group_num
snake_case: Union[str, Any] = hidden_act
snake_case: Optional[Any] = intermediate_size
snake_case: int = hidden_dropout_prob
snake_case: Optional[Any] = attention_probs_dropout_prob
snake_case: str = max_position_embeddings
snake_case: Tuple = type_vocab_size
snake_case: Optional[int] = initializer_range
snake_case: List[str] = layer_norm_eps
snake_case: Dict = classifier_dropout_prob
snake_case: int = position_embedding_type
class SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
snake_case: int = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case: List[str] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] ) | 701 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : float = 0.1 ):
'''simple docstring'''
snake_case: Optional[int] = 3
snake_case: int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__A )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
"constant": get_constant_schedule,
"constant_w_warmup": get_constant_schedule_with_warmup,
}
class SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(*lowercase__ , **lowercase__ )
if config is None:
assert isinstance(self.model , lowercase__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case: str = self.model.config
else:
snake_case: Union[str, Any] = config
snake_case: Any = data_args
snake_case: Optional[Any] = self.config.tgt_vocab_size if isinstance(self.config , lowercase__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
' padding..' )
if self.args.label_smoothing == 0:
snake_case: Tuple = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case: List[Any] = label_smoothed_nll_loss
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.optimizer is None:
snake_case: Optional[Any] = ["bias", "LayerNorm.weight"]
snake_case: Dict = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
snake_case: List[str] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case: Optional[int] = Adafactor
snake_case: List[Any] = {"scale_parameter": False, "relative_step": False}
else:
snake_case: int = AdamW
snake_case: Dict = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
snake_case: Optional[Any] = self.args.learning_rate
if self.sharded_ddp:
snake_case: List[str] = OSS(
params=lowercase__ , optim=lowercase__ , **lowercase__ , )
else:
snake_case: int = optimizer_cls(lowercase__ , **lowercase__ )
if self.lr_scheduler is None:
snake_case: List[str] = self._get_lr_scheduler(lowercase__ )
else: # ignoring --lr_scheduler
logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.' )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case: Tuple = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case: str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case: Dict = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=lowercase__ )
return scheduler
def _UpperCamelCase ( self ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case: List[str] = model(**lowercase__ , use_cache=lowercase__ )[0]
snake_case: Union[str, Any] = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case: Optional[Any] = model(**lowercase__ , labels=lowercase__ , use_cache=lowercase__ )[:2]
else:
# compute label smoothed loss
snake_case: str = model(**lowercase__ , use_cache=lowercase__ )[0]
snake_case: List[str] = torch.nn.functional.log_softmax(lowercase__ , dim=-1 )
snake_case: Optional[int] = self.loss_fn(lowercase__ , lowercase__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = inputs.pop('labels' )
snake_case: Any = self._compute_loss(lowercase__ , lowercase__ , lowercase__ )
return loss
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Tuple = self._prepare_inputs(lowercase__ )
snake_case: int = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case: str = self.model.generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , **lowercase__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case: List[str] = self._pad_tensors_to_max_len(lowercase__ , gen_kwargs['max_length'] )
snake_case: Any = inputs.pop('labels' )
with torch.no_grad():
# compute loss on predict data
snake_case: str = self._compute_loss(lowercase__ , lowercase__ , lowercase__ )
snake_case: Union[str, Any] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case: Any = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case: Optional[int] = self._pad_tensors_to_max_len(lowercase__ , gen_kwargs['max_length'] )
return (loss, logits, labels)
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
F""" padded to `max_length`={max_length}""" )
snake_case: Optional[int] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case: List[str] = tensor
return padded_tensor
| 702 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] ) | 692 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : int , __A : str ):
'''simple docstring'''
snake_case: str = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
snake_case: Optional[int] = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
return torch.mm(SCREAMING_SNAKE_CASE_ , normalized_text_embeds.t() )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = CLIPConfig
__UpperCamelCase = ["CLIPEncoderLayer"]
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = CLIPVisionModel(config.vision_config )
snake_case: Tuple = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = nn.Parameter(torch.ones(17 ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = nn.Parameter(torch.ones(3 ) , requires_grad=SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: str = self.vision_model(SCREAMING_SNAKE_CASE__ )[1] # pooled_output
snake_case: Optional[int] = self.visual_projection(SCREAMING_SNAKE_CASE__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case: Any = cosine_distance(SCREAMING_SNAKE_CASE__ , self.special_care_embeds ).cpu().float().numpy()
snake_case: Any = cosine_distance(SCREAMING_SNAKE_CASE__ , self.concept_embeds ).cpu().float().numpy()
snake_case: List[str] = []
snake_case: Optional[int] = image_embeds.shape[0]
for i in range(SCREAMING_SNAKE_CASE__ ):
snake_case: List[str] = {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case: str = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
snake_case: int = special_cos_dist[i][concept_idx]
snake_case: str = self.special_care_embeds_weights[concept_idx].item()
snake_case: Optional[Any] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]} )
snake_case: str = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
snake_case: Optional[int] = cos_dist[i][concept_idx]
snake_case: List[Any] = self.concept_embeds_weights[concept_idx].item()
snake_case: List[str] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(SCREAMING_SNAKE_CASE__ )
result.append(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [len(res['bad_concepts'] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.vision_model(SCREAMING_SNAKE_CASE__ )[1] # pooled_output
snake_case: Optional[Any] = self.visual_projection(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = cosine_distance(SCREAMING_SNAKE_CASE__ , self.special_care_embeds )
snake_case: Optional[Any] = cosine_distance(SCREAMING_SNAKE_CASE__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case: Union[str, Any] = 0.0
snake_case: Optional[Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case: str = torch.any(special_scores > 0 , dim=1 )
snake_case: List[str] = special_care * 0.01
snake_case: Tuple = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
snake_case: int = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case: Tuple = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts | 703 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "layer_norm" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = only_cross_attention
snake_case: Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
snake_case: Tuple = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case: List[str] = AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case: str = AdaLayerNormZero(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=SCREAMING_SNAKE_CASE__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case: Tuple = (
AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , upcast_attention=SCREAMING_SNAKE_CASE__ , ) # is self-attn if encoder_hidden_states is none
else:
snake_case: int = None
snake_case: Tuple = None
# 3. Feed-forward
snake_case: Union[str, Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = FeedForward(SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , final_dropout=SCREAMING_SNAKE_CASE__ )
# let chunk size default to None
snake_case: Any = None
snake_case: Any = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = chunk_size
snake_case: str = dim
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
if self.use_ada_layer_norm:
snake_case: Optional[int] = self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case , snake_case , snake_case , snake_case , snake_case: int = self.norma(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=hidden_states.dtype )
else:
snake_case: List[str] = self.norma(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case: List[str] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if self.use_ada_layer_norm_zero:
snake_case: Tuple = gate_msa.unsqueeze(1 ) * attn_output
snake_case: List[str] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case: Dict = (
self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: List[str] = attn_output + hidden_states
# 3. Feed-forward
snake_case: str = self.norma(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: str = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case: List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case: Optional[Any] = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE__ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case: int = self.ff(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case: Tuple = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 4 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: int = int(dim * mult )
snake_case: Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case: int = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if activation_fn == "gelu-approximate":
snake_case: Optional[Any] = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , approximate='tanh' )
elif activation_fn == "geglu":
snake_case: List[Any] = GEGLU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif activation_fn == "geglu-approximate":
snake_case: Optional[int] = ApproximateGELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE__ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for module in self.net:
snake_case: Optional[int] = module(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "none" ):
'''simple docstring'''
super().__init__()
snake_case: Optional[int] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = approximate
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.proj(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = self.gelu(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = nn.Linear(SCREAMING_SNAKE_CASE__ , dim_out * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case: int = self.proj(SCREAMING_SNAKE_CASE__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = self.proj(SCREAMING_SNAKE_CASE__ )
return x * torch.sigmoid(1.7_02 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Optional[Any] = nn.Embedding(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = nn.SiLU()
snake_case: Union[str, Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , embedding_dim * 2 )
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case: Dict = torch.chunk(SCREAMING_SNAKE_CASE__ , 2 )
snake_case: str = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.SiLU()
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , 6 * embedding_dim , bias=SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ , eps=1E-6 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case: str = emb.chunk(6 , dim=1 )
snake_case: Dict = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 1E-5 ):
'''simple docstring'''
super().__init__()
snake_case: str = num_groups
snake_case: str = eps
if act_fn is None:
snake_case: Dict = None
else:
snake_case: List[str] = get_activation(SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , out_dim * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.act:
snake_case: Optional[Any] = self.act(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.linear(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = emb[:, :, None, None]
snake_case , snake_case: List[Any] = emb.chunk(2 , dim=1 )
snake_case: Any = F.group_norm(SCREAMING_SNAKE_CASE__ , self.num_groups , eps=self.eps )
snake_case: Optional[int] = x * (1 + scale) + shift
return x | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__UpperCAmelCase = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 704 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = RoCBertTokenizer
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = filter_non_english
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: Any = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
snake_case: List[Any] = {}
snake_case: List[str] = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = i
snake_case: Union[str, Any] = i
snake_case: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
snake_case: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: Dict = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
snake_case: Union[str, Any] = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: str = i
snake_case: Optional[int] = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
snake_case: int = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def _UpperCamelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
snake_case: List[str] = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , return_token_type_ids=SCREAMING_SNAKE_CASE__ , return_offsets_mapping=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , )
snake_case: Optional[int] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__ , 'do_lower_case' ) else False
snake_case: int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = ['的', '人', '有']
snake_case: Any = ''.join(SCREAMING_SNAKE_CASE__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = True
snake_case: List[Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = False
snake_case: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: int = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that only the first Chinese character is not preceded by "##".
snake_case: Union[str, Any] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: int = tokenizer.encode('你好' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Any = tokenizer.encode('你是谁' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Dict = '你好，你是谁'
snake_case: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.encode_plus(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__UpperCAmelCase = re.compile(r"\b(a|an|the)\b", re.UNICODE)
__UpperCAmelCase = None
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: List[str] = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' , metavar='data.json' , help='Input data JSON file.' )
parser.add_argument('pred_file' , metavar='pred.json' , help='Model predictions.' )
parser.add_argument(
'--out-file' , '-o' , metavar='eval.json' , help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' , '-n' , metavar='na_prob.json' , help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' , '-t' , type=__snake_case , default=1.0 , help='Predict "" if no-answer probability exceeds this (default = 1.0).' , )
parser.add_argument(
'--out-image-dir' , '-p' , metavar='out_images' , default=__snake_case , help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' , '-v' , action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Dict = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case: Any = bool(qa['answers']['text'] )
return qid_to_has_ans
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
def remove_articles(__A : int ):
return ARTICLES_REGEX.sub(' ' , __snake_case )
def white_space_fix(__A : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(__A : Any ):
snake_case: str = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__A : Any ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__snake_case ) ) ) )
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
if not s:
return []
return normalize_answer(__snake_case ).split()
def lowerCAmelCase_ ( __A : int , __A : List[Any] ):
'''simple docstring'''
return int(normalize_answer(__snake_case ) == normalize_answer(__snake_case ) )
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Dict ):
'''simple docstring'''
snake_case: Tuple = get_tokens(__snake_case )
snake_case: Optional[int] = get_tokens(__snake_case )
snake_case: Optional[Any] = collections.Counter(__snake_case ) & collections.Counter(__snake_case )
snake_case: Dict = sum(common.values() )
if len(__snake_case ) == 0 or len(__snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case: Optional[int] = 1.0 * num_same / len(__snake_case )
snake_case: Optional[int] = 1.0 * num_same / len(__snake_case )
snake_case: Tuple = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: List[Any] = {}
snake_case: Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case: Optional[Any] = qa['id']
snake_case: Optional[int] = [t for t in qa['answers']['text'] if normalize_answer(__snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case: str = ['']
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
snake_case: Optional[int] = preds[qid]
# Take max over all gold answers
snake_case: List[Any] = max(compute_exact(__snake_case , __snake_case ) for a in gold_answers )
snake_case: Tuple = max(compute_fa(__snake_case , __snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( __A : str , __A : int , __A : List[str] , __A : Tuple ):
'''simple docstring'''
snake_case: Dict = {}
for qid, s in scores.items():
snake_case: Optional[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case: List[str] = float(not qid_to_has_ans[qid] )
else:
snake_case: str = s
return new_scores
def lowerCAmelCase_ ( __A : int , __A : Optional[Any] , __A : List[Any]=None ):
'''simple docstring'''
if not qid_list:
snake_case: str = len(__snake_case )
return collections.OrderedDict(
[
('exact', 1_00.0 * sum(exact_scores.values() ) / total),
('f1', 1_00.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
snake_case: Optional[int] = len(__snake_case )
return collections.OrderedDict(
[
('exact', 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Optional[Any] , __A : List[str] ):
'''simple docstring'''
for k in new_eval:
snake_case: str = new_eval[k]
def lowerCAmelCase_ ( __A : Dict , __A : str , __A : Tuple , __A : List[Any] ):
'''simple docstring'''
plt.step(__snake_case , __snake_case , color='b' , alpha=0.2 , where='post' )
plt.fill_between(__snake_case , __snake_case , step='post' , alpha=0.2 , color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__snake_case )
plt.savefig(__snake_case )
plt.clf()
def lowerCAmelCase_ ( __A : Dict , __A : Optional[Any] , __A : Optional[int] , __A : int , __A : Tuple=None , __A : Any=None ):
'''simple docstring'''
snake_case: Dict = sorted(__snake_case , key=lambda __A : na_probs[k] )
snake_case: Dict = 0.0
snake_case: Union[str, Any] = 1.0
snake_case: Optional[int] = 0.0
snake_case: List[Any] = [1.0]
snake_case: Any = [0.0]
snake_case: Tuple = 0.0
for i, qid in enumerate(__snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case: Tuple = true_pos / float(i + 1 )
snake_case: Union[str, Any] = true_pos / float(__snake_case )
if i == len(__snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__snake_case )
recalls.append(__snake_case )
if out_image:
plot_pr_curve(__snake_case , __snake_case , __snake_case , __snake_case )
return {"ap": 1_00.0 * avg_prec}
def lowerCAmelCase_ ( __A : str , __A : List[str] , __A : str , __A : List[str] , __A : Union[str, Any] , __A : int ):
'''simple docstring'''
if out_image_dir and not os.path.exists(__snake_case ):
os.makedirs(__snake_case )
snake_case: Optional[Any] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case: Union[str, Any] = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , 'pr_exact.png' ) , title='Precision-Recall curve for Exact Match score' , )
snake_case: Any = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , 'pr_f1.png' ) , title='Precision-Recall curve for F1 score' , )
snake_case: Optional[int] = {k: float(__snake_case ) for k, v in qid_to_has_ans.items()}
snake_case: List[str] = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , 'pr_oracle.png' ) , title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' , )
merge_eval(__snake_case , __snake_case , 'pr_exact' )
merge_eval(__snake_case , __snake_case , 'pr_f1' )
merge_eval(__snake_case , __snake_case , 'pr_oracle' )
def lowerCAmelCase_ ( __A : Dict , __A : Tuple , __A : Optional[int] , __A : str ):
'''simple docstring'''
if not qid_list:
return
snake_case: str = [na_probs[k] for k in qid_list]
snake_case: str = np.ones_like(__snake_case ) / float(len(__snake_case ) )
plt.hist(__snake_case , weights=__snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(__snake_case , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def lowerCAmelCase_ ( __A : Tuple , __A : List[str] , __A : Optional[int] , __A : Union[str, Any] ):
'''simple docstring'''
snake_case: Optional[Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case: Union[str, Any] = num_no_ans
snake_case: Dict = cur_score
snake_case: Optional[int] = 0.0
snake_case: Dict = sorted(__snake_case , key=lambda __A : na_probs[k] )
for i, qid in enumerate(__snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case: List[str] = scores[qid]
else:
if preds[qid]:
snake_case: List[str] = -1
else:
snake_case: Dict = 0
cur_score += diff
if cur_score > best_score:
snake_case: Tuple = cur_score
snake_case: int = na_probs[qid]
return 1_00.0 * best_score / len(__snake_case ), best_thresh
def lowerCAmelCase_ ( __A : Any , __A : Any , __A : int , __A : Tuple , __A : Tuple , __A : int ):
'''simple docstring'''
snake_case , snake_case: Optional[int] = find_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case , snake_case: str = find_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case: Optional[int] = best_exact
snake_case: Optional[int] = exact_thresh
snake_case: Any = best_fa
snake_case: List[str] = fa_thresh
def lowerCAmelCase_ ( ):
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case: Any = json.load(__snake_case )
snake_case: Any = dataset_json['data']
with open(OPTS.pred_file ) as f:
snake_case: List[str] = json.load(__snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case: Dict = json.load(__snake_case )
else:
snake_case: Union[str, Any] = {k: 0.0 for k in preds}
snake_case: Tuple = make_qid_to_has_ans(__snake_case ) # maps qid to True/False
snake_case: Union[str, Any] = [k for k, v in qid_to_has_ans.items() if v]
snake_case: List[Any] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case , snake_case: Union[str, Any] = get_raw_scores(__snake_case , __snake_case )
snake_case: str = apply_no_ans_threshold(__snake_case , __snake_case , __snake_case , OPTS.na_prob_thresh )
snake_case: Union[str, Any] = apply_no_ans_threshold(__snake_case , __snake_case , __snake_case , OPTS.na_prob_thresh )
snake_case: Optional[int] = make_eval_dict(__snake_case , __snake_case )
if has_ans_qids:
snake_case: Any = make_eval_dict(__snake_case , __snake_case , qid_list=__snake_case )
merge_eval(__snake_case , __snake_case , 'HasAns' )
if no_ans_qids:
snake_case: Tuple = make_eval_dict(__snake_case , __snake_case , qid_list=__snake_case )
merge_eval(__snake_case , __snake_case , 'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , OPTS.out_image_dir )
histogram_na_prob(__snake_case , __snake_case , OPTS.out_image_dir , 'hasAns' )
histogram_na_prob(__snake_case , __snake_case , OPTS.out_image_dir , 'noAns' )
if OPTS.out_file:
with open(OPTS.out_file , 'w' ) as f:
json.dump(__snake_case , __snake_case )
else:
print(json.dumps(__snake_case , indent=2 ) )
if __name__ == "__main__":
__UpperCAmelCase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main() | 705 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCAmelCase = 6378137.0
__UpperCAmelCase = 6356752.314245
__UpperCAmelCase = 6_378_137
def lowerCAmelCase_ ( __A : float , __A : float , __A : float , __A : float ):
'''simple docstring'''
snake_case: Optional[Any] = (AXIS_A - AXIS_B) / AXIS_A
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: Tuple = radians(__A )
snake_case: Tuple = radians(__A )
# Equation
snake_case: List[Any] = sin((phi_a - phi_a) / 2 )
snake_case: Dict = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
snake_case: Union[str, Any] = sqrt(sin_sq_phi + (cos(__A ) * cos(__A ) * sin_sq_lambda) )
return 2 * RADIUS * asin(__A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCAmelCase = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
__UpperCAmelCase = {
'''gpt-neox-20b''': 2_048,
}
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="<|endoftext|>" , SCREAMING_SNAKE_CASE__="<|endoftext|>" , SCREAMING_SNAKE_CASE__="<|endoftext|>" , SCREAMING_SNAKE_CASE__=False , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(
snake_case__ , snake_case__ , tokenizer_file=snake_case__ , unk_token=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , add_prefix_space=snake_case__ , **snake_case__ , )
snake_case: Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , snake_case__ ) != add_prefix_space:
snake_case: Tuple = getattr(snake_case__ , pre_tok_state.pop('type' ) )
snake_case: int = add_prefix_space
snake_case: List[Any] = pre_tok_class(**snake_case__ )
snake_case: Tuple = add_prefix_space
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: Union[str, Any] = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(snake_case__ , add_special_tokens=snake_case__ ) + [self.eos_token_id] )
if len(snake_case__ ) > self.model_max_length:
snake_case: List[str] = input_ids[-self.model_max_length :]
return input_ids | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 692 | 0 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = CodeGenTokenizer
__UpperCamelCase = CodeGenTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = {'''add_prefix_space''': True}
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case: List[str] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
snake_case: Dict = dict(zip(a_ , range(len(a_ ) ) ) )
snake_case: Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case: str = {"unk_token": "<unk>"}
snake_case: Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case: Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(a_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(a_ ) )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **a_ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **a_ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = "lower newer"
snake_case: Optional[Any] = "lower newer"
return input_text, output_text
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case: List[Any] = "lower newer"
snake_case: str = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
snake_case: str = tokenizer.tokenize(a_ , add_prefix_space=a_ )
self.assertListEqual(a_ , a_ )
snake_case: Dict = tokens + [tokenizer.unk_token]
snake_case: Dict = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ ) , a_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
snake_case: Union[str, Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer(add_prefix_space=a_ )
snake_case: Union[str, Any] = "lower newer"
# Testing tokenization
snake_case: Tuple = tokenizer.tokenize(a_ , add_prefix_space=a_ )
snake_case: List[str] = rust_tokenizer.tokenize(a_ )
self.assertListEqual(a_ , a_ )
# Testing conversion to ids without special tokens
snake_case: Any = tokenizer.encode(a_ , add_special_tokens=a_ , add_prefix_space=a_ )
snake_case: Tuple = rust_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
# Testing conversion to ids with special tokens
snake_case: Any = self.get_rust_tokenizer(add_prefix_space=a_ )
snake_case: Dict = tokenizer.encode(a_ , add_prefix_space=a_ )
snake_case: str = rust_tokenizer.encode(a_ )
self.assertListEqual(a_ , a_ )
# Testing the unknown token
snake_case: int = tokens + [rust_tokenizer.unk_token]
snake_case: Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_ ) , a_ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Optional[int] = self.rust_tokenizer_class.from_pretrained(a_ , **a_ )
# Simple input
snake_case: Optional[Any] = "This is a simple input"
snake_case: Tuple = ["This is a simple input 1", "This is a simple input 2"]
snake_case: int = ("This is a simple input", "This is a pair")
snake_case: List[Any] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(a_ , tokenizer_r.encode , a_ , max_length=a_ , padding='max_length' )
# Simple input
self.assertRaises(a_ , tokenizer_r.encode_plus , a_ , max_length=a_ , padding='max_length' )
# Simple input
self.assertRaises(
a_ , tokenizer_r.batch_encode_plus , a_ , max_length=a_ , padding='max_length' , )
# Pair input
self.assertRaises(a_ , tokenizer_r.encode , a_ , max_length=a_ , padding='max_length' )
# Pair input
self.assertRaises(a_ , tokenizer_r.encode_plus , a_ , max_length=a_ , padding='max_length' )
# Pair input
self.assertRaises(
a_ , tokenizer_r.batch_encode_plus , a_ , max_length=a_ , padding='max_length' , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
snake_case: Dict = "This is a simple input"
snake_case: List[Any] = ["This is a simple input looooooooong", "This is a simple input"]
snake_case: Optional[int] = ("This is a simple input", "This is a pair")
snake_case: Union[str, Any] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
snake_case: Optional[Any] = tokenizer.pad_token_id
snake_case: str = tokenizer(a_ , padding='max_length' , max_length=30 , return_tensors='np' )
snake_case: Tuple = tokenizer(a_ , padding=a_ , truncate=a_ , return_tensors='np' )
snake_case: Any = tokenizer(*a_ , padding='max_length' , max_length=60 , return_tensors='np' )
snake_case: Optional[int] = tokenizer(a_ , padding=a_ , truncate=a_ , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = "$$$"
snake_case: Dict = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=a_ , add_bos_token=a_ )
snake_case: Optional[Any] = "This is a simple input"
snake_case: List[Any] = ["This is a simple input 1", "This is a simple input 2"]
snake_case: str = tokenizer.bos_token_id
snake_case: Dict = tokenizer(a_ )
snake_case: List[str] = tokenizer(a_ )
self.assertEqual(out_s.input_ids[0] , a_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case: Union[str, Any] = tokenizer.decode(out_s.input_ids )
snake_case: Optional[int] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , a_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
snake_case: Union[str, Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
snake_case: str = "\nif len_a > len_b: result = a\nelse: result = b"
snake_case: str = tokenizer.encode(a_ )
snake_case: int = ["^#", re.escape('<|endoftext|>' ), "^'''", "^\"\"\"", "\n\n\n"]
snake_case: Optional[Any] = tokenizer.decode(a_ , truncate_before_pattern=a_ )
self.assertEqual(a_ , a_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass | 707 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
snake_case: Tuple = model.config
snake_case: str = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
snake_case: Optional[Any] = MBartConfig(
is_decoder=__A , is_encoder_decoder=__A , add_cross_attention=__A , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__A , add_final_layer_norm=__A , )
return encoder_config, decoder_config
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if "encoder.model" in name:
snake_case: Optional[Any] = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
snake_case: str = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
snake_case: Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
snake_case: Optional[int] = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
snake_case: Tuple = 'encoder.' + name
if "attn.proj" in name:
snake_case: Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
snake_case: Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
snake_case: Union[str, Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
snake_case: Dict = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
snake_case: List[str] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
snake_case: Dict = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
snake_case: Dict = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
snake_case: int = 'encoder.layernorm.bias'
return name
def lowerCAmelCase_ ( __A : List[Any] , __A : Optional[Any] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case: List[Any] = orig_state_dict.pop(__A )
if "qkv" in key:
snake_case: Union[str, Any] = key.split('.' )
snake_case: Optional[Any] = int(key_split[3] )
snake_case: Any = int(key_split[5] )
snake_case: Union[str, Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case: Union[str, Any] = val[:dim, :]
snake_case: Any = val[dim : dim * 2, :]
snake_case: List[str] = val[-dim:, :]
else:
snake_case: str = val[:dim]
snake_case: Union[str, Any] = val[dim : dim * 2]
snake_case: List[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
snake_case: Optional[int] = val
return orig_state_dict
def lowerCAmelCase_ ( __A : List[Any] , __A : Any=None , __A : List[str]=False ):
'''simple docstring'''
snake_case: str = DonutModel.from_pretrained(__A ).eval()
# load HuggingFace model
snake_case , snake_case: Optional[Any] = get_configs(__A )
snake_case: Optional[int] = DonutSwinModel(__A )
snake_case: Tuple = MBartForCausalLM(__A )
snake_case: Optional[Any] = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
snake_case: Optional[int] = original_model.state_dict()
snake_case: Optional[int] = convert_state_dict(__A , __A )
model.load_state_dict(__A )
# verify results on scanned document
snake_case: Union[str, Any] = load_dataset('hf-internal-testing/example-documents' )
snake_case: str = dataset['test'][0]['image'].convert('RGB' )
snake_case: Optional[int] = XLMRobertaTokenizerFast.from_pretrained(__A , from_slow=__A )
snake_case: Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
snake_case: Dict = DonutProcessor(__A , __A )
snake_case: Optional[Any] = processor(__A , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
snake_case: int = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
snake_case: Optional[Any] = 'When is the coffee break?'
snake_case: Optional[int] = task_prompt.replace('{user_input}' , __A )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
snake_case: Dict = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
snake_case: str = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
snake_case: str = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
snake_case: int = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
snake_case: Optional[Any] = 'hello world'
else:
raise ValueError('Model name not supported' )
snake_case: Optional[int] = original_model.decoder.tokenizer(__A , add_special_tokens=__A , return_tensors='pt' )[
'input_ids'
]
snake_case: Any = original_model.encoder.model.patch_embed(__A )
snake_case , snake_case: Dict = model.encoder.embeddings(__A )
assert torch.allclose(__A , __A , atol=1E-3 )
# verify encoder hidden states
snake_case: Tuple = original_model.encoder(__A )
snake_case: List[str] = model.encoder(__A ).last_hidden_state
assert torch.allclose(__A , __A , atol=1E-2 )
# verify decoder hidden states
snake_case: List[Any] = original_model(__A , __A , __A ).logits
snake_case: List[Any] = model(__A , decoder_input_ids=__A ).logits
assert torch.allclose(__A , __A , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
__UpperCAmelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 692 | 0 |
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: List[str] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCAmelCase_ ( __A : str , __A : List[str] , __A : Optional[Any] ):
'''simple docstring'''
snake_case: Tuple = 0
while b > 0:
if b & 1:
snake_case: Union[str, Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 708 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
snake_case: Union[str, Any] = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 1_28,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 1_42,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , x.transpose() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = np.random.randn(3 , 4 )
snake_case: Optional[Any] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(3 , 4 , 5 )
snake_case: Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Dict = np.random.randn(3 , 4 , 5 )
snake_case: str = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Optional[int] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Optional[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) )
snake_case: Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: str = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: List[str] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.squeeze(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: List[str] = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
snake_case: List[str] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: int = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = np.random.randn(1 , 3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(1 , 3 , 4 )
snake_case: List[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Tuple = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Tuple = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Any = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = np.random.randn(3 , 4 )
snake_case: int = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.asarray(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) ) ) | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( __A : list[float] ):
'''simple docstring'''
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 709 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__UpperCAmelCase = logging.get_logger(__name__)
# General docstring
__UpperCAmelCase = "PoolFormerConfig"
# Base docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = [1, 512, 7, 7]
# Image classification docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = "tabby, tabby cat"
__UpperCAmelCase = [
"sail/poolformer_s12",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowerCAmelCase_ ( __A : Tuple , __A : float = 0.0 , __A : bool = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
snake_case: Union[str, Any] = 1 - drop_prob
snake_case: List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
snake_case: List[Any] = keep_prob + torch.rand(__A , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
snake_case: Any = input.div(__A ) * random_tensor
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = drop_prob
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return drop_path(SCREAMING_SNAKE_CASE__ , self.drop_prob , self.training )
def _UpperCamelCase ( self ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = patch_size if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (patch_size, patch_size)
snake_case: List[str] = stride if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (stride, stride)
snake_case: Union[str, Any] = padding if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (padding, padding)
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = norm_layer(SCREAMING_SNAKE_CASE__ ) if norm_layer else nn.Identity()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.projection(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.norm(SCREAMING_SNAKE_CASE__ )
return embeddings
class SCREAMING_SNAKE_CASE ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.AvgPoolad(SCREAMING_SNAKE_CASE__ , stride=1 , padding=pool_size // 2 , count_include_pad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.pool(SCREAMING_SNAKE_CASE__ ) - hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: str = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ )
if isinstance(config.hidden_act , SCREAMING_SNAKE_CASE__ ):
snake_case: Tuple = ACTaFN[config.hidden_act]
else:
snake_case: int = config.hidden_act
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.act_fn(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.drop(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: str = self.drop(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = PoolFormerPooling(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerOutput(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
# Useful for training neural nets
snake_case: Union[str, Any] = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ ) if drop_path > 0.0 else nn.Identity()
snake_case: Optional[Any] = config.use_layer_scale
if config.use_layer_scale:
snake_case: Any = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.use_layer_scale:
snake_case: str = self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Dict = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
snake_case: str = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = ()
snake_case: Dict = self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Union[str, Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
snake_case: Any = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = (output,) + outputs
return outputs
else:
snake_case: Optional[Any] = self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) ) )
# First residual connection
snake_case: Union[str, Any] = pooling_output + hidden_states
snake_case: List[Any] = ()
# Second residual connection inside the PoolFormerOutput block
snake_case: List[str] = self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: Dict = hidden_states + layer_output
snake_case: Optional[Any] = (output,) + outputs
return outputs
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = config
# stochastic depth decay rule
snake_case: List[Any] = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
snake_case: Union[str, Any] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
snake_case: List[Any] = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
# Transformer blocks
snake_case: str = []
snake_case: int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
snake_case: List[str] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
SCREAMING_SNAKE_CASE__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__ ) )
snake_case: Tuple = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True ):
'''simple docstring'''
snake_case: str = () if output_hidden_states else None
snake_case: Dict = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
snake_case , snake_case: Dict = layers
# Get patch embeddings from hidden_states
snake_case: int = embedding_layer(SCREAMING_SNAKE_CASE__ )
# Send the embeddings through the blocks
for _, blk in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = blk(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = layer_outputs[0]
if output_hidden_states:
snake_case: List[str] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = PoolFormerConfig
__UpperCamelCase = "poolformer"
__UpperCamelCase = "pixel_values"
__UpperCamelCase = True
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE__ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = value
__UpperCAmelCase = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
__UpperCAmelCase = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n"
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = config
snake_case: Tuple = PoolFormerEncoder(SCREAMING_SNAKE_CASE__ )
# Initialize weights and apply final processing
self.post_init()
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case: List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
snake_case: Optional[Any] = self.encoder(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: List[Any] = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Any = nn.Linear(config.hidden_size , config.hidden_size )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.dense(SCREAMING_SNAKE_CASE__ )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = config.num_labels
snake_case: str = PoolFormerModel(SCREAMING_SNAKE_CASE__ )
# Final norm
snake_case: int = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
snake_case: Dict = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
snake_case: Optional[Any] = self.poolformer(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: Any = outputs[0]
snake_case: str = self.classifier(self.norm(SCREAMING_SNAKE_CASE__ ).mean([-2, -1] ) )
snake_case: Any = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case: Tuple = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case: Dict = 'single_label_classification'
else:
snake_case: List[str] = 'multi_label_classification'
if self.config.problem_type == "regression":
snake_case: Union[str, Any] = MSELoss()
if self.num_labels == 1:
snake_case: List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case: int = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.config.problem_type == "single_label_classification":
snake_case: Union[str, Any] = CrossEntropyLoss()
snake_case: Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case: int = BCEWithLogitsLoss()
snake_case: Optional[int] = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not return_dict:
snake_case: str = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states ) | 692 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser(A_ )
snake_case: Any = parser.parse_args_into_dataclasses()[0]
snake_case: Tuple = TensorFlowBenchmark(args=A_ )
try:
snake_case: Optional[int] = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
snake_case: List[str] = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
snake_case: Union[str, Any] = ' '.join(str(A_ ).split(' ' )[:-1] )
snake_case: List[str] = ''
snake_case: Dict = eval(str(A_ ).split(' ' )[-1] )
snake_case: List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(A_ )
if len(A_ ) > 0:
snake_case: str = full_error_msg + begin_error_msg + str(A_ )
raise ValueError(A_ )
benchmark.run()
if __name__ == "__main__":
main() | 710 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __A : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case: Any = cst_fwd.get(__A , np.inf )
snake_case: int = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
snake_case: Union[str, Any] = new_cost_f
snake_case: Tuple = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
snake_case: List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[Any] = -1
snake_case: Any = set()
snake_case: str = set()
snake_case: int = {source: 0}
snake_case: Dict = {destination: 0}
snake_case: int = {source: None}
snake_case: Union[str, Any] = {destination: None}
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: Tuple = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
snake_case , snake_case: List[str] = queue_forward.get()
visited_forward.add(__A )
snake_case , snake_case: int = queue_backward.get()
visited_backward.add(__A )
snake_case: str = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
snake_case: Optional[Any] = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
snake_case: Any = shortest_distance
return shortest_path_distance
__UpperCAmelCase = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
__UpperCAmelCase = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''spiece.model'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__UpperCAmelCase = {
'''AI-Sweden/gpt-sw3-126m''': 2_048,
'''AI-Sweden/gpt-sw3-350m''': 2_048,
'''AI-Sweden/gpt-sw3-1.6b''': 2_048,
'''AI-Sweden/gpt-sw3-6.7b''': 2_048,
'''AI-Sweden/gpt-sw3-20b''': 2_048,
}
class SCREAMING_SNAKE_CASE ( snake_case__ ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Any = {} if sp_model_kwargs is None else sp_model_kwargs
snake_case: str = kwargs.get('name_or_path' )
if name_or_path is None:
logger.warning(
'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
' you are testing the model, this can safely be ignored' )
snake_case: Any = 'None'
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
snake_case: int = '<|endoftext|>' if eos_token is None else eos_token
snake_case: Any = '<unk>' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
snake_case: Optional[int] = unk_token if pad_token is None else pad_token
snake_case: List[Any] = eos_token if bos_token is None else bos_token
else:
snake_case: List[str] = '<pad>' if pad_token is None else pad_token
snake_case: Optional[int] = '<s>' if bos_token is None else bos_token
super().__init__(
do_lower_case=_A , remove_space=_A , keep_accents=_A , bos_token=_A , eos_token=_A , unk_token=_A , pad_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
snake_case: Union[str, Any] = do_lower_case
snake_case: int = remove_space
snake_case: Any = keep_accents
snake_case: str = vocab_file
snake_case: Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_A )
# Used for whitespace normalization in input texts
# fmt : off
snake_case: str = {' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '', ''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
snake_case: Union[str, Any] = re.compile(
F"""[{''.join(map(_A , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]""" )
def __getstate__( self ):
'''simple docstring'''
snake_case: List[str] = self.__dict__.copy()
snake_case: Tuple = None
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case: List[Any] = {}
snake_case: List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _UpperCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = self.non_printing_characters_re.sub('' , _A )
# Normalize whitespaces
snake_case: str = ''.join([char if char not in self.whitespaces else ' ' for char in text] )
# NFC Unicode normalization
snake_case: Optional[Any] = unicodedata.normalize('NFC' , _A )
return text
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = self.preprocess_text(_A )
return self.sp_model.encode(_A , out_type=_A )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.PieceToId(_A )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.IdToPiece(_A )
@staticmethod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return out_string
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = []
snake_case: Optional[int] = ''
snake_case: List[str] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_A ) + token
snake_case: Tuple = True
snake_case: int = []
else:
current_sub_tokens.append(_A )
snake_case: List[str] = False
out_string += self.sp_model.decode(_A )
return out_string
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case: List[str] = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , 'wb' ) as fi:
snake_case: Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if isinstance(_A , _A ):
snake_case: Union[str, Any] = self.preprocess_text(_A )
snake_case: Optional[Any] = self.sp_model.encode(_A )
else:
snake_case: Any = [self.preprocess_text(_A ) for t in text]
snake_case: str = self.sp_model.encode(_A )
if return_tensors is True or return_tensors == "pt":
snake_case: Dict = torch.tensor(_A )
return token_ids
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.decode(_A )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
snake_case: Any = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(_A ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=_A ) | 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "▁"
__UpperCAmelCase = {"vocab_file": "sentencepiece.bpe.model"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__UpperCAmelCase = {
"facebook/xglm-564M": 2_048,
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
snake_case: Optional[Any] = 7
snake_case: List[str] = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
snake_case: str = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
snake_case: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
snake_case: int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case: Tuple = 1
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case: Optional[Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
snake_case: Union[str, Any] = len(self.sp_model )
snake_case: str = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
snake_case: List[Any] = self.__dict__.copy()
snake_case: Union[str, Any] = None
snake_case: Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case: Union[str, Any] = {}
snake_case: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
snake_case: Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: int = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case: Dict = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = ''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ' ' ).strip()
return out_string
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case: List[str] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as fi:
snake_case: int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,) | 692 | 0 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
__UpperCAmelCase = NewType("DataClass", Any)
__UpperCAmelCase = NewType("DataClassType", Any)
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
if isinstance(__snake_case , __snake_case ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: int = {str(__snake_case ): choice for choice in choices}
return lambda __A : str_to_choice.get(__snake_case , __snake_case )
def lowerCAmelCase_ ( *,
__A : List[Any] = None , __A : Optional[int] = None , __A : Optional[int] = dataclasses.MISSING , __A : Dict = dataclasses.MISSING , __A : Optional[Any] = None , **__A : Dict , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
snake_case: Optional[int] = {}
if aliases is not None:
snake_case: int = aliases
if help is not None:
snake_case: List[Any] = help
return dataclasses.field(metadata=__snake_case , default=__snake_case , default_factory=__snake_case , **__snake_case )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = 42
def __init__( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if "formatter_class" not in kwargs:
snake_case: List[str] = ArgumentDefaultsHelpFormatter
super().__init__(**__a )
if dataclasses.is_dataclass(__a ):
snake_case: Dict = [dataclass_types]
snake_case: Tuple = list(__a )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__a )
@staticmethod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = F"""--{field.name}"""
snake_case: int = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __a ):
raise RuntimeError(
'Unresolved type detected, which should have been done with the help of '
'`typing.get_type_hints` method by default' )
snake_case: Optional[Any] = kwargs.pop('aliases' , [] )
if isinstance(__a , __a ):
snake_case: List[Any] = [aliases]
snake_case: List[Any] = getattr(field.type , '__origin__' , field.type )
if origin_type is Union or (hasattr(__a , 'UnionType' ) and isinstance(__a , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__a ) not in field.type.__args__
):
raise ValueError(
'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
' the argument parser only supports one type per argument.'
F""" Problem encountered in field \'{field.name}\'.""" )
if type(__a ) not in field.type.__args__:
# filter `str` in Union
snake_case: Optional[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
snake_case: Dict = getattr(field.type , '__origin__' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
snake_case: Tuple = (
field.type.__args__[0] if isinstance(__a , field.type.__args__[1] ) else field.type.__args__[1]
)
snake_case: Union[str, Any] = getattr(field.type , '__origin__' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
snake_case: Tuple = {}
if origin_type is Literal or (isinstance(field.type , __a ) and issubclass(field.type , __a )):
if origin_type is Literal:
snake_case: Union[str, Any] = field.type.__args__
else:
snake_case: List[str] = [x.value for x in field.type]
snake_case: Optional[Any] = make_choice_type_function(kwargs['choices'] )
if field.default is not dataclasses.MISSING:
snake_case: Optional[int] = field.default
else:
snake_case: Any = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
snake_case: List[str] = copy(__a )
# Hack because type=bool in argparse does not behave as we want.
snake_case: List[str] = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
snake_case: List[Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
snake_case: str = default
# This tells argparse we accept 0 or 1 value after --field_name
snake_case: str = '?'
# This is the value that will get picked if we do --field_name (without value)
snake_case: List[str] = True
elif isclass(__a ) and issubclass(__a , __a ):
snake_case: Optional[Any] = field.type.__args__[0]
snake_case: Tuple = '+'
if field.default_factory is not dataclasses.MISSING:
snake_case: int = field.default_factory()
elif field.default is dataclasses.MISSING:
snake_case: Optional[int] = True
else:
snake_case: List[str] = field.type
if field.default is not dataclasses.MISSING:
snake_case: Optional[int] = field.default
elif field.default_factory is not dataclasses.MISSING:
snake_case: Optional[Any] = field.default_factory()
else:
snake_case: str = True
parser.add_argument(__a , *__a , **__a )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
snake_case: List[str] = False
parser.add_argument(F"""--no_{field.name}""" , action='store_false' , dest=field.name , **__a )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if hasattr(__a , '_argument_group_name' ):
snake_case: Dict = self.add_argument_group(dtype._argument_group_name )
else:
snake_case: Tuple = self
try:
snake_case: Optional[int] = get_type_hints(__a )
except NameError:
raise RuntimeError(
F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'removing line of `from __future__ import annotations` which opts in Postponed '
'Evaluation of Annotations (PEP 563)' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__a ):
snake_case: int = '.'.join(map(__a , sys.version_info[:3] ) )
raise RuntimeError(
F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'line of `from __future__ import annotations` which opts in union types as '
'`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
'support Python versions that lower than 3.10, you need to use '
'`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
'`X | None`.' ) from ex
raise
for field in dataclasses.fields(__a ):
if not field.init:
continue
snake_case: List[str] = type_hints[field.name]
self._parse_dataclass_field(__a , __a )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , ):
'''simple docstring'''
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
snake_case: str = []
if args_filename:
args_files.append(Path(__a ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('.args' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
snake_case: List[Any] = ArgumentParser()
args_file_parser.add_argument(__a , type=__a , action='append' )
# Use only remaining args for further parsing (remove the args_file_flag)
snake_case , snake_case: List[Any] = args_file_parser.parse_known_args(args=__a )
snake_case: str = vars(__a ).get(args_file_flag.lstrip('-' ) , __a )
if cmd_args_file_paths:
args_files.extend([Path(__a ) for p in cmd_args_file_paths] )
snake_case: str = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
snake_case: Tuple = file_args + args if args is not None else file_args + sys.argv[1:]
snake_case , snake_case: Union[str, Any] = self.parse_known_args(args=__a )
snake_case: Tuple = []
for dtype in self.dataclass_types:
snake_case: Optional[int] = {f.name for f in dataclasses.fields(__a ) if f.init}
snake_case: Optional[int] = {k: v for k, v in vars(__a ).items() if k in keys}
for k in keys:
delattr(__a , __a )
snake_case: List[str] = dtype(**__a )
outputs.append(__a )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__a )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
snake_case: List[str] = set(args.keys() )
snake_case: Any = []
for dtype in self.dataclass_types:
snake_case: Any = {f.name for f in dataclasses.fields(__a ) if f.init}
snake_case: List[str] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
snake_case: Optional[int] = dtype(**__a )
outputs.append(__a )
if not allow_extra_keys and unused_keys:
raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(__a )}""" )
return tuple(__a )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
with open(Path(__a ) , encoding='utf-8' ) as open_json_file:
snake_case: List[Any] = json.loads(open_json_file.read() )
snake_case: int = self.parse_dict(__a , allow_extra_keys=__a )
return tuple(__a )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
snake_case: Optional[int] = self.parse_dict(yaml.safe_load(Path(__a ).read_text() ) , allow_extra_keys=__a )
return tuple(__a ) | 712 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
return getitem, k
def lowerCAmelCase_ ( __A : Any , __A : Optional[int] ):
'''simple docstring'''
return setitem, k, v
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
return delitem, k
def lowerCAmelCase_ ( __A : str , __A : int , *__A : Tuple ):
'''simple docstring'''
try:
return fun(__A , *__A ), None
except Exception as e:
return None, e
__UpperCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__UpperCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
snake_case: List[Any] = HashMap(initial_block_size=4 )
snake_case: List[Any] = {}
for _, (fun, *args) in enumerate(__A ):
snake_case , snake_case: Optional[int] = _run_operation(__A , __A , *__A )
snake_case , snake_case: str = _run_operation(__A , __A , *__A )
assert my_res == py_res
assert str(__A ) == str(__A )
assert set(__A ) == set(__A )
assert len(__A ) == len(__A )
assert set(my.items() ) == set(py.items() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
def is_public(__A : str ) -> bool:
return not name.startswith('_' )
snake_case: Dict = {name for name in dir({} ) if is_public(__A )}
snake_case: List[str] = {name for name in dir(HashMap() ) if is_public(__A )}
assert dict_public_names > hash_public_names | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( __A : Optional[int] , __A : Tuple , __A : Dict , __A : Union[str, Any] ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
snake_case: Tuple = array[indexa], array[indexa]
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Any , __A : List[str] , __A : Optional[Any] ):
'''simple docstring'''
if length > 1:
snake_case: Dict = int(length / 2 )
for i in range(lowerCamelCase_ , low + middle ):
comp_and_swap(lowerCamelCase_ , lowerCamelCase_ , i + middle , lowerCamelCase_ )
bitonic_merge(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
bitonic_merge(lowerCamelCase_ , low + middle , lowerCamelCase_ , lowerCamelCase_ )
def lowerCAmelCase_ ( __A : Optional[Any] , __A : int , __A : Any , __A : Union[str, Any] ):
'''simple docstring'''
if length > 1:
snake_case: int = int(length / 2 )
bitonic_sort(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , 1 )
bitonic_sort(lowerCamelCase_ , low + middle , lowerCamelCase_ , 0 )
bitonic_merge(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
__UpperCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__UpperCAmelCase = [int(item.strip()) for item in user_input.split(",")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("\nSorted array in ascending order is: ", end="")
print(*unsorted, sep=", ")
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("Sorted array in descending order is: ", end="")
print(*unsorted, sep=", ") | 713 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: List[str] = getattr(__A , __A )
if weight_type is not None:
snake_case: Optional[int] = getattr(__A , __A ).shape
else:
snake_case: Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Optional[int] = value
elif weight_type == "weight_g":
snake_case: List[str] = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: Optional[Any] = value
else:
snake_case: int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: List[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case: Dict = None
for name, value in fairseq_dict.items():
snake_case: Tuple = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
elif name.split('.' )[0] == "proj":
snake_case: List[Any] = fairseq_model.proj
snake_case: int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: int = True
if "*" in mapped_key:
snake_case: List[str] = name.split(__A )[0].split('.' )[-2]
snake_case: Dict = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Tuple = 'weight_g'
elif "weight_v" in name:
snake_case: int = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
snake_case: List[Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCAmelCase_ ( __A : List[str] , __A : List[Any] , __A : int , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: Tuple = name.split('.' )
snake_case: Any = int(items[0] )
snake_case: Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case , snake_case: List[Any] = emb.weight.shape
snake_case: Optional[int] = nn.Linear(__A , __A , bias=__A )
snake_case: Any = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
with open(__A , 'r' , encoding='utf-8' ) as f:
snake_case: List[Any] = f.readlines()
snake_case: Any = [line.split(' ' )[0] for line in lines]
snake_case: int = len(__A )
snake_case: Dict = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(__A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : int , __A : str , ):
'''simple docstring'''
snake_case: Union[str, Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: str = SpeechaTextaConfig.from_pretrained(
__A , vocab_size=__A , decoder_layers=__A , do_stable_layer_norm=__A )
snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
snake_case: List[Any] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case: Optional[Any] = WavaVecaModel(__A )
snake_case: Any = recursively_load_weights_wavaveca(model.encoder , __A )
snake_case: Union[str, Any] = SpeechaTextaForCausalLM(__A )
snake_case , snake_case: Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__A )
# set output linear layer
unexpected_keys.remove('embed_out' )
snake_case: str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
snake_case: int = SpeechEncoderDecoderModel(encoder=__A , decoder=__A )
snake_case: List[Any] = False
# add projection layer
snake_case: Union[str, Any] = nn.Parameter(projection_layer.weight )
snake_case: Union[str, Any] = nn.Parameter(projection_layer.bias )
snake_case: List[Any] = create_vocab_dict(__A )
with open(os.path.join(__A , 'vocab.json' ) , 'w' ) as fp:
json.dump(__A , __A )
snake_case: Union[str, Any] = SpeechaTextaTokenizer(os.path.join(__A , 'vocab.json' ) )
tokenizer.save_pretrained(__A )
snake_case: Tuple = hf_wavavec.config.to_dict()
snake_case: int = tokenizer.pad_token_id
snake_case: Dict = tokenizer.bos_token_id
snake_case: Optional[int] = tokenizer.eos_token_id
snake_case: Dict = 'speech_to_text_2'
snake_case: Optional[Any] = 'wav2vec2'
snake_case: Tuple = SpeechEncoderDecoderConfig.from_dict(__A )
hf_wavavec.save_pretrained(__A )
feature_extractor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
) | 692 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"adapter_layer": "encoder.layers.*.adapter_layer",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
snake_case: int = {}
with open(A__ , 'r' ) as file:
for line_number, line in enumerate(A__ ):
snake_case: Union[str, Any] = line.strip()
if line:
snake_case: List[str] = line.split()
snake_case: Tuple = line_number
snake_case: Tuple = words[0]
snake_case: Optional[Any] = value
return result
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Tuple , __A : str , __A : Union[str, Any] , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: Tuple = getattr(A__ , A__ )
snake_case: str = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
snake_case: int = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case: List[str] = 'param'
if weight_type is not None and weight_type != "param":
snake_case: str = getattr(A__ , A__ ).shape
elif weight_type is not None and weight_type == "param":
snake_case: Any = hf_pointer
for attribute in hf_param_name.split('.' ):
snake_case: List[Any] = getattr(A__ , A__ )
snake_case: Any = shape_pointer.shape
# let's reduce dimension
snake_case: Dict = value[0]
else:
snake_case: Union[str, Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
snake_case: Tuple = value
elif weight_type == "weight_g":
snake_case: Any = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: str = value
elif weight_type == "param":
for attribute in hf_param_name.split('.' ):
snake_case: int = getattr(A__ , A__ )
snake_case: Optional[int] = value
else:
snake_case: Tuple = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[str] , __A : Union[str, Any] , __A : str , __A : List[Any] , __A : int ):
'''simple docstring'''
snake_case: Dict = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
snake_case: int = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case: List[Any] = 'param'
if weight_type is not None and weight_type != "param":
snake_case: Dict = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
snake_case: Dict = '.'.join([key, hf_param_name] )
else:
snake_case: Any = key
snake_case: Any = value if 'lm_head' in full_key else value[0]
__UpperCAmelCase = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def lowerCAmelCase_ ( __A : Any , __A : List[Any] , __A : Any=None , __A : Union[str, Any]=None ):
'''simple docstring'''
snake_case: Optional[int] = False
for key, mapped_key in MAPPING.items():
snake_case: Tuple = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: Optional[Any] = True
if "*" in mapped_key:
snake_case: List[Any] = name.split(A__ )[0].split('.' )[-2]
snake_case: int = mapped_key.replace('*' , A__ )
if "weight_g" in name:
snake_case: Union[str, Any] = 'weight_g'
elif "weight_v" in name:
snake_case: List[Any] = 'weight_v'
elif "bias" in name:
snake_case: Any = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case: List[Any] = 'weight'
else:
snake_case: Union[str, Any] = None
if hf_dict is not None:
rename_dict(A__ , A__ , A__ , A__ , A__ )
else:
set_recursively(A__ , A__ , A__ , A__ , A__ )
return is_used
return is_used
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Tuple , __A : int ):
'''simple docstring'''
snake_case: str = []
snake_case: Optional[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
snake_case: List[str] = False
if "conv_layers" in name:
load_conv_layer(
A__ , A__ , A__ , A__ , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
else:
snake_case: Union[str, Any] = load_wavaveca_layer(A__ , A__ , A__ )
if not is_used:
unused_weights.append(A__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCAmelCase_ ( __A : int , __A : int , __A : str , __A : int , __A : Union[str, Any] ):
'''simple docstring'''
snake_case: Dict = full_name.split('conv_layers.' )[-1]
snake_case: str = name.split('.' )
snake_case: Union[str, Any] = int(items[0] )
snake_case: int = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
snake_case: Optional[Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
snake_case: str = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case: List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case: List[str] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A__ )
@torch.no_grad()
def lowerCAmelCase_ ( __A : List[Any] , __A : Optional[int] , __A : Dict=None , __A : Tuple=None , __A : List[str]=True , __A : Optional[int]=False ):
'''simple docstring'''
if config_path is not None:
snake_case: Dict = WavaVecaConfig.from_pretrained(A__ )
else:
snake_case: List[str] = WavaVecaConfig()
if is_seq_class:
snake_case: Any = read_txt_into_dict(A__ )
snake_case: Tuple = idalabel
snake_case: str = WavaVecaForSequenceClassification(A__ )
snake_case: Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
feature_extractor.save_pretrained(A__ )
elif is_finetuned:
if dict_path:
snake_case: List[Any] = Dictionary.load(A__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case: List[Any] = target_dict.pad_index
snake_case: Optional[Any] = target_dict.bos_index
snake_case: Tuple = target_dict.eos_index
snake_case: int = len(target_dict.symbols )
snake_case: List[str] = os.path.join(A__ , 'vocab.json' )
if not os.path.isdir(A__ ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A__ ) )
return
os.makedirs(A__ , exist_ok=A__ )
snake_case: List[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case: Optional[Any] = 0
snake_case: Union[str, Any] = 1
with open(A__ , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A__ , A__ )
snake_case: Any = WavaVecaCTCTokenizer(
A__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=A__ , )
snake_case: Union[str, Any] = True if config.feat_extract_norm == 'layer' else False
snake_case: Tuple = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
snake_case: List[Any] = WavaVecaProcessor(feature_extractor=A__ , tokenizer=A__ )
processor.save_pretrained(A__ )
snake_case: int = WavaVecaForCTC(A__ )
else:
snake_case: Dict = WavaVecaForPreTraining(A__ )
if is_finetuned or is_seq_class:
snake_case , snake_case , snake_case: List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case: Union[str, Any] = argparse.Namespace(task='audio_pretraining' )
snake_case: int = fairseq.tasks.setup_task(A__ )
snake_case , snake_case , snake_case: Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A__ )
snake_case: List[str] = model[0].eval()
recursively_load_weights(A__ , A__ , not is_finetuned )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
) | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }') | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__UpperCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
snake_case: List[Any] = k.replace(__A , __A )
return k
def lowerCAmelCase_ ( __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[int] = BigBirdPegasusConfig(**__A )
snake_case: List[Any] = BigBirdPegasusForConditionalGeneration(__A )
snake_case: Any = torch_model.state_dict()
snake_case: Any = {}
# separating decoder weights
snake_case: Optional[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
snake_case: Any = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
snake_case: List[str] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Any = DECODER_PATTERNS
snake_case: int = rename_state_dict_key(__A , __A )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: Optional[Any] = v.T
snake_case: Any = torch.from_numpy(__A )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
snake_case: List[Any] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Union[str, Any] = REMAINING_PATTERNS
snake_case: str = rename_state_dict_key(__A , __A )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: int = v.T
snake_case: Any = torch.from_numpy(__A )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
snake_case: str = mapping['model.embed_positions.weight']
snake_case: Any = mapping.pop('model.embed_positions.weight' )
snake_case , snake_case: Union[str, Any] = torch_model.load_state_dict(__A , strict=__A )
snake_case: Optional[int] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Tuple = tf.train.list_variables(__A )
snake_case: str = {}
snake_case: List[str] = ['global_step']
for name, shape in tqdm(__A , desc='converting tf checkpoint to dict' ):
snake_case: str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case: Any = tf.train.load_variable(__A , __A )
snake_case: Optional[int] = array
return tf_weights
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict ):
'''simple docstring'''
snake_case: int = get_tf_weights_as_numpy(__A )
snake_case: int = convert_bigbird_pegasus(__A , __A )
torch_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 692 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : Optional[int] , __A : Optional[int]=False ):
'''simple docstring'''
snake_case: Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case: List[Any] = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def lowerCAmelCase_ ( __A : str , __A : int , __A : List[Any]=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
snake_case: List[str] = ''
else:
snake_case: Tuple = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case: str = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
snake_case: int = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case: Any = in_proj_weight[
: config.hidden_size, :
]
snake_case: Optional[int] = in_proj_bias[: config.hidden_size]
snake_case: Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case: Union[str, Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case: Tuple = in_proj_weight[
-config.hidden_size :, :
]
snake_case: Optional[Any] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: Dict = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(a__ , a__ )
def lowerCAmelCase_ ( __A : Optional[Any] , __A : str , __A : List[str] ):
'''simple docstring'''
snake_case: Tuple = dct.pop(a__ )
snake_case: List[str] = val
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Optional[int] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case: Dict = Image.open(requests.get(a__ , stream=a__ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __A : Any , __A : Dict , __A : List[Any]=True ):
'''simple docstring'''
snake_case: List[Any] = ViTConfig()
# patch_size
if model_name[-1] == "8":
snake_case: Any = 8
# set labels if required
if not base_model:
snake_case: str = 10_00
snake_case: Any = 'huggingface/label-files'
snake_case: Any = 'imagenet-1k-id2label.json'
snake_case: Optional[int] = json.load(open(hf_hub_download(a__ , a__ , repo_type='dataset' ) , 'r' ) )
snake_case: List[Any] = {int(a__ ): v for k, v in idalabel.items()}
snake_case: Optional[Any] = idalabel
snake_case: Optional[Any] = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
snake_case: Any = 3_84
snake_case: Union[str, Any] = 15_36
snake_case: str = 12
snake_case: Any = 6
# load original model from torch hub
snake_case: str = torch.hub.load('facebookresearch/dino:main' , a__ )
original_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case: Any = original_model.state_dict()
if base_model:
remove_classification_head_(a__ )
snake_case: Optional[Any] = create_rename_keys(a__ , base_model=a__ )
for src, dest in rename_keys:
rename_key(a__ , a__ , a__ )
read_in_q_k_v(a__ , a__ , a__ )
# load HuggingFace model
if base_model:
snake_case: Dict = ViTModel(a__ , add_pooling_layer=a__ ).eval()
else:
snake_case: Dict = ViTForImageClassification(a__ ).eval()
model.load_state_dict(a__ )
# Check outputs on an image, prepared by ViTImageProcessor
snake_case: List[Any] = ViTImageProcessor()
snake_case: Tuple = image_processor(images=prepare_img() , return_tensors='pt' )
snake_case: Union[str, Any] = encoding['pixel_values']
snake_case: Union[str, Any] = model(a__ )
if base_model:
snake_case: Any = original_model(a__ )
assert torch.allclose(a__ , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
snake_case: Tuple = original_model(a__ )
assert logits.shape == outputs.logits.shape
assert torch.allclose(a__ , outputs.logits , atol=1E-3 )
Path(a__ ).mkdir(exist_ok=a__ )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(a__ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
__UpperCAmelCase = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model) | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case: int = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case: Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__UpperCAmelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 692 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["EncoderDecoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["TFEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["FlaxEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = tempfile.mkdtemp()
snake_case: Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
snake_case: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
snake_case: Optional[int] = {
'do_resize': True,
'size': {'height': 2_24, 'width': 2_24},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'do_convert_rgb': True,
}
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
snake_case: Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer()
snake_case: Union[str, Any] = self.get_image_processor()
snake_case: List[str] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
snake_case: List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
snake_case: Any = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
snake_case: Union[str, Any] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_image_processor()
snake_case: Tuple = self.get_tokenizer()
snake_case: Optional[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.prepare_image_inputs()
snake_case: List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
snake_case: Dict = processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_image_processor()
snake_case: Optional[int] = self.get_tokenizer()
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Union[str, Any] = processor(text=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Tuple = self.prepare_image_inputs()
snake_case: Any = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.get_image_processor()
snake_case: str = self.get_tokenizer()
snake_case: Union[str, Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case: int = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = 'Alexandra，T-shirt的价格是15便士。'
snake_case: List[Any] = self.prepare_image_inputs()
snake_case: Dict = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 692 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")
__UpperCAmelCase = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
with open(__A , 'rb' ) as f:
snake_case: List[str] = Image.open(__A )
return im.convert('RGB' )
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={
"help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A folder containing the training data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A folder containing the validation data."} )
__UpperCamelCase = field(
default=0.15 , metadata={"help": "Percent to split off of train for validation."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'You must specify either a dataset name from the hub or a train and/or validation directory.' )
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="google/vit-base-patch16-224-in21k" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(snake_case )} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(default=snake_case , metadata={"help": "Name or path of preprocessor config."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
snake_case: str = torch.stack([example['pixel_values'] for example in examples] )
snake_case: List[Any] = torch.tensor([example['labels'] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case: str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case: Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_image_classification' , __A , __A )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case: Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
snake_case: Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task='image-classification' , use_auth_token=True if model_args.use_auth_token else None , )
else:
snake_case: Optional[Any] = {}
if data_args.train_dir is not None:
snake_case: str = os.path.join(data_args.train_dir , '**' )
if data_args.validation_dir is not None:
snake_case: Dict = os.path.join(data_args.validation_dir , '**' )
snake_case: Tuple = load_dataset(
'imagefolder' , data_files=__A , cache_dir=model_args.cache_dir , task='image-classification' , )
# If we don't have a validation split, split off a percentage of train as validation.
snake_case: List[Any] = None if '''validation''' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __A ) and data_args.train_val_split > 0.0:
snake_case: List[str] = dataset['''train'''].train_test_split(data_args.train_val_split )
snake_case: List[Any] = split['''train''']
snake_case: List[str] = split['''test''']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
snake_case: List[str] = dataset['''train'''].features['''labels'''].names
snake_case: Dict = {}, {}
for i, label in enumerate(__A ):
snake_case: Dict = str(__A )
snake_case: Tuple = label
# Load the accuracy metric from the datasets package
snake_case: Optional[int] = evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : Any ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
snake_case: Any = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__A ) , labelaid=__A , idalabel=__A , finetuning_task='image-classification' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case: Tuple = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
snake_case: str = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
snake_case: Any = image_processor.size['''shortest_edge''']
else:
snake_case: Tuple = (image_processor.size['''height'''], image_processor.size['''width'''])
snake_case: Union[str, Any] = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
snake_case: List[Any] = Compose(
[
RandomResizedCrop(__A ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
snake_case: Optional[Any] = Compose(
[
Resize(__A ),
CenterCrop(__A ),
ToTensor(),
normalize,
] )
def train_transforms(__A : Union[str, Any] ):
snake_case: Optional[int] = [
_train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['''image''']
]
return example_batch
def val_transforms(__A : Any ):
snake_case: Union[str, Any] = [_val_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['''image''']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
snake_case: Optional[Any] = (
dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(__A )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
snake_case: Dict = (
dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(__A )
# Initalize our trainer
snake_case: Dict = Trainer(
model=__A , args=__A , train_dataset=dataset['train'] if training_args.do_train else None , eval_dataset=dataset['validation'] if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
snake_case: Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: List[Any] = last_checkpoint
snake_case: Dict = trainer.train(resume_from_checkpoint=__A )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case: List[Any] = trainer.evaluate()
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
# Write model card and (optionally) push to hub
snake_case: List[str] = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''image-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''image-classification''', '''vision'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
if __name__ == "__main__":
main() | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "swinv2"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: int = image_size
snake_case: Union[str, Any] = patch_size
snake_case: List[str] = num_channels
snake_case: Tuple = embed_dim
snake_case: str = depths
snake_case: Any = len(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = num_heads
snake_case: Optional[int] = window_size
snake_case: Any = mlp_ratio
snake_case: Optional[int] = qkv_bias
snake_case: Union[str, Any] = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: Dict = drop_path_rate
snake_case: List[str] = hidden_act
snake_case: int = use_absolute_embeddings
snake_case: Any = layer_norm_eps
snake_case: Dict = initializer_range
snake_case: List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Tuple = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
snake_case: Union[str, Any] = (0, 0, 0, 0) | 692 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(__A ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(__A ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = "src/transformers"
__UpperCAmelCase = "docs/source/en/tasks"
def lowerCAmelCase_ ( __A : Dict , __A : List[str] , __A : Optional[int] ):
'''simple docstring'''
with open(__a , 'r' , encoding='utf-8' , newline='\n' ) as f:
snake_case: int = f.readlines()
# Find the start prompt.
snake_case: Any = 0
while not lines[start_index].startswith(__a ):
start_index += 1
start_index += 1
snake_case: Optional[int] = start_index
while not lines[end_index].startswith(__a ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
"asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
"summarization.md": ("nllb",),
"translation.md": ("nllb",),
}
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
snake_case: Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
snake_case: List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__a , set() )
snake_case: List[str] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : Union[str, Any]=False ):
'''simple docstring'''
snake_case , snake_case , snake_case , snake_case: str = _find_text_in_file(
filename=os.path.join(__a , __a ) , start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->' , end_prompt='<!--End of the generated tip-->' , )
snake_case: Dict = get_model_list_for_task(__a )
if current_list != new_list:
if overwrite:
with open(os.path.join(__a , __a ) , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
' to fix this.' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite) | 720 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = question_encoder
snake_case: Union[str, Any] = generator
snake_case: Optional[int] = self.question_encoder
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case: int = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
snake_case: str = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
snake_case: Dict = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.question_encoder
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.generator
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "longest" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
snake_case: Optional[Any] = self.current_tokenizer.model_max_length
snake_case: int = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case: Any = self.current_tokenizer.model_max_length
snake_case: List[str] = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: Dict = labels['input_ids']
return model_inputs | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""ConvNextFeatureExtractor"""]
__UpperCAmelCase = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure) | 721 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'mock-s3-bucket'
snake_case: int = f"""s3://{mock_bucket}"""
snake_case: Any = extract_path_from_uri(__A )
assert dataset_path.startswith('s3://' ) is False
snake_case: Union[str, Any] = './local/path'
snake_case: Union[str, Any] = extract_path_from_uri(__A )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = is_remote_filesystem(__A )
assert is_remote is True
snake_case: int = fsspec.filesystem('file' )
snake_case: int = is_remote_filesystem(__A )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , __A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : str , __A : Optional[Any] , __A : List[str] , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
snake_case: Optional[int] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case: str = f"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=__A )
assert isinstance(__A , __A )
snake_case: Any = os.path.basename(__A )
snake_case: int = expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(__A , 'r' , encoding='utf-8' ) as f, open(__A , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def lowerCAmelCase_ ( __A : Any , __A : int , __A : int ):
'''simple docstring'''
snake_case: List[str] = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
snake_case: str = compressed_file_paths[protocol]
snake_case: Dict = 'dataset.jsonl'
snake_case: Optional[Any] = f"""{protocol}://{member_file_path}::{compressed_file_path}"""
snake_case , *snake_case: List[Any] = fsspec.get_fs_token_paths(__A )
assert fs.isfile(__A )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : List[Any] ):
'''simple docstring'''
snake_case: Tuple = hf_api.dataset_info(__A , token=__A )
snake_case: List[str] = HfFileSystem(repo_info=__A , token=__A )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(__A ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__A , __A , clobber=__A )
with pytest.warns(__A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__A ) == 1
assert (
str(warning_info[0].message )
== f"""A filesystem protocol was already set for {protocol} and will be overwritten."""
) | 692 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"GraphormerForGraphClassification",
"GraphormerModel",
"GraphormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 700 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__UpperCamelCase = field(
default=1024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the training data."} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A csv or a json file containing the test data."} )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
snake_case: str = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case: Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case: Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case: str = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
snake_case: Tuple = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case: Optional[int] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case: Tuple = data_args.train_file.split('.' )[-1]
snake_case: Union[str, Any] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case: Union[str, Any] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
snake_case: List[Any] = load_dataset('csv' , data_files=__A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case: Optional[Any] = load_dataset('json' , data_files=__A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case: Tuple = raw_datasets['train'].features['label'].names
snake_case: List[str] = len(__A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case: List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
snake_case: Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case: int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case: Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case: Optional[Any] = {'Refused': 0, 'Entailed': 1}
snake_case: List[Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case: List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__A : Any ):
# Tokenize the texts
def _convert_table_text_to_pandas(__A : Dict ):
snake_case: str = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
snake_case: List[str] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case: str = examples['statement']
snake_case: int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
snake_case: List[Any] = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A )
snake_case: List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
snake_case: int = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case: List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case: Tuple = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case: Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case: Optional[int] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
snake_case: str = raw_datasets['test']
if data_args.max_predict_samples is not None:
snake_case: List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
snake_case: int = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
snake_case: List[str] = np.argmax(__A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case: str = default_data_collator
elif training_args.fpaa:
snake_case: List[str] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
snake_case: List[Any] = None
# Initialize our Trainer
snake_case: List[str] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
snake_case: str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: Optional[Any] = last_checkpoint
snake_case: Union[str, Any] = trainer.train(resume_from_checkpoint=__A )
snake_case: List[Any] = train_result.metrics
snake_case: List[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
snake_case: Optional[Any] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , __A )
trainer.save_metrics('train' , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case: Dict = trainer.evaluate(eval_dataset=__A )
snake_case: Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
snake_case: Dict = min(__A , len(__A ) )
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case: Optional[int] = predict_dataset.remove_columns('label' )
snake_case: str = trainer.predict(__A , metric_key_prefix='predict' ).predictions
snake_case: Any = np.argmax(__A , axis=1 )
snake_case: int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(__A , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(__A ):
snake_case: int = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
snake_case: Optional[int] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 692 | 0 |
'''simple docstring'''
from typing import Any
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
if not input_list:
return []
snake_case: str = [input_list.count(_lowerCAmelCase ) for value in input_list]
snake_case: Any = max(_lowerCAmelCase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 701 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : float = 0.1 ):
'''simple docstring'''
snake_case: Optional[int] = 3
snake_case: int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__A )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 702 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] ) | 692 | 0 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _UpperCamelCase ( *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: Any = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[str] = DepthEstimationPipeline(model=_lowercase , image_processor=_lowercase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )} , _lowercase )
import datasets
snake_case: Union[str, Any] = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
snake_case: int = depth_estimator(
[
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
] )
self.assertEqual(
[
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
{'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
] , _lowercase , )
@require_tf
@unittest.skip('Depth estimation is not implemented in TF' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@slow
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = 'Intel/dpt-large'
snake_case: Union[str, Any] = pipeline('depth-estimation' , model=_lowercase )
snake_case: str = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
snake_case: List[Any] = hashimage(outputs['depth'] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() ) , 29.3_04 )
self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() ) , 2.6_62 )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' ) | 703 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "layer_norm" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = only_cross_attention
snake_case: Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
snake_case: Tuple = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case: List[str] = AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case: str = AdaLayerNormZero(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=SCREAMING_SNAKE_CASE__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case: Tuple = (
AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , upcast_attention=SCREAMING_SNAKE_CASE__ , ) # is self-attn if encoder_hidden_states is none
else:
snake_case: int = None
snake_case: Tuple = None
# 3. Feed-forward
snake_case: Union[str, Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = FeedForward(SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , final_dropout=SCREAMING_SNAKE_CASE__ )
# let chunk size default to None
snake_case: Any = None
snake_case: Any = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = chunk_size
snake_case: str = dim
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
if self.use_ada_layer_norm:
snake_case: Optional[int] = self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
snake_case , snake_case , snake_case , snake_case , snake_case: int = self.norma(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=hidden_states.dtype )
else:
snake_case: List[str] = self.norma(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case: List[str] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if self.use_ada_layer_norm_zero:
snake_case: Tuple = gate_msa.unsqueeze(1 ) * attn_output
snake_case: List[str] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case: Dict = (
self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__ )
)
snake_case: Any = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: List[str] = attn_output + hidden_states
# 3. Feed-forward
snake_case: str = self.norma(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: str = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case: List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case: Optional[Any] = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE__ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case: int = self.ff(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
snake_case: Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
snake_case: Tuple = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 4 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = False , ):
'''simple docstring'''
super().__init__()
snake_case: int = int(dim * mult )
snake_case: Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case: int = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if activation_fn == "gelu-approximate":
snake_case: Optional[Any] = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , approximate='tanh' )
elif activation_fn == "geglu":
snake_case: List[Any] = GEGLU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif activation_fn == "geglu-approximate":
snake_case: Optional[int] = ApproximateGELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE__ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for module in self.net:
snake_case: Optional[int] = module(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "none" ):
'''simple docstring'''
super().__init__()
snake_case: Optional[int] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = approximate
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.proj(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = self.gelu(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = nn.Linear(SCREAMING_SNAKE_CASE__ , dim_out * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case: int = self.proj(SCREAMING_SNAKE_CASE__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = self.proj(SCREAMING_SNAKE_CASE__ )
return x * torch.sigmoid(1.7_02 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Optional[Any] = nn.Embedding(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = nn.SiLU()
snake_case: Union[str, Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , embedding_dim * 2 )
snake_case: int = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case: Dict = torch.chunk(SCREAMING_SNAKE_CASE__ , 2 )
snake_case: str = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.SiLU()
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , 6 * embedding_dim , bias=SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ , eps=1E-6 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=SCREAMING_SNAKE_CASE__ ) ) )
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case: str = emb.chunk(6 , dim=1 )
snake_case: Dict = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 1E-5 ):
'''simple docstring'''
super().__init__()
snake_case: str = num_groups
snake_case: str = eps
if act_fn is None:
snake_case: Dict = None
else:
snake_case: List[str] = get_activation(SCREAMING_SNAKE_CASE__ )
snake_case: Any = nn.Linear(SCREAMING_SNAKE_CASE__ , out_dim * 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.act:
snake_case: Optional[Any] = self.act(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.linear(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = emb[:, :, None, None]
snake_case , snake_case: List[Any] = emb.chunk(2 , dim=1 )
snake_case: Any = F.group_norm(SCREAMING_SNAKE_CASE__ , self.num_groups , eps=self.eps )
snake_case: Optional[int] = x * (1 + scale) + shift
return x | 692 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def lowerCAmelCase_ ( __A : Dict , __A : str , __A : int , __A : Optional[Any] = None , ):
'''simple docstring'''
snake_case: Any = np.shape(__SCREAMING_SNAKE_CASE )
snake_case: int = np.shape(__SCREAMING_SNAKE_CASE )
snake_case: str = np.shape(__SCREAMING_SNAKE_CASE )
if shape_a[0] != shape_b[0]:
snake_case: Union[str, Any] = (
"Expected the same number of rows for A and B. "
f"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(__SCREAMING_SNAKE_CASE )
if shape_b[1] != shape_c[1]:
snake_case: str = (
"Expected the same number of columns for B and C. "
f"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(__SCREAMING_SNAKE_CASE )
snake_case: Any = pseudo_inv
if a_inv is None:
try:
snake_case: Optional[Any] = np.linalg.inv(__SCREAMING_SNAKE_CASE )
except np.linalg.LinAlgError:
raise ValueError(
'Input matrix A is not invertible. Cannot compute Schur complement.' )
return mat_c - mat_b.T @ a_inv @ mat_b
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case: Dict = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case: List[Any] = np.array([[2, 1], [6, 3]] )
snake_case: str = schur_complement(lowercase_ , lowercase_ , lowercase_ )
snake_case: int = np.block([[a, b], [b.T, c]] )
snake_case: int = np.linalg.det(lowercase_ )
snake_case: int = np.linalg.det(lowercase_ )
snake_case: str = np.linalg.det(lowercase_ )
self.assertAlmostEqual(lowercase_ , det_a * det_s )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case: Dict = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case: List[Any] = np.array([[2, 1], [6, 3]] )
with self.assertRaises(lowercase_ ):
schur_complement(lowercase_ , lowercase_ , lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case: Any = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case: List[Any] = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(lowercase_ ):
schur_complement(lowercase_ , lowercase_ , lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 704 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = RoCBertTokenizer
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = filter_non_english
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: Any = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
snake_case: List[Any] = {}
snake_case: List[str] = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = i
snake_case: Union[str, Any] = i
snake_case: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
snake_case: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: Dict = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ ) , [5, 6, 2, 5, 7, 8] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
snake_case: Union[str, Any] = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: str = i
snake_case: Optional[int] = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
snake_case: int = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def _UpperCamelCase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
snake_case: List[str] = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , return_token_type_ids=SCREAMING_SNAKE_CASE__ , return_offsets_mapping=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , )
snake_case: Optional[int] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__ , 'do_lower_case' ) else False
snake_case: int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = ['的', '人', '有']
snake_case: Any = ''.join(SCREAMING_SNAKE_CASE__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case: Tuple = True
snake_case: List[Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = False
snake_case: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: int = tokenizer_p.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
# it is expected that only the first Chinese character is not preceded by "##".
snake_case: Union[str, Any] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
snake_case: int = tokenizer.encode('你好' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Any = tokenizer.encode('你是谁' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
snake_case: str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Dict = '你好，你是谁'
snake_case: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.encode_plus(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_ ( __A : Any , __A : Any , __A : Optional[Any] , __A : str , __A : Dict ):
'''simple docstring'''
with open(__A ) as metadata_file:
snake_case: int = json.load(__A )
snake_case: List[str] = LukeConfig(use_entity_aware_attention=__A , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
snake_case: List[str] = torch.load(__A , map_location='cpu' )['module']
# Load the entity vocab file
snake_case: Any = load_original_entity_vocab(__A )
# add an entry for [MASK2]
snake_case: int = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
snake_case: Optional[Any] = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
snake_case: Union[str, Any] = AddedToken('<ent>' , lstrip=__A , rstrip=__A )
snake_case: Optional[Any] = AddedToken('<ent2>' , lstrip=__A , rstrip=__A )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__A )
with open(os.path.join(__A , 'tokenizer_config.json' ) , 'r' ) as f:
snake_case: int = json.load(__A )
snake_case: List[Any] = 'MLukeTokenizer'
with open(os.path.join(__A , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(__A , __A )
with open(os.path.join(__A , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(__A , __A )
snake_case: List[Any] = MLukeTokenizer.from_pretrained(__A )
# Initialize the embeddings of the special tokens
snake_case: Tuple = tokenizer.convert_tokens_to_ids(['@'] )[0]
snake_case: List[str] = tokenizer.convert_tokens_to_ids(['#'] )[0]
snake_case: Union[str, Any] = state_dict['embeddings.word_embeddings.weight']
snake_case: List[Any] = word_emb[ent_init_index].unsqueeze(0 )
snake_case: List[Any] = word_emb[enta_init_index].unsqueeze(0 )
snake_case: List[str] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
snake_case: Any = state_dict[bias_name]
snake_case: Union[str, Any] = decoder_bias[ent_init_index].unsqueeze(0 )
snake_case: Optional[Any] = decoder_bias[enta_init_index].unsqueeze(0 )
snake_case: Tuple = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
snake_case: Dict = f"""encoder.layer.{layer_index}.attention.self."""
snake_case: Union[str, Any] = state_dict[prefix + matrix_name]
snake_case: Optional[int] = state_dict[prefix + matrix_name]
snake_case: Optional[int] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
snake_case: str = state_dict['entity_embeddings.entity_embeddings.weight']
snake_case: Dict = entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
snake_case: str = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
snake_case: Optional[Any] = state_dict['entity_predictions.bias']
snake_case: Any = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
snake_case: List[str] = torch.cat([entity_prediction_bias, entity_mask_bias] )
snake_case: Any = LukeForMaskedLM(config=__A ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
snake_case: Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
snake_case: Optional[int] = state_dict[key]
else:
snake_case: Union[str, Any] = state_dict[key]
snake_case , snake_case: List[str] = model.load_state_dict(__A , strict=__A )
if set(__A ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__A ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
snake_case: str = MLukeTokenizer.from_pretrained(__A , task='entity_classification' )
snake_case: int = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
snake_case: Tuple = (0, 9)
snake_case: List[Any] = tokenizer(__A , entity_spans=[span] , return_tensors='pt' )
snake_case: Optional[int] = model(**__A )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
snake_case: Any = torch.Size((1, 33, 7_68) )
snake_case: int = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __A , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
snake_case: List[str] = torch.Size((1, 1, 7_68) )
snake_case: Union[str, Any] = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
f""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __A , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
snake_case: Any = MLukeTokenizer.from_pretrained(__A )
snake_case: Optional[int] = 'Tokyo is the capital of <mask>.'
snake_case: Dict = (24, 30)
snake_case: str = tokenizer(__A , entity_spans=[span] , return_tensors='pt' )
snake_case: str = model(**__A )
snake_case: List[str] = encoding['input_ids'][0].tolist()
snake_case: int = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
snake_case: List[Any] = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__A )
snake_case: Union[str, Any] = outputs.entity_logits[0][0].argmax().item()
snake_case: Optional[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(__A ) )
model.save_pretrained(__A )
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: List[str] = ['[MASK]', '[PAD]', '[UNK]']
snake_case: int = [json.loads(__A ) for line in open(__A )]
snake_case: Optional[Any] = {}
for entry in data:
snake_case: Union[str, Any] = entry['id']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
snake_case: int = entity_id
break
snake_case: int = f"""{language}:{entity_name}"""
snake_case: Dict = entity_id
return new_mapping
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
__UpperCAmelCase = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
) | 705 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCAmelCase = 6378137.0
__UpperCAmelCase = 6356752.314245
__UpperCAmelCase = 6_378_137
def lowerCAmelCase_ ( __A : float , __A : float , __A : float , __A : float ):
'''simple docstring'''
snake_case: Optional[Any] = (AXIS_A - AXIS_B) / AXIS_A
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: List[Any] = atan((1 - flattening) * tan(radians(__A ) ) )
snake_case: Tuple = radians(__A )
snake_case: Tuple = radians(__A )
# Equation
snake_case: List[Any] = sin((phi_a - phi_a) / 2 )
snake_case: Dict = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
snake_case: Union[str, Any] = sqrt(sin_sq_phi + (cos(__A ) * cos(__A ) * sin_sq_lambda) )
return 2 * RADIUS * asin(__A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 692 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__UpperCAmelCase = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = None
class SCREAMING_SNAKE_CASE ( folder_based_builder.FolderBasedBuilder ):
'''simple docstring'''
__UpperCamelCase = datasets.Audio()
__UpperCamelCase = "audio"
__UpperCamelCase = AudioFolderConfig
__UpperCamelCase = 42 # definition at the bottom of the script
__UpperCamelCase = AudioClassification(audio_column="audio" , label_column="label" )
__UpperCAmelCase = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
__UpperCAmelCase = AUDIO_EXTENSIONS | 707 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
snake_case: Tuple = model.config
snake_case: str = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
snake_case: Optional[Any] = MBartConfig(
is_decoder=__A , is_encoder_decoder=__A , add_cross_attention=__A , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__A , add_final_layer_norm=__A , )
return encoder_config, decoder_config
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if "encoder.model" in name:
snake_case: Optional[Any] = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
snake_case: str = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
snake_case: Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
snake_case: Optional[int] = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
snake_case: Tuple = 'encoder.' + name
if "attn.proj" in name:
snake_case: Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
snake_case: Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
snake_case: Union[str, Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
snake_case: Dict = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
snake_case: List[str] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
snake_case: Dict = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
snake_case: Dict = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
snake_case: int = 'encoder.layernorm.bias'
return name
def lowerCAmelCase_ ( __A : List[Any] , __A : Optional[Any] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case: List[Any] = orig_state_dict.pop(__A )
if "qkv" in key:
snake_case: Union[str, Any] = key.split('.' )
snake_case: Optional[Any] = int(key_split[3] )
snake_case: Any = int(key_split[5] )
snake_case: Union[str, Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case: Union[str, Any] = val[:dim, :]
snake_case: Any = val[dim : dim * 2, :]
snake_case: List[str] = val[-dim:, :]
else:
snake_case: str = val[:dim]
snake_case: Union[str, Any] = val[dim : dim * 2]
snake_case: List[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
snake_case: Optional[int] = val
return orig_state_dict
def lowerCAmelCase_ ( __A : List[Any] , __A : Any=None , __A : List[str]=False ):
'''simple docstring'''
snake_case: str = DonutModel.from_pretrained(__A ).eval()
# load HuggingFace model
snake_case , snake_case: Optional[Any] = get_configs(__A )
snake_case: Optional[int] = DonutSwinModel(__A )
snake_case: Tuple = MBartForCausalLM(__A )
snake_case: Optional[Any] = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
snake_case: Optional[int] = original_model.state_dict()
snake_case: Optional[int] = convert_state_dict(__A , __A )
model.load_state_dict(__A )
# verify results on scanned document
snake_case: Union[str, Any] = load_dataset('hf-internal-testing/example-documents' )
snake_case: str = dataset['test'][0]['image'].convert('RGB' )
snake_case: Optional[int] = XLMRobertaTokenizerFast.from_pretrained(__A , from_slow=__A )
snake_case: Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
snake_case: Dict = DonutProcessor(__A , __A )
snake_case: Optional[Any] = processor(__A , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
snake_case: int = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
snake_case: Optional[Any] = 'When is the coffee break?'
snake_case: Optional[int] = task_prompt.replace('{user_input}' , __A )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
snake_case: Dict = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
snake_case: str = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
snake_case: str = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
snake_case: int = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
snake_case: Optional[Any] = 'hello world'
else:
raise ValueError('Model name not supported' )
snake_case: Optional[int] = original_model.decoder.tokenizer(__A , add_special_tokens=__A , return_tensors='pt' )[
'input_ids'
]
snake_case: Any = original_model.encoder.model.patch_embed(__A )
snake_case , snake_case: Dict = model.encoder.embeddings(__A )
assert torch.allclose(__A , __A , atol=1E-3 )
# verify encoder hidden states
snake_case: Tuple = original_model.encoder(__A )
snake_case: List[str] = model.encoder(__A ).last_hidden_state
assert torch.allclose(__A , __A , atol=1E-2 )
# verify decoder hidden states
snake_case: List[Any] = original_model(__A , __A , __A ).logits
snake_case: List[Any] = model(__A , decoder_input_ids=__A ).logits
assert torch.allclose(__A , __A , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
processor.save_pretrained(__A )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
__UpperCAmelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 692 | 0 |
from __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase_ ( __A : float , __A : float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 708 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
snake_case: Union[str, Any] = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 1_28,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 1_42,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , x.transpose() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = np.random.randn(3 , 4 )
snake_case: Optional[Any] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(3 , 4 , 5 )
snake_case: Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , transpose(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Dict = np.random.randn(3 , 4 , 5 )
snake_case: str = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Optional[int] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Optional[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) , np.asarray(transpose(SCREAMING_SNAKE_CASE__ , axes=(1, 2, 0) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) )
snake_case: Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: List[str] = np.random.randn(3 , 4 , 5 )
snake_case: Tuple = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = np.random.randn(3 , 4 )
snake_case: Tuple = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ).numpy() ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: str = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (4, 3) ) ) ) )
snake_case: Any = np.random.randn(3 , 4 , 5 )
snake_case: List[str] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) , np.asarray(reshape(SCREAMING_SNAKE_CASE__ , (12, 5) ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.squeeze(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: List[str] = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(1 , 3 , 4 )
snake_case: List[str] = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Optional[Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: int = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = np.random.randn(1 , 3 , 4 )
snake_case: Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , squeeze(SCREAMING_SNAKE_CASE__ ).numpy() ) )
snake_case: Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(1 , 3 , 4 )
snake_case: List[Any] = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ ) ) ) )
snake_case: Tuple = np.random.randn(1 , 4 , 1 , 5 )
snake_case: Tuple = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) , np.asarray(squeeze(SCREAMING_SNAKE_CASE__ , axis=2 ) ) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) )
@require_torch
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = np.random.randn(3 , 4 )
snake_case: Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_tf
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = np.random.randn(3 , 4 )
snake_case: Any = tf.constant(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ).numpy() ) )
@require_flax
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = np.random.randn(3 , 4 )
snake_case: int = jnp.array(SCREAMING_SNAKE_CASE__ )
self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) , np.asarray(expand_dims(SCREAMING_SNAKE_CASE__ , axis=1 ) ) ) ) | 692 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
super().__init__()
snake_case: int = nn.Linear(3 , 4 )
snake_case: Tuple = nn.BatchNormad(4 )
snake_case: Union[str, Any] = nn.Linear(4 , 5 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__ ) ) )
class SCREAMING_SNAKE_CASE ( __A ):
'''simple docstring'''
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class SCREAMING_SNAKE_CASE ( __A ):
'''simple docstring'''
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return output + 1
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = ModelForTest()
snake_case: Union[str, Any] = ModelHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(test_model._hf_hook , UpperCamelCase__ )
self.assertTrue(hasattr(UpperCamelCase__ , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(UpperCamelCase__ )
self.assertFalse(hasattr(UpperCamelCase__ , '_hf_hook' ) )
self.assertFalse(hasattr(UpperCamelCase__ , '_old_forward' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = ModelForTest()
snake_case: Optional[Any] = ModelHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ , append=UpperCamelCase__ )
self.assertEqual(isinstance(test_model._hf_hook , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(UpperCamelCase__ , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(UpperCamelCase__ )
self.assertFalse(hasattr(UpperCamelCase__ , '_hf_hook' ) )
self.assertFalse(hasattr(UpperCamelCase__ , '_old_forward' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = ModelForTest()
snake_case: Dict = torch.randn(2 , 3 )
snake_case: Optional[int] = test_model(x + 1 )
snake_case: Dict = test_model(x + 2 )
snake_case: List[Any] = PreForwardHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: Tuple = test_model(UpperCamelCase__ )
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
snake_case: Optional[int] = PreForwardHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: List[str] = test_model(UpperCamelCase__ )
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
snake_case: Any = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: Tuple = test_model(UpperCamelCase__ )
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = ModelForTest()
snake_case: List[Any] = torch.randn(2 , 3 )
snake_case: Tuple = test_model(UpperCamelCase__ )
snake_case: Union[str, Any] = PostForwardHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: Any = test_model(UpperCamelCase__ )
self.assertTrue(torch.allclose(UpperCamelCase__ , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
snake_case: Optional[int] = PostForwardHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: int = test_model(UpperCamelCase__ )
self.assertTrue(torch.allclose(UpperCamelCase__ , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
snake_case: Tuple = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: str = test_model(UpperCamelCase__ )
assert torch.allclose(UpperCamelCase__ , output + 2 , atol=1E-5 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = ModelForTest()
snake_case: Optional[Any] = torch.randn(2 , 3 )
snake_case: List[Any] = test_model(UpperCamelCase__ )
snake_case: Any = PostForwardHook()
add_hook_to_module(UpperCamelCase__ , UpperCamelCase__ )
snake_case: List[Any] = test_model(UpperCamelCase__ )
self.assertTrue(torch.allclose(UpperCamelCase__ , output + 1 ) )
self.assertTrue(outputa.requires_grad )
snake_case: Optional[int] = True
snake_case: Optional[Any] = test_model(UpperCamelCase__ )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Tuple = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
snake_case: int = torch.randn(2 , 3 )
snake_case: Dict = model(UpperCamelCase__ )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(UpperCamelCase__ , AlignDevicesHook(io_same_device=UpperCamelCase__ ) )
snake_case: Union[str, Any] = torch.randn(2 , 3 ).to(0 )
snake_case: int = model(UpperCamelCase__ )
self.assertEqual(output.device , torch.device(0 ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case: str = {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**UpperCamelCase__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**UpperCamelCase__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**UpperCamelCase__ ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case: Dict = torch.device(hook_kwargs['execution_device'] )
self.assertEqual(model.batchnorm.running_mean.device , UpperCamelCase__ )
snake_case: str = torch.randn(2 , 3 )
snake_case: Any = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
snake_case: str = {
'execution_device': 0 if torch.cuda.is_available() else 'cpu',
'offload': True,
'offload_buffers': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**UpperCamelCase__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**UpperCamelCase__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**UpperCamelCase__ ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case: Union[str, Any] = torch.randn(2 , 3 )
snake_case: Union[str, Any] = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case: Union[str, Any] = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(UpperCamelCase__ , execution_device=UpperCamelCase__ , offload=UpperCamelCase__ )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case: Any = torch.device(UpperCamelCase__ )
self.assertEqual(model.batchnorm.running_mean.device , UpperCamelCase__ )
snake_case: Optional[Any] = torch.randn(2 , 3 )
snake_case: Optional[int] = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(UpperCamelCase__ )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(UpperCamelCase__ , execution_device=UpperCamelCase__ , offload=UpperCamelCase__ , offload_buffers=UpperCamelCase__ )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case: int = torch.randn(2 , 3 )
snake_case: List[str] = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(UpperCamelCase__ )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
snake_case: Union[str, Any] = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(
UpperCamelCase__ , execution_device=UpperCamelCase__ , offload=UpperCamelCase__ , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
snake_case: List[str] = torch.device(UpperCamelCase__ )
self.assertEqual(model.batchnorm.running_mean.device , UpperCamelCase__ )
snake_case: Any = torch.randn(2 , 3 )
snake_case: List[str] = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(UpperCamelCase__ )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
UpperCamelCase__ , execution_device=UpperCamelCase__ , offload=UpperCamelCase__ , weights_map=model.state_dict() , offload_buffers=UpperCamelCase__ , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
snake_case: Tuple = torch.randn(2 , 3 )
snake_case: Dict = model(UpperCamelCase__ )
self.assertEqual(output.device , UpperCamelCase__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(UpperCamelCase__ )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) | 709 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__UpperCAmelCase = logging.get_logger(__name__)
# General docstring
__UpperCAmelCase = "PoolFormerConfig"
# Base docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = [1, 512, 7, 7]
# Image classification docstring
__UpperCAmelCase = "sail/poolformer_s12"
__UpperCAmelCase = "tabby, tabby cat"
__UpperCAmelCase = [
"sail/poolformer_s12",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowerCAmelCase_ ( __A : Tuple , __A : float = 0.0 , __A : bool = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
snake_case: Union[str, Any] = 1 - drop_prob
snake_case: List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
snake_case: List[Any] = keep_prob + torch.rand(__A , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
snake_case: Any = input.div(__A ) * random_tensor
return output
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = drop_prob
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return drop_path(SCREAMING_SNAKE_CASE__ , self.drop_prob , self.training )
def _UpperCamelCase ( self ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
super().__init__()
snake_case: List[str] = patch_size if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (patch_size, patch_size)
snake_case: List[str] = stride if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (stride, stride)
snake_case: Union[str, Any] = padding if isinstance(SCREAMING_SNAKE_CASE__ , collections.abc.Iterable ) else (padding, padding)
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = norm_layer(SCREAMING_SNAKE_CASE__ ) if norm_layer else nn.Identity()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.projection(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.norm(SCREAMING_SNAKE_CASE__ )
return embeddings
class SCREAMING_SNAKE_CASE ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(1 , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: str = nn.AvgPoolad(SCREAMING_SNAKE_CASE__ , stride=1 , padding=pool_size // 2 , count_include_pad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.pool(SCREAMING_SNAKE_CASE__ ) - hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: Any = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
snake_case: str = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ )
if isinstance(config.hidden_act , SCREAMING_SNAKE_CASE__ ):
snake_case: Tuple = ACTaFN[config.hidden_act]
else:
snake_case: int = config.hidden_act
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.act_fn(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.drop(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.conva(SCREAMING_SNAKE_CASE__ )
snake_case: str = self.drop(SCREAMING_SNAKE_CASE__ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Tuple = PoolFormerPooling(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerOutput(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Dict = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__ )
# Useful for training neural nets
snake_case: Union[str, Any] = PoolFormerDropPath(SCREAMING_SNAKE_CASE__ ) if drop_path > 0.0 else nn.Identity()
snake_case: Optional[Any] = config.use_layer_scale
if config.use_layer_scale:
snake_case: Any = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
snake_case: int = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__) ) , requires_grad=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self.use_layer_scale:
snake_case: str = self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Dict = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
snake_case: str = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = ()
snake_case: Dict = self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) )
snake_case: Union[str, Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
snake_case: Any = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = (output,) + outputs
return outputs
else:
snake_case: Optional[Any] = self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__ ) ) )
# First residual connection
snake_case: Union[str, Any] = pooling_output + hidden_states
snake_case: List[Any] = ()
# Second residual connection inside the PoolFormerOutput block
snake_case: List[str] = self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__ ) ) )
snake_case: Dict = hidden_states + layer_output
snake_case: Optional[Any] = (output,) + outputs
return outputs
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: List[Any] = config
# stochastic depth decay rule
snake_case: List[Any] = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
snake_case: Union[str, Any] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
snake_case: List[Any] = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
# Transformer blocks
snake_case: str = []
snake_case: int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
snake_case: List[str] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
SCREAMING_SNAKE_CASE__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__ ) )
snake_case: Tuple = nn.ModuleList(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True ):
'''simple docstring'''
snake_case: str = () if output_hidden_states else None
snake_case: Dict = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
snake_case , snake_case: Dict = layers
# Get patch embeddings from hidden_states
snake_case: int = embedding_layer(SCREAMING_SNAKE_CASE__ )
# Send the embeddings through the blocks
for _, blk in enumerate(SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = blk(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = layer_outputs[0]
if output_hidden_states:
snake_case: List[str] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = PoolFormerConfig
__UpperCamelCase = "poolformer"
__UpperCamelCase = "pixel_values"
__UpperCamelCase = True
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE__ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: List[Any] = value
__UpperCAmelCase = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
__UpperCAmelCase = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n"
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = config
snake_case: Tuple = PoolFormerEncoder(SCREAMING_SNAKE_CASE__ )
# Initialize weights and apply final processing
self.post_init()
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case: List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
snake_case: Optional[Any] = self.encoder(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: List[Any] = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__()
snake_case: Any = nn.Linear(config.hidden_size , config.hidden_size )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = self.dense(SCREAMING_SNAKE_CASE__ )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , snake_case , )
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = config.num_labels
snake_case: str = PoolFormerModel(SCREAMING_SNAKE_CASE__ )
# Final norm
snake_case: int = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
snake_case: Dict = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ):
'''simple docstring'''
snake_case: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
snake_case: Optional[Any] = self.poolformer(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )
snake_case: Any = outputs[0]
snake_case: str = self.classifier(self.norm(SCREAMING_SNAKE_CASE__ ).mean([-2, -1] ) )
snake_case: Any = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case: Tuple = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case: Dict = 'single_label_classification'
else:
snake_case: List[str] = 'multi_label_classification'
if self.config.problem_type == "regression":
snake_case: Union[str, Any] = MSELoss()
if self.num_labels == 1:
snake_case: List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case: int = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.config.problem_type == "single_label_classification":
snake_case: Union[str, Any] = CrossEntropyLoss()
snake_case: Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case: int = BCEWithLogitsLoss()
snake_case: Optional[int] = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not return_dict:
snake_case: str = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states ) | 692 | 0 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__UpperCAmelCase = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
__UpperCAmelCase = logging.WARNING
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Optional[Any] = os.getenv('DATASETS_VERBOSITY' , __A )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
return _default_log_level
def lowerCAmelCase_ ( ):
'''simple docstring'''
return __name__.split('.' )[0]
def lowerCAmelCase_ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Tuple = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Tuple = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def lowerCAmelCase_ ( __A : Union[str, Any] = None ):
'''simple docstring'''
if name is None:
snake_case: Optional[Any] = _get_library_name()
return logging.getLogger(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def lowerCAmelCase_ ( __A : List[Any] ):
'''simple docstring'''
_get_library_root_logger().setLevel(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
return set_verbosity(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
return set_verbosity(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
return set_verbosity(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
return set_verbosity(__A )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Optional[int] = False
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ): # pylint: disable=unused-argument
'''simple docstring'''
snake_case: Dict = args[0] if args else None
def __iter__( self ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def empty_fn(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
'''simple docstring'''
return self
def __exit__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return
__UpperCAmelCase = True
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __call__( self , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*lowerCamelCase_ , **lowerCamelCase_ )
else:
return EmptyTqdm(*lowerCamelCase_ , **lowerCamelCase_ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowerCamelCase_ , **lowerCamelCase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__UpperCAmelCase = _tqdm_cls()
def lowerCAmelCase_ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def lowerCAmelCase_ ( ):
'''simple docstring'''
global _tqdm_active
snake_case: str = True
def lowerCAmelCase_ ( ):
'''simple docstring'''
global _tqdm_active
snake_case: Union[str, Any] = False | 710 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __A : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case: Any = cst_fwd.get(__A , np.inf )
snake_case: int = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
snake_case: Union[str, Any] = new_cost_f
snake_case: Tuple = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
snake_case: List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[Any] = -1
snake_case: Any = set()
snake_case: str = set()
snake_case: int = {source: 0}
snake_case: Dict = {destination: 0}
snake_case: int = {source: None}
snake_case: Union[str, Any] = {destination: None}
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: PriorityQueue[Any] = PriorityQueue()
snake_case: Tuple = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
snake_case , snake_case: List[str] = queue_forward.get()
visited_forward.add(__A )
snake_case , snake_case: int = queue_backward.get()
visited_backward.add(__A )
snake_case: str = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
snake_case: Optional[Any] = pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
snake_case: Any = shortest_distance
return shortest_path_distance
__UpperCAmelCase = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
__UpperCAmelCase = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__UpperCAmelCase = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: List[str] = self.layer[current_layer](UpperCamelCase__ , UpperCamelCase__ , head_mask[current_layer] )
snake_case: Any = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , lowercase_ , )
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(UpperCamelCase__ )
snake_case: Any = BertEncoderWithPabee(UpperCamelCase__ )
self.init_weights()
snake_case: Union[str, Any] = 0
snake_case: List[Any] = 0
snake_case: Optional[Any] = 0
snake_case: Any = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = threshold
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[Any] = patience
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = 0
snake_case: int = 0
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.inference_layers_num / self.inference_instances_num
snake_case: List[Any] = (
F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(UpperCamelCase__ )
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=False , ):
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
snake_case: List[str] = input_ids.size()
elif inputs_embeds is not None:
snake_case: List[Any] = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
snake_case: Dict = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
snake_case: Any = torch.ones(UpperCamelCase__ , device=UpperCamelCase__ )
if token_type_ids is None:
snake_case: Optional[Any] = torch.zeros(UpperCamelCase__ , dtype=torch.long , device=UpperCamelCase__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
snake_case: Optional[Any] = self.get_extended_attention_mask(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
snake_case , snake_case , snake_case: List[str] = encoder_hidden_states.size()
snake_case: Tuple = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
snake_case: int = torch.ones(UpperCamelCase__ , device=UpperCamelCase__ )
snake_case: List[Any] = self.invert_attention_mask(UpperCamelCase__ )
else:
snake_case: Any = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
snake_case: Optional[Any] = self.get_head_mask(UpperCamelCase__ , self.config.num_hidden_layers )
snake_case: Dict = self.embeddings(
input_ids=UpperCamelCase__ , position_ids=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ )
snake_case: Dict = embedding_output
if self.training:
snake_case: Dict = []
for i in range(self.config.num_hidden_layers ):
snake_case: Any = self.encoder.adaptive_forward(
UpperCamelCase__ , current_layer=UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ )
snake_case: Tuple = self.pooler(UpperCamelCase__ )
snake_case: Dict = output_layers[i](output_dropout(UpperCamelCase__ ) )
res.append(UpperCamelCase__ )
elif self.patience == 0: # Use all layers for inference
snake_case: List[str] = self.encoder(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , )
snake_case: Union[str, Any] = self.pooler(encoder_outputs[0] )
snake_case: Optional[Any] = [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase__ )]
else:
snake_case: List[str] = 0
snake_case: str = None
snake_case: List[str] = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
snake_case: Any = self.encoder.adaptive_forward(
UpperCamelCase__ , current_layer=UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ )
snake_case: str = self.pooler(UpperCamelCase__ )
snake_case: List[Any] = output_layers[i](UpperCamelCase__ )
if regression:
snake_case: Optional[int] = logits.detach()
if patient_result is not None:
snake_case: Union[str, Any] = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
snake_case: Optional[Any] = 0
else:
snake_case: List[Any] = logits.detach().argmax(dim=1 )
if patient_result is not None:
snake_case: List[str] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase__ ) ):
patient_counter += 1
else:
snake_case: Optional[int] = 0
snake_case: List[Any] = logits
if patient_counter == self.patience:
break
snake_case: Dict = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , lowercase_ , )
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
super().__init__(UpperCamelCase__ )
snake_case: List[Any] = config.num_labels
snake_case: Union[str, Any] = BertModelWithPabee(UpperCamelCase__ )
snake_case: Union[str, Any] = nn.Dropout(config.hidden_dropout_prob )
snake_case: Optional[int] = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , ):
'''simple docstring'''
snake_case: List[Any] = self.bert(
input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , position_ids=UpperCamelCase__ , head_mask=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
snake_case: List[str] = (logits[-1],)
if labels is not None:
snake_case: Dict = None
snake_case: str = 0
for ix, logits_item in enumerate(UpperCamelCase__ ):
if self.num_labels == 1:
# We are doing regression
snake_case: Optional[int] = MSELoss()
snake_case: List[Any] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
snake_case: Dict = CrossEntropyLoss()
snake_case: List[Any] = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
snake_case: Optional[int] = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
snake_case: List[str] = (total_loss / total_weights,) + outputs
return outputs | 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "▁"
__UpperCAmelCase = {"vocab_file": "sentencepiece.bpe.model"}
__UpperCAmelCase = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__UpperCAmelCase = {
"facebook/xglm-564M": 2_048,
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
snake_case: Optional[Any] = 7
snake_case: List[str] = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
snake_case: str = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
snake_case: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
snake_case: int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case: Tuple = 1
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case: Optional[Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
snake_case: Union[str, Any] = len(self.sp_model )
snake_case: str = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
snake_case: List[Any] = self.__dict__.copy()
snake_case: Union[str, Any] = None
snake_case: Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case: Union[str, Any] = {}
snake_case: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
snake_case: Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ ))
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: int = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case: Dict = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = ''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ' ' ).strip()
return out_string
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case: List[str] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as fi:
snake_case: int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,) | 692 | 0 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__UpperCAmelCase = [
"python",
"tqdm",
"regex",
"requests",
"packaging",
"filelock",
"numpy",
"tokenizers",
"huggingface-hub",
"safetensors",
"accelerate",
"pyyaml",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Any=None ):
'''simple docstring'''
require_version(deps[pkg] , __lowerCAmelCase ) | 712 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
return getitem, k
def lowerCAmelCase_ ( __A : Any , __A : Optional[int] ):
'''simple docstring'''
return setitem, k, v
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
return delitem, k
def lowerCAmelCase_ ( __A : str , __A : int , *__A : Tuple ):
'''simple docstring'''
try:
return fun(__A , *__A ), None
except Exception as e:
return None, e
__UpperCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__UpperCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__UpperCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__UpperCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def lowerCAmelCase_ ( __A : str ):
'''simple docstring'''
snake_case: List[Any] = HashMap(initial_block_size=4 )
snake_case: List[Any] = {}
for _, (fun, *args) in enumerate(__A ):
snake_case , snake_case: Optional[int] = _run_operation(__A , __A , *__A )
snake_case , snake_case: str = _run_operation(__A , __A , *__A )
assert my_res == py_res
assert str(__A ) == str(__A )
assert set(__A ) == set(__A )
assert len(__A ) == len(__A )
assert set(my.items() ) == set(py.items() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
def is_public(__A : str ) -> bool:
return not name.startswith('_' )
snake_case: Dict = {name for name in dir({} ) if is_public(__A )}
snake_case: List[str] = {name for name in dir(HashMap() ) if is_public(__A )}
assert dict_public_names > hash_public_names | 692 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int , __A : str ):
'''simple docstring'''
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(a_ ):
for j in range(a_ ):
if dist[i][j] != float('inf' ):
print(int(dist[i][j] ) , end='\t' )
else:
print('INF' , end='\t' )
print()
def lowerCAmelCase_ ( __A : str , __A : str ):
'''simple docstring'''
snake_case: Optional[int] = [[float('inf' ) for _ in range(a_ )] for _ in range(a_ )]
for i in range(a_ ):
for j in range(a_ ):
snake_case: Any = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(a_ ):
# looping through rows of graph array
for i in range(a_ ):
# looping through columns of graph array
for j in range(a_ ):
if (
dist[i][k] != float('inf' )
and dist[k][j] != float('inf' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
snake_case: Any = dist[i][k] + dist[k][j]
_print_dist(a_ , a_ )
return dist, v
if __name__ == "__main__":
__UpperCAmelCase = int(input("Enter number of vertices: "))
__UpperCAmelCase = int(input("Enter number of edges: "))
__UpperCAmelCase = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
__UpperCAmelCase = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
__UpperCAmelCase = int(input("Enter source:"))
__UpperCAmelCase = int(input("Enter destination:"))
__UpperCAmelCase = float(input("Enter weight:"))
__UpperCAmelCase = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0 | 713 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case: List[str] = getattr(__A , __A )
if weight_type is not None:
snake_case: Optional[int] = getattr(__A , __A ).shape
else:
snake_case: Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case: Optional[int] = value
elif weight_type == "weight_g":
snake_case: List[str] = value
elif weight_type == "weight_v":
snake_case: Dict = value
elif weight_type == "bias":
snake_case: Optional[Any] = value
else:
snake_case: int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __A : List[Any] , __A : List[str] ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: List[Any] = fairseq_model.state_dict()
snake_case: Union[str, Any] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case: Dict = None
for name, value in fairseq_dict.items():
snake_case: Tuple = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == 'group' , )
snake_case: List[Any] = True
elif name.split('.' )[0] == "proj":
snake_case: List[Any] = fairseq_model.proj
snake_case: int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case: int = True
if "*" in mapped_key:
snake_case: List[str] = name.split(__A )[0].split('.' )[-2]
snake_case: Dict = mapped_key.replace('*' , __A )
if "weight_g" in name:
snake_case: Tuple = 'weight_g'
elif "weight_v" in name:
snake_case: int = 'weight_v'
elif "bias" in name:
snake_case: Tuple = 'bias'
elif "weight" in name:
snake_case: List[Any] = 'weight'
else:
snake_case: Any = None
set_recursively(__A , __A , __A , __A , __A )
continue
if not is_used:
unused_weights.append(__A )
logger.warning(f"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCAmelCase_ ( __A : List[str] , __A : List[Any] , __A : int , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: int = full_name.split('conv_layers.' )[-1]
snake_case: Tuple = name.split('.' )
snake_case: Any = int(items[0] )
snake_case: Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case: Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case: int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case: Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case: str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__A )
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
snake_case , snake_case: List[Any] = emb.weight.shape
snake_case: Optional[int] = nn.Linear(__A , __A , bias=__A )
snake_case: Any = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
with open(__A , 'r' , encoding='utf-8' ) as f:
snake_case: List[Any] = f.readlines()
snake_case: Any = [line.split(' ' )[0] for line in lines]
snake_case: int = len(__A )
snake_case: Dict = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(__A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : int , __A : str , ):
'''simple docstring'''
snake_case: Union[str, Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: str = SpeechaTextaConfig.from_pretrained(
__A , vocab_size=__A , decoder_layers=__A , do_stable_layer_norm=__A )
snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
snake_case , snake_case , snake_case: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
snake_case: List[Any] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case: Optional[Any] = WavaVecaModel(__A )
snake_case: Any = recursively_load_weights_wavaveca(model.encoder , __A )
snake_case: Union[str, Any] = SpeechaTextaForCausalLM(__A )
snake_case , snake_case: Optional[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__A )
# set output linear layer
unexpected_keys.remove('embed_out' )
snake_case: str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
snake_case: int = SpeechEncoderDecoderModel(encoder=__A , decoder=__A )
snake_case: List[Any] = False
# add projection layer
snake_case: Union[str, Any] = nn.Parameter(projection_layer.weight )
snake_case: Union[str, Any] = nn.Parameter(projection_layer.bias )
snake_case: List[Any] = create_vocab_dict(__A )
with open(os.path.join(__A , 'vocab.json' ) , 'w' ) as fp:
json.dump(__A , __A )
snake_case: Union[str, Any] = SpeechaTextaTokenizer(os.path.join(__A , 'vocab.json' ) )
tokenizer.save_pretrained(__A )
snake_case: Tuple = hf_wavavec.config.to_dict()
snake_case: int = tokenizer.pad_token_id
snake_case: Dict = tokenizer.bos_token_id
snake_case: Optional[int] = tokenizer.eos_token_id
snake_case: Dict = 'speech_to_text_2'
snake_case: Optional[Any] = 'wav2vec2'
snake_case: Tuple = SpeechEncoderDecoderConfig.from_dict(__A )
hf_wavavec.save_pretrained(__A )
feature_extractor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
) | 692 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class SCREAMING_SNAKE_CASE ( _a ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(features=_A , cache_dir=_A , keep_in_memory=_A , **_A )
snake_case: int = Sql(
cache_dir=_A , features=_A , sql=_A , con=_A , **_A , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = None
snake_case: str = None
snake_case: Union[str, Any] = None
snake_case: str = None
self.builder.download_and_prepare(
download_config=_A , download_mode=_A , verification_mode=_A , base_path=_A , )
# Build dataset for splits
snake_case: Optional[Any] = self.builder.as_dataset(
split='train' , verification_mode=_A , in_memory=self.keep_in_memory )
return dataset
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
snake_case: int = dataset
snake_case: int = name
snake_case: Optional[Any] = con
snake_case: List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
snake_case: str = num_proc
snake_case: int = to_sql_kwargs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.to_sql_kwargs.pop('sql' , _A )
snake_case: Union[str, Any] = self.to_sql_kwargs.pop('con' , _A )
snake_case: int = self.to_sql_kwargs.pop('index' , _A )
snake_case: Optional[Any] = self._write(index=_A , **self.to_sql_kwargs )
return written
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case , snake_case: Dict = args
snake_case: List[Any] = {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
snake_case: Union[str, Any] = query_table(
table=self.dataset.data , key=slice(_A , offset + self.batch_size ) , indices=self.dataset._indices , )
snake_case: List[str] = batch.to_pandas()
snake_case: Union[str, Any] = df.to_sql(self.name , self.con , index=_A , **_A )
return num_rows or len(_A )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: List[Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating SQL from Arrow format' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
snake_case , snake_case: str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _A , _A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating SQL from Arrow format' , ):
written += num_rows
return written | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }') | 692 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"nielsr/canine-s": 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
__UpperCAmelCase = 1_114_112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__UpperCAmelCase = 0
__UpperCAmelCase = 0XE_000
__UpperCAmelCase = 0XE_001
__UpperCAmelCase = 0XE_002
__UpperCAmelCase = 0XE_003
__UpperCAmelCase = 0XE_004
# Maps special codepoints to human-readable names.
__UpperCAmelCase = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
__UpperCAmelCase = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=chr(__UpperCamelCase ) , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20_48 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
snake_case: List[str] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else bos_token
snake_case: List[str] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else eos_token
snake_case: Tuple = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else sep_token
snake_case: Optional[int] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else cls_token
snake_case: Optional[Any] = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case: str = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token
super().__init__(
bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , model_max_length=__UpperCamelCase , **__UpperCamelCase , )
# Creates a mapping for looking up the IDs of special symbols.
snake_case: List[str] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
snake_case: List[str] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
snake_case: List[Any] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
snake_case: List[str] = UNICODE_VOCAB_SIZE
snake_case: Optional[Any] = len(self._special_codepoints )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return self._unicode_vocab_size
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return list(__UpperCamelCase )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
try:
return ord(__UpperCamelCase )
except TypeError:
raise ValueError(F"""invalid token: \'{token}\'""" )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(__UpperCamelCase )
except TypeError:
raise ValueError(F"""invalid id: {index}""" )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return "".join(__UpperCamelCase )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: Tuple = [self.sep_token_id]
snake_case: Union[str, Any] = [self.cls_token_id]
snake_case: int = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
snake_case: Any = [1] + ([0] * len(__UpperCamelCase )) + [1]
if token_ids_a is not None:
result += ([0] * len(__UpperCamelCase )) + [1]
return result
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
snake_case: List[str] = [self.sep_token_id]
snake_case: Dict = [self.cls_token_id]
snake_case: str = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
return () | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
__UpperCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
__UpperCAmelCase = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
__UpperCAmelCase = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def lowerCAmelCase_ ( __A : Dict , __A : List[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
snake_case: List[Any] = k.replace(__A , __A )
return k
def lowerCAmelCase_ ( __A : dict , __A : dict ):
'''simple docstring'''
snake_case: Optional[int] = BigBirdPegasusConfig(**__A )
snake_case: List[Any] = BigBirdPegasusForConditionalGeneration(__A )
snake_case: Any = torch_model.state_dict()
snake_case: Any = {}
# separating decoder weights
snake_case: Optional[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
snake_case: Any = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
snake_case: List[str] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Any = DECODER_PATTERNS
snake_case: int = rename_state_dict_key(__A , __A )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: Optional[Any] = v.T
snake_case: Any = torch.from_numpy(__A )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
snake_case: List[Any] = [k.endswith(__A ) for ending in KEYS_TO_IGNORE]
if any(__A ):
continue
snake_case: Union[str, Any] = REMAINING_PATTERNS
snake_case: str = rename_state_dict_key(__A , __A )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
snake_case: int = v.T
snake_case: Any = torch.from_numpy(__A )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
snake_case: str = mapping['model.embed_positions.weight']
snake_case: Any = mapping.pop('model.embed_positions.weight' )
snake_case , snake_case: Union[str, Any] = torch_model.load_state_dict(__A , strict=__A )
snake_case: Optional[int] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
snake_case: Tuple = tf.train.list_variables(__A )
snake_case: str = {}
snake_case: List[str] = ['global_step']
for name, shape in tqdm(__A , desc='converting tf checkpoint to dict' ):
snake_case: str = any(pat in name for pat in ignore_name )
if skip_key:
continue
snake_case: Any = tf.train.load_variable(__A , __A )
snake_case: Optional[int] = array
return tf_weights
def lowerCAmelCase_ ( __A : str , __A : str , __A : dict ):
'''simple docstring'''
snake_case: int = get_tf_weights_as_numpy(__A )
snake_case: int = convert_bigbird_pegasus(__A , __A )
torch_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 692 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
snake_case: int = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case: Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__UpperCAmelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 692 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__UpperCAmelCase = """ def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
"""
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , 'models/bert/' ) )
snake_case: List[str] = self.transformer_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , 'src/transformers/models/bert/modeling_bert.py' ) , os.path.join(self.transformer_dir , 'models/bert/modeling_bert.py' ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = 'src/transformers'
shutil.rmtree(self.transformer_dir )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
'''simple docstring'''
snake_case: Union[str, Any] = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
snake_case: List[Any] = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
snake_case: Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
snake_case: Any = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = os.path.join(self.transformer_dir , 'new_code.py' )
with open(SCREAMING_SNAKE_CASE__ , 'w' , newline='\n' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , 'r' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , re.sub('Bert' , 'TestModel' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
snake_case: Dict = 'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}""" , F"""{long_class_name}LMPredictionHead""" , re.sub('Bert' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('Bert' , 'TestModel' , SCREAMING_SNAKE_CASE__ ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = check_copies.LOCALIZED_READMES['README_zh-hans.md']
snake_case: Union[str, Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
' Luong, Quoc V. Le, Christopher D. Manning.'
)
snake_case: str = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case: Union[str, Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
' Christopher D. Manning 发布。\n'
)
snake_case , snake_case: Dict = check_copies.convert_to_localized_md(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , localized_readme['format_model_list'] )
self.assertFalse(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case , snake_case: Optional[Any] = check_copies.convert_to_localized_md(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , localized_readme['format_model_list'] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
)
snake_case: int = (
'1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case: str = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
snake_case , snake_case: Dict = check_copies.convert_to_localized_md(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , localized_readme['format_model_list'] )
# Check if the model link is synchronized.
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) | 717 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = tempfile.mkdtemp()
snake_case: Optional[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
snake_case: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
snake_case: Optional[int] = {
'do_resize': True,
'size': {'height': 2_24, 'width': 2_24},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'do_convert_rgb': True,
}
snake_case: Union[str, Any] = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
snake_case: Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_tokenizer()
snake_case: Union[str, Any] = self.get_rust_tokenizer()
snake_case: Union[str, Any] = self.get_image_processor()
snake_case: List[str] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
snake_case: List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
snake_case: Any = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case: Optional[int] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
snake_case: Union[str, Any] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = self.get_image_processor()
snake_case: Tuple = self.get_tokenizer()
snake_case: Optional[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.prepare_image_inputs()
snake_case: List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
snake_case: Dict = processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.get_image_processor()
snake_case: Optional[int] = self.get_tokenizer()
snake_case: List[Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Union[str, Any] = processor(text=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 'Alexandra，T-shirt的价格是15便士。'
snake_case: Tuple = self.prepare_image_inputs()
snake_case: Any = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.get_image_processor()
snake_case: str = self.get_tokenizer()
snake_case: Union[str, Any] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case: int = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_image_processor()
snake_case: Dict = self.get_tokenizer()
snake_case: Optional[int] = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = 'Alexandra，T-shirt的价格是15便士。'
snake_case: List[Any] = self.prepare_image_inputs()
snake_case: Dict = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 692 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : Any , __A : int , __A : Dict ):
'''simple docstring'''
snake_case: Dict = WavaVecaForSequenceClassification.from_pretrained(__A , config=__A )
snake_case: Optional[int] = downstream_dict['''projector.weight''']
snake_case: Optional[int] = downstream_dict['''projector.bias''']
snake_case: Dict = downstream_dict['''model.post_net.linear.weight''']
snake_case: Optional[Any] = downstream_dict['''model.post_net.linear.bias''']
return model
def lowerCAmelCase_ ( __A : Optional[int] , __A : Dict , __A : Union[str, Any] ):
'''simple docstring'''
snake_case: str = WavaVecaForAudioFrameClassification.from_pretrained(__A , config=__A )
snake_case: List[Any] = downstream_dict['''model.linear.weight''']
snake_case: Tuple = downstream_dict['''model.linear.bias''']
return model
def lowerCAmelCase_ ( __A : Optional[int] , __A : Any , __A : List[Any] ):
'''simple docstring'''
snake_case: Dict = WavaVecaForXVector.from_pretrained(__A , config=__A )
snake_case: Tuple = downstream_dict['''connector.weight''']
snake_case: Any = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
snake_case: int = downstream_dict[
f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
snake_case: int = downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
snake_case: List[Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
snake_case: Any = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
snake_case: Dict = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
snake_case: Any = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
snake_case: Union[str, Any] = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def lowerCAmelCase_ ( __A : Union[str, Any] , __A : List[str] , __A : Dict , __A : Any ):
'''simple docstring'''
snake_case: List[str] = torch.load(__A , map_location='cpu' )
snake_case: Dict = checkpoint['''Downstream''']
snake_case: Optional[Any] = WavaVecaConfig.from_pretrained(__A )
snake_case: Tuple = WavaVecaFeatureExtractor.from_pretrained(
__A , return_attention_mask=__A , do_normalize=__A )
snake_case: List[str] = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
snake_case: Dict = convert_classification(__A , __A , __A )
elif arch.endswith('ForAudioFrameClassification' ):
snake_case: Union[str, Any] = convert_diarization(__A , __A , __A )
elif arch.endswith('ForXVector' ):
snake_case: Any = convert_xvector(__A , __A , __A )
else:
raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
snake_case: str = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(__A )
hf_model.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
)
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
__UpperCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path) | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "swinv2"
__UpperCamelCase = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: int = image_size
snake_case: Union[str, Any] = patch_size
snake_case: List[str] = num_channels
snake_case: Tuple = embed_dim
snake_case: str = depths
snake_case: Any = len(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = num_heads
snake_case: Optional[int] = window_size
snake_case: Any = mlp_ratio
snake_case: Optional[int] = qkv_bias
snake_case: Union[str, Any] = hidden_dropout_prob
snake_case: List[str] = attention_probs_dropout_prob
snake_case: Dict = drop_path_rate
snake_case: List[str] = hidden_act
snake_case: int = use_absolute_embeddings
snake_case: Any = layer_norm_eps
snake_case: Dict = initializer_range
snake_case: List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case: Tuple = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__ ) - 1) )
snake_case: Union[str, Any] = (0, 0, 0, 0) | 692 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = IFInpaintingPipeline
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {"latents"}
def _UpperCamelCase ( self ):
'''simple docstring'''
return self._get_dummy_components()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE__ ).startswith('mps' ):
snake_case: Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
snake_case: int = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
snake_case: str = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def _UpperCamelCase ( self ):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_save_load_local()
def _UpperCamelCase ( self ):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 719 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase = os.path.join(git_repo_path, "src", "transformers")
__UpperCAmelCase = "\n{0} = None\n"
__UpperCAmelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__UpperCAmelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tokenizers' )
snake_case: List[Any] = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'tensorflow_text' )
snake_case: int = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers' )
snake_case: Optional[Any] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tensorflow_text' )
snake_case: Dict = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'sentencepiece_and_tokenizers_and_vision' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , SCREAMING_SNAKE_CASE__ )
self.assertIn('tensorflow_text' , SCREAMING_SNAKE_CASE__ )
self.assertIn('sentencepiece_and_tokenizers' , SCREAMING_SNAKE_CASE__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertModel' , objects['tf'] )
self.assertIn('FlaxBertModel' , objects['flax'] )
self.assertIn('BertModel' , objects['torch'] )
self.assertIn('TFBertTokenizer' , objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer' , objects['sentencepiece_and_tokenizers'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , '\nCONSTANT = None\n' )
snake_case: Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case: Optional[int] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
snake_case: Tuple = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
snake_case: Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , SCREAMING_SNAKE_CASE__ ) | 692 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=99 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=37 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=None , ):
'''simple docstring'''
snake_case: Optional[Any] = parent
snake_case: List[Any] = batch_size
snake_case: Optional[int] = seq_length
snake_case: List[Any] = is_training
snake_case: Optional[int] = use_input_mask
snake_case: Optional[Any] = use_token_type_ids
snake_case: Optional[Any] = use_labels
snake_case: Optional[Any] = vocab_size
snake_case: List[str] = hidden_size
snake_case: int = num_hidden_layers
snake_case: Tuple = num_attention_heads
snake_case: Optional[Any] = intermediate_size
snake_case: Optional[Any] = hidden_act
snake_case: Dict = hidden_dropout_prob
snake_case: int = attention_probs_dropout_prob
snake_case: Any = max_position_embeddings
snake_case: str = type_vocab_size
snake_case: List[Any] = type_sequence_label_size
snake_case: Optional[Any] = initializer_range
snake_case: List[Any] = num_labels
snake_case: str = num_choices
snake_case: Dict = scope
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case: Optional[Any] = None
if self.use_input_mask:
snake_case: int = random_attention_mask([self.batch_size, self.seq_length] )
snake_case: int = None
snake_case: List[str] = None
snake_case: str = None
if self.use_labels:
snake_case: int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case: int = ids_tensor([self.batch_size] , self.num_choices )
snake_case: Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCamelCase ( self ):
'''simple docstring'''
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = DistilBertModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: List[str] = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: int = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Any = DistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: Tuple = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[int] = DistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: List[str] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = self.num_labels
snake_case: List[Any] = DistilBertForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: List[Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = self.num_labels
snake_case: List[Any] = DistilBertForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: int = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Union[str, Any] = self.num_choices
snake_case: Optional[Any] = DistilBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
snake_case: List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case: List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case: Optional[int] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.prepare_config_and_inputs()
((snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case)): int = config_and_inputs
snake_case: Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCamelCase = (
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = True
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = DistilBertModelTester(self )
snake_case: Union[str, Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , dim=37 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case: List[str] = DistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@slow
@require_torch_gpu
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
snake_case: List[Any] = True
snake_case: Optional[Any] = model_class(config=SCREAMING_SNAKE_CASE__ )
snake_case: int = self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = torch.jit.trace(
SCREAMING_SNAKE_CASE__ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , 'traced_model.pt' ) )
snake_case: Optional[int] = torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE__ , 'traced_model.pt' ) , map_location=SCREAMING_SNAKE_CASE__ )
loaded(inputs_dict['input_ids'].to(SCREAMING_SNAKE_CASE__ ) , inputs_dict['attention_mask'].to(SCREAMING_SNAKE_CASE__ ) )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = DistilBertModel.from_pretrained('distilbert-base-uncased' )
snake_case: Any = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
snake_case: str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case: str = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )[0]
snake_case: Optional[Any] = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
snake_case: Any = torch.tensor(
[[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) ) | 720 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Dict = question_encoder
snake_case: Union[str, Any] = generator
snake_case: Optional[int] = self.question_encoder
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
snake_case: Tuple = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case: int = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
snake_case: str = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
snake_case: Dict = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.question_encoder
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.generator
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "longest" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
snake_case: Optional[Any] = self.current_tokenizer.model_max_length
snake_case: int = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case: Any = self.current_tokenizer.model_max_length
snake_case: List[str] = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
snake_case: Dict = labels['input_ids']
return model_inputs | 692 | 0 |
'''simple docstring'''
import numpy as np
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case: int = (0, 0)
snake_case: Tuple = None
snake_case: List[Any] = 0
snake_case: Union[str, Any] = 0
snake_case: str = 0
def __eq__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.position == cell.position
def _UpperCamelCase ( self ):
'''simple docstring'''
print(self.position )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__=(5, 5) ):
'''simple docstring'''
snake_case: Any = np.zeros(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = world_size[0]
snake_case: List[str] = world_size[1]
def _UpperCamelCase ( self ):
'''simple docstring'''
print(self.w )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: int = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
snake_case: Dict = cell.position[0]
snake_case: Any = cell.position[1]
snake_case: List[str] = []
for n in neughbour_cord:
snake_case: Union[str, Any] = current_x + n[0]
snake_case: str = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
snake_case: Optional[Any] = Cell()
snake_case: Optional[Any] = (x, y)
snake_case: int = cell
neighbours.append(SCREAMING_SNAKE_CASE__ )
return neighbours
def lowerCAmelCase_ ( __A : Dict , __A : Union[str, Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: str = []
snake_case: List[str] = []
_open.append(lowerCamelCase__ )
while _open:
snake_case: List[str] = np.argmin([n.f for n in _open] )
snake_case: int = _open[min_f]
_closed.append(_open.pop(lowerCamelCase__ ) )
if current == goal:
break
for n in world.get_neigbours(lowerCamelCase__ ):
for c in _closed:
if c == n:
continue
snake_case: Dict = current.g + 1
snake_case , snake_case: List[Any] = n.position
snake_case , snake_case: Optional[Any] = goal.position
snake_case: List[Any] = (ya - ya) ** 2 + (xa - xa) ** 2
snake_case: int = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(lowerCamelCase__ )
snake_case: str = []
while current.parent is not None:
path.append(current.position )
snake_case: Optional[Any] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
__UpperCAmelCase = Gridworld()
# Start position and goal
__UpperCAmelCase = Cell()
__UpperCAmelCase = (0, 0)
__UpperCAmelCase = Cell()
__UpperCAmelCase = (4, 4)
print(F'path from {start.position} to {goal.position}')
__UpperCAmelCase = astar(world, start, goal)
# Just for visual reasons.
for i in s:
__UpperCAmelCase = 1
print(world.w) | 721 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( __A : Tuple ):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'mock-s3-bucket'
snake_case: int = f"""s3://{mock_bucket}"""
snake_case: Any = extract_path_from_uri(__A )
assert dataset_path.startswith('s3://' ) is False
snake_case: Union[str, Any] = './local/path'
snake_case: Union[str, Any] = extract_path_from_uri(__A )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( __A : Any ):
'''simple docstring'''
snake_case: List[str] = is_remote_filesystem(__A )
assert is_remote is True
snake_case: int = fsspec.filesystem('file' )
snake_case: int = is_remote_filesystem(__A )
assert is_remote is False
@pytest.mark.parametrize('compression_fs_class' , __A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : int , __A : str , __A : Optional[Any] , __A : List[str] , __A : Optional[Any] , __A : Optional[int] ):
'''simple docstring'''
snake_case: Optional[Any] = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
snake_case: Optional[int] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case: str = f"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=__A )
assert isinstance(__A , __A )
snake_case: Any = os.path.basename(__A )
snake_case: int = expected_filename[: expected_filename.rindex('.' )]
assert fs.glob('*' ) == [expected_filename]
with fs.open(__A , 'r' , encoding='utf-8' ) as f, open(__A , encoding='utf-8' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol' , ['zip', 'gzip'] )
def lowerCAmelCase_ ( __A : Any , __A : int , __A : int ):
'''simple docstring'''
snake_case: List[str] = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
snake_case: str = compressed_file_paths[protocol]
snake_case: Dict = 'dataset.jsonl'
snake_case: Optional[Any] = f"""{protocol}://{member_file_path}::{compressed_file_path}"""
snake_case , *snake_case: List[Any] = fsspec.get_fs_token_paths(__A )
assert fs.isfile(__A )
assert not fs.isfile('non_existing_' + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : Union[str, Any] , __A : List[Any] ):
'''simple docstring'''
snake_case: Tuple = hf_api.dataset_info(__A , token=__A )
snake_case: List[str] = HfFileSystem(repo_info=__A , token=__A )
assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
assert hffs.isdir('data' )
assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
with open(__A ) as f:
assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read()
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Union[str, Any] = 'bz2'
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(__A , __A , clobber=__A )
with pytest.warns(__A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(__A ) == 1
assert (
str(warning_info[0].message )
== f"""A filesystem protocol was already set for {protocol} and will be overwritten."""
) | 692 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
set_seed(770)
__UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
__UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
__UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
__UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def lowerCAmelCase_ ( __A : List[str] , __A : Tuple=False ):
'''simple docstring'''
snake_case: str = model_type
if use_small:
key += "_small"
return os.path.join(__A , REMOTE_MODEL_PATHS[key]['file_name'] )
def lowerCAmelCase_ ( __A : Dict , __A : str ):
'''simple docstring'''
os.makedirs(__A , exist_ok=__A )
hf_hub_download(repo_id=__A , filename=__A , local_dir=__A )
def lowerCAmelCase_ ( __A : str , __A : Optional[Any] , __A : Union[str, Any]=False , __A : Union[str, Any]="text" ):
'''simple docstring'''
if model_type == "text":
snake_case: Union[str, Any] = BarkSemanticModel
snake_case: str = BarkSemanticConfig
snake_case: Union[str, Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case: Any = BarkCoarseModel
snake_case: Tuple = BarkCoarseConfig
snake_case: List[Any] = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case: Union[str, Any] = BarkFineModel
snake_case: Union[str, Any] = BarkFineConfig
snake_case: str = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case: int = f"""{model_type}_small""" if use_small else model_type
snake_case: List[Any] = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__A ):
logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
_download(model_info['repo_id'] , model_info['file_name'] )
snake_case: Dict = torch.load(__A , map_location=__A )
# this is a hack
snake_case: Tuple = checkpoint['model_args']
if "input_vocab_size" not in model_args:
snake_case: List[Any] = model_args['vocab_size']
snake_case: int = model_args['vocab_size']
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case: Dict = model_args.pop('n_head' )
snake_case: Tuple = model_args.pop('n_embd' )
snake_case: List[Any] = model_args.pop('n_layer' )
snake_case: Any = ConfigClass(**checkpoint['model_args'] )
snake_case: Tuple = ModelClass(config=__A )
snake_case: int = GenerationConfigClass()
snake_case: List[str] = model_generation_config
snake_case: Optional[Any] = checkpoint['model']
# fixup checkpoint
snake_case: List[Any] = '_orig_mod.'
for k, v in list(state_dict.items() ):
if k.startswith(__A ):
# replace part of the key with corresponding layer name in HF implementation
snake_case: Union[str, Any] = k[len(__A ) :]
for old_layer_name in new_layer_name_dict:
snake_case: Optional[int] = new_k.replace(__A , new_layer_name_dict[old_layer_name] )
snake_case: Optional[int] = state_dict.pop(__A )
snake_case: Union[str, Any] = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case: List[Any] = {k for k in extra_keys if not k.endswith('.attn.bias' )}
snake_case: Dict = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case: Tuple = {k for k in missing_keys if not k.endswith('.attn.bias' )}
if len(__A ) != 0:
raise ValueError(f"""extra keys found: {extra_keys}""" )
if len(__A ) != 0:
raise ValueError(f"""missing keys: {missing_keys}""" )
model.load_state_dict(__A , strict=__A )
snake_case: int = model.num_parameters(exclude_embeddings=__A )
snake_case: int = checkpoint['best_val_loss'].item()
logger.info(f"""model loaded: {round(n_params/1E6 , 1 )}M params, {round(__A , 3 )} loss""" )
model.eval()
model.to(__A )
del checkpoint, state_dict
return model
def lowerCAmelCase_ ( __A : str , __A : int=False , __A : Union[str, Any]="text" ):
'''simple docstring'''
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case: Dict = 'cpu' # do conversion on cpu
snake_case: List[Any] = _get_ckpt_path(__A , use_small=__A )
snake_case: Tuple = _load_model(__A , __A , model_type=__A , use_small=__A )
# load bark initial model
snake_case: List[str] = _bark_load_model(__A , 'cpu' , model_type=__A , use_small=__A )
if model_type == "text":
snake_case: str = bark_model['model']
if model.num_parameters(exclude_embeddings=__A ) != bark_model.get_num_params():
raise ValueError('initial and new models don\'t have the same number of parameters' )
# check if same output as the bark model
snake_case: List[Any] = 5
snake_case: str = 10
if model_type in ["text", "coarse"]:
snake_case: List[Any] = torch.randint(2_56 , (batch_size, sequence_length) , dtype=torch.int )
snake_case: List[Any] = bark_model(__A )[0]
snake_case: Optional[Any] = model(__A )
# take last logits
snake_case: Optional[Any] = output_new_model_total.logits[:, [-1], :]
else:
snake_case: Optional[int] = 3
snake_case: str = 8
snake_case: Optional[Any] = torch.randint(2_56 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case: Optional[int] = model(__A , __A )
snake_case: Dict = bark_model(__A , __A )
snake_case: Any = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError('initial and new outputs don\'t have the same shape' )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError('initial and new outputs are not equal' )
Path(__A ).mkdir(exist_ok=__A )
model.save_pretrained(__A )
def lowerCAmelCase_ ( __A : Optional[int] , __A : str , __A : int , __A : str , __A : Tuple , __A : Dict , ):
'''simple docstring'''
snake_case: List[Any] = os.path.join(__A , __A )
snake_case: List[Any] = BarkSemanticConfig.from_pretrained(os.path.join(__A , 'config.json' ) )
snake_case: Dict = BarkCoarseConfig.from_pretrained(os.path.join(__A , 'config.json' ) )
snake_case: Tuple = BarkFineConfig.from_pretrained(os.path.join(__A , 'config.json' ) )
snake_case: Tuple = EncodecConfig.from_pretrained('facebook/encodec_24khz' )
snake_case: Tuple = BarkSemanticModel.from_pretrained(__A )
snake_case: int = BarkCoarseModel.from_pretrained(__A )
snake_case: str = BarkFineModel.from_pretrained(__A )
snake_case: Tuple = EncodecModel.from_pretrained('facebook/encodec_24khz' )
snake_case: Optional[Any] = BarkConfig.from_sub_model_configs(
__A , __A , __A , __A )
snake_case: Optional[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case: List[Any] = BarkModel(__A )
snake_case: Any = semantic
snake_case: Union[str, Any] = coarseAcoustic
snake_case: Tuple = fineAcoustic
snake_case: Any = codec
snake_case: str = bark_generation_config
Path(__A ).mkdir(exist_ok=__A )
bark.save_pretrained(__A , repo_id=__A , push_to_hub=__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
__UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small) | 700 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
__UpperCamelCase = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
__UpperCamelCase = field(
default=1024 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the training data."} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
__UpperCamelCase = field(default=snake_case , metadata={"help": "A csv or a json file containing the test data."} )
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
snake_case: str = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case: Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=snake_case , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case , snake_case , snake_case: Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case: str = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
snake_case: Tuple = training_args.get_process_log_level()
logger.setLevel(__A )
datasets.utils.logging.set_verbosity(__A )
transformers.utils.logging.set_verbosity(__A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case: List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case: Optional[int] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case: Tuple = data_args.train_file.split('.' )[-1]
snake_case: Union[str, Any] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case: Union[str, Any] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
snake_case: List[Any] = load_dataset('csv' , data_files=__A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case: Optional[Any] = load_dataset('json' , data_files=__A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case: Tuple = raw_datasets['train'].features['label'].names
snake_case: List[str] = len(__A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case: List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
snake_case: Union[str, Any] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case: int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case: Union[str, Any] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case: Optional[Any] = {'Refused': 0, 'Entailed': 1}
snake_case: List[Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case: List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__A : Any ):
# Tokenize the texts
def _convert_table_text_to_pandas(__A : Dict ):
snake_case: str = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
snake_case: List[str] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case: str = examples['statement']
snake_case: int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
snake_case: List[Any] = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A )
snake_case: List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
snake_case: int = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case: List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case: Tuple = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case: Any = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case: Optional[int] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
snake_case: str = raw_datasets['test']
if data_args.max_predict_samples is not None:
snake_case: List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A : EvalPrediction ):
snake_case: int = p.predictions[0] if isinstance(p.predictions , __A ) else p.predictions
snake_case: List[str] = np.argmax(__A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case: str = default_data_collator
elif training_args.fpaa:
snake_case: List[str] = DataCollatorWithPadding(__A , pad_to_multiple_of=8 )
else:
snake_case: List[Any] = None
# Initialize our Trainer
snake_case: List[str] = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
snake_case: Optional[int] = None
if training_args.resume_from_checkpoint is not None:
snake_case: str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case: Optional[Any] = last_checkpoint
snake_case: Union[str, Any] = trainer.train(resume_from_checkpoint=__A )
snake_case: List[Any] = train_result.metrics
snake_case: List[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A )
)
snake_case: Optional[Any] = min(__A , len(__A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , __A )
trainer.save_metrics('train' , __A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case: Dict = trainer.evaluate(eval_dataset=__A )
snake_case: Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A )
snake_case: Dict = min(__A , len(__A ) )
trainer.log_metrics('eval' , __A )
trainer.save_metrics('eval' , __A )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case: Optional[int] = predict_dataset.remove_columns('label' )
snake_case: str = trainer.predict(__A , metric_key_prefix='predict' ).predictions
snake_case: Any = np.argmax(__A , axis=1 )
snake_case: int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(__A , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(__A ):
snake_case: int = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
snake_case: Optional[int] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**__A )
else:
trainer.create_model_card(**__A )
def lowerCAmelCase_ ( __A : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 692 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
'''simple docstring'''
__UpperCamelCase = "focalnet"
def __init__( self , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=96 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=[1_92, 3_84, 7_68, 7_68] , SCREAMING_SNAKE_CASE__=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__=[2, 2, 2, 2] , SCREAMING_SNAKE_CASE__=[3, 3, 3, 3] , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=4.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1E-4 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = image_size
snake_case: Tuple = patch_size
snake_case: str = num_channels
snake_case: Dict = embed_dim
snake_case: str = use_conv_embed
snake_case: Tuple = hidden_sizes
snake_case: int = depths
snake_case: List[str] = focal_levels
snake_case: Any = focal_windows
snake_case: Any = hidden_act
snake_case: Optional[int] = mlp_ratio
snake_case: List[str] = hidden_dropout_prob
snake_case: Optional[Any] = drop_path_rate
snake_case: Dict = use_layerscale
snake_case: Union[str, Any] = layerscale_value
snake_case: List[Any] = use_post_layernorm
snake_case: int = use_post_layernorm_in_modulation
snake_case: Optional[Any] = normalize_modulator
snake_case: int = initializer_range
snake_case: Union[str, Any] = layer_norm_eps
snake_case: Optional[int] = encoder_stride
snake_case: Any = ['stem'] + [F"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
snake_case: List[str] = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE__ , out_indices=SCREAMING_SNAKE_CASE__ , stage_names=self.stage_names ) | 701 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( __A : float = 0.1 ):
'''simple docstring'''
snake_case: Optional[int] = 3
snake_case: int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__A )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 | 0 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def lowerCAmelCase_ ( __A : int , __A : List[str] , __A : Dict , __A : Optional[Any] , __A : Optional[int] , __A : int , __A : Any , __A : List[str] , __A : Union[str, Any] , __A : List[Any] , __A : Union[str, Any] , __A : List[Any] , ):
'''simple docstring'''
snake_case: Optional[Any] = {
'7z': (seven_zip_file, SevenZipExtractor),
'bz2': (bza_file, BzipaExtractor),
'gzip': (gz_file, GzipExtractor),
'lz4': (lza_file, LzaExtractor),
'tar': (tar_file, TarExtractor),
'xz': (xz_file, XzExtractor),
'zip': (zip_file, ZipExtractor),
'zstd': (zstd_file, ZstdExtractor),
}
snake_case: List[str] = input_paths_and_base_extractors[compression_format]
if input_path is None:
snake_case: str = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
assert base_extractor.is_extractable(__A )
snake_case: str = tmp_path / ('extracted' if is_archive else 'extracted.txt')
base_extractor.extract(__A , __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case: Optional[int] = file_path.read_text(encoding='utf-8' )
else:
snake_case: Union[str, Any] = output_path.read_text(encoding='utf-8' )
snake_case: Optional[Any] = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def lowerCAmelCase_ ( __A : List[str] , __A : Optional[Any] , __A : int , __A : Optional[int] , __A : Any , __A : Union[str, Any] , __A : str , __A : Tuple , __A : str , __A : Union[str, Any] , __A : str , __A : Optional[int] , ):
'''simple docstring'''
snake_case: List[Any] = {
'7z': seven_zip_file,
'bz2': bza_file,
'gzip': gz_file,
'lz4': lza_file,
'tar': tar_file,
'xz': xz_file,
'zip': zip_file,
'zstd': zstd_file,
}
snake_case: Dict = input_paths[compression_format]
if input_path is None:
snake_case: Any = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
snake_case: List[Any] = Extractor.infer_extractor_format(__A )
assert extractor_format is not None
snake_case: Tuple = tmp_path / ('extracted' if is_archive else 'extracted.txt')
Extractor.extract(__A , __A , __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case: str = file_path.read_text(encoding='utf-8' )
else:
snake_case: Any = output_path.read_text(encoding='utf-8' )
snake_case: Optional[Any] = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCAmelCase_ ( __A : List[str] , __A : Dict ):
'''simple docstring'''
import tarfile
snake_case: List[str] = tmp_path / 'data_dot_dot'
directory.mkdir()
snake_case: Optional[int] = directory / 'tar_file_with_dot_dot.tar'
with tarfile.TarFile(__A , 'w' ) as f:
f.add(__A , arcname=os.path.join('..' , text_file.name ) )
return path
@pytest.fixture
def lowerCAmelCase_ ( __A : Optional[int] ):
'''simple docstring'''
import tarfile
snake_case: List[str] = tmp_path / 'data_sym_link'
directory.mkdir()
snake_case: Optional[int] = directory / 'tar_file_with_sym_link.tar'
os.symlink('..' , directory / 'subdir' , target_is_directory=__A )
with tarfile.TarFile(__A , 'w' ) as f:
f.add(str(directory / 'subdir' ) , arcname='subdir' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'insecure_tar_file, error_log' , [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')] , )
def lowerCAmelCase_ ( __A : str , __A : List[Any] , __A : Optional[Any] , __A : int , __A : Dict , __A : Any ):
'''simple docstring'''
snake_case: Tuple = {
'tar_file_with_dot_dot': tar_file_with_dot_dot,
'tar_file_with_sym_link': tar_file_with_sym_link,
}
snake_case: str = insecure_tar_files[insecure_tar_file]
snake_case: str = tmp_path / 'extracted'
TarExtractor.extract(__A , __A )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCAmelCase_ ( __A : Union[str, Any] ):
'''simple docstring'''
snake_case: Optional[int] = tmpdir / 'not_a_zip_file'
# From: https://github.com/python/cpython/pull/5053
snake_case: Dict = (
b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
b'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
b'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
b'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
)
with not_a_zip_file.open('wb' ) as f:
f.write(__A )
assert zipfile.is_zipfile(str(__A ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__A ) # but we're right
| 702 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] ) | 692 | 0 |
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