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import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowerCamelCase =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A__ :
_UpperCAmelCase : str = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Model type selected in the list: """ + """, """.join(__SCREAMING_SNAKE_CASE)})
_UpperCAmelCase : str = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""})
_UpperCAmelCase : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_UpperCAmelCase : int = field(
default=128 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
_UpperCAmelCase : int = field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
_UpperCAmelCase : int = field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
_UpperCAmelCase : bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
_UpperCAmelCase : bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""})
_UpperCAmelCase : float = field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""})
_UpperCAmelCase : int = field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""})
_UpperCAmelCase : int = field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
_UpperCAmelCase : int = field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""})
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Tuple = """train"""
_UpperCAmelCase : Any = """dev"""
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : SquadDataTrainingArguments
_UpperCAmelCase : List[SquadFeatures]
_UpperCAmelCase : Split
_UpperCAmelCase : bool
def __init__( self , __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = Split.train , __magic_name__ = False , __magic_name__ = None , __magic_name__ = "pt" , ):
lowerCamelCase : Optional[int] = args
lowerCamelCase : Tuple = is_language_sensitive
lowerCamelCase : int = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__magic_name__ , __magic_name__ ):
try:
lowerCamelCase : Tuple = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
lowerCamelCase : Dict = mode
# Load data features from cache or dataset file
lowerCamelCase : Tuple = """v2""" if args.version_2_with_negative else """v1"""
lowerCamelCase : List[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCamelCase : Any = cached_features_file + """.lock"""
with FileLock(__magic_name__ ):
if os.path.exists(__magic_name__ ) and not args.overwrite_cache:
lowerCamelCase : int = time.time()
lowerCamelCase : List[str] = torch.load(__magic_name__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowerCamelCase : Dict = self.old_features["""features"""]
lowerCamelCase : List[Any] = self.old_features.get("""dataset""" , __magic_name__ )
lowerCamelCase : List[Any] = self.old_features.get("""examples""" , __magic_name__ )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
""" future run""" )
else:
if mode == Split.dev:
lowerCamelCase : int = self.processor.get_dev_examples(args.data_dir )
else:
lowerCamelCase : Optional[Any] = self.processor.get_train_examples(args.data_dir )
lowerCamelCase , lowerCamelCase : List[str] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=__magic_name__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=__magic_name__ , )
lowerCamelCase : List[Any] = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , __magic_name__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ):
return len(self.features )
def __getitem__( self , __magic_name__ ):
# Convert to Tensors and build dataset
lowerCamelCase : Tuple = self.features[i]
lowerCamelCase : Union[str, Any] = torch.tensor(feature.input_ids , dtype=torch.long )
lowerCamelCase : Tuple = torch.tensor(feature.attention_mask , dtype=torch.long )
lowerCamelCase : Dict = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowerCamelCase : Any = torch.tensor(feature.cls_index , dtype=torch.long )
lowerCamelCase : int = torch.tensor(feature.p_mask , dtype=torch.float )
lowerCamelCase : Any = torch.tensor(feature.is_impossible , dtype=torch.float )
lowerCamelCase : Optional[int] = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowerCamelCase : List[Any] = torch.tensor(feature.start_position , dtype=torch.long )
lowerCamelCase : Dict = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = """gpt_neo"""
_UpperCAmelCase : Union[str, Any] = ["""past_key_values"""]
_UpperCAmelCase : List[Any] = {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , __magic_name__=5_0_2_5_7 , __magic_name__=2_0_4_8 , __magic_name__=2_0_4_8 , __magic_name__=2_4 , __magic_name__=[[["global", "local"], 1_2]] , __magic_name__=1_6 , __magic_name__=None , __magic_name__=2_5_6 , __magic_name__="gelu_new" , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__=1e-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=5_0_2_5_6 , __magic_name__=5_0_2_5_6 , **__magic_name__ , ):
lowerCamelCase : List[Any] = vocab_size
lowerCamelCase : str = max_position_embeddings
lowerCamelCase : str = hidden_size
lowerCamelCase : Optional[int] = num_layers
lowerCamelCase : str = num_heads
lowerCamelCase : Optional[Any] = intermediate_size
lowerCamelCase : List[Any] = window_size
lowerCamelCase : int = activation_function
lowerCamelCase : Union[str, Any] = resid_dropout
lowerCamelCase : List[Any] = embed_dropout
lowerCamelCase : List[str] = attention_dropout
lowerCamelCase : Dict = classifier_dropout
lowerCamelCase : Any = layer_norm_epsilon
lowerCamelCase : Dict = initializer_range
lowerCamelCase : Dict = use_cache
lowerCamelCase : Optional[Any] = bos_token_id
lowerCamelCase : int = eos_token_id
lowerCamelCase : List[Any] = attention_types
lowerCamelCase : Optional[Any] = self.expand_attention_types_params(__magic_name__ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.attention_layers)` == `config.num_layers` """
F'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, '''
F'''`config.num_layers = {self.num_layers}`. '''
"""`config.attention_layers` is prepared using `config.attention_types`. """
"""Please verify the value of `config.attention_types` argument.""" )
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
@staticmethod
def UpperCamelCase__ ( __magic_name__ ):
lowerCamelCase : Optional[int] = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
import torch
lowerCamelCase : Any = input.size()
lowerCamelCase : List[Any] = len(lowerCamelCase )
lowerCamelCase : Optional[Any] = shape[dimension]
lowerCamelCase : Optional[int] = torch.arange(0, lowerCamelCase, lowerCamelCase )
lowerCamelCase : Dict = torch.div(sizedim - size, lowerCamelCase, rounding_mode="""floor""" ) + 1
lowerCamelCase : int = torch.arange(lowerCamelCase ) + low_indices[:min_length][:, None]
lowerCamelCase : str = [slice(lowerCamelCase )] * rank
lowerCamelCase : List[str] = indices
lowerCamelCase : Dict = input[s]
lowerCamelCase : Any = list(range(0, rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(lowerCamelCase )
def _a ( lowerCamelCase, lowerCamelCase ):
import torch
lowerCamelCase : List[Any] = torch.arange(1, lowerCamelCase )
lowerCamelCase : Optional[int] = torch.remainder(lowerCamelCase, lowerCamelCase )
lowerCamelCase : List[Any] = remainders == 0
lowerCamelCase : List[Any] = candidates[divisor_indices]
lowerCamelCase : Optional[Any] = torch.max(lowerCamelCase )
return largest_divisor, torch.div(lowerCamelCase, lowerCamelCase, rounding_mode="""floor""" )
class A__ ( __SCREAMING_SNAKE_CASE):
@property
def UpperCamelCase__ ( self ):
lowerCamelCase : str = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="""inputs""" )
lowerCamelCase : int = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase : Tuple = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase__ ( self ):
return self._config.num_heads
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = False , __magic_name__ = None , ):
lowerCamelCase : Optional[int] = super(__magic_name__ , self ).generate_dummy_inputs(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
# We need to order the input in the way they appears in the forward()
lowerCamelCase : int = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase , lowerCamelCase : Optional[Any] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase : Optional[int] = seqlen + 2
lowerCamelCase : List[Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase : str = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(self.num_layers )
]
lowerCamelCase : Tuple = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase : str = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase : Any = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
return ordered_inputs
@property
def UpperCamelCase__ ( self ):
return 1_3
| 287 | 1 |
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()
lowercase : str = logging.get_logger(__name__)
set_seed(7_7_0)
lowercase : Optional[int] = {
"""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""",
}
lowercase : Tuple = {
"""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""",
},
}
lowercase : Union[str, Any] = os.path.dirname(os.path.abspath(__file__))
lowercase : Optional[Any] = os.path.join(os.path.expanduser("""~"""), """.cache""")
lowercase : Any = os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def A_ ( A__ , A__=False ) -> Dict:
a__ : Optional[int] = model_type
if use_small:
key += "_small"
return os.path.join(A__ , REMOTE_MODEL_PATHS[key]['file_name'] )
def A_ ( A__ , A__ ) -> List[str]:
os.makedirs(A__ , exist_ok=A__ )
hf_hub_download(repo_id=A__ , filename=A__ , local_dir=A__ )
def A_ ( A__ , A__ , A__=False , A__="text" ) -> Optional[int]:
if model_type == "text":
a__ : Union[str, Any] = BarkSemanticModel
a__ : Tuple = BarkSemanticConfig
a__ : Union[str, Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
a__ : Tuple = BarkCoarseModel
a__ : int = BarkCoarseConfig
a__ : Dict = BarkCoarseGenerationConfig
elif model_type == "fine":
a__ : Dict = BarkFineModel
a__ : Dict = BarkFineConfig
a__ : Optional[int] = BarkFineGenerationConfig
else:
raise NotImplementedError()
a__ : int = F'{model_type}_small' if use_small else model_type
a__ : List[str] = 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'] )
a__ : Dict = torch.load(A__ , map_location=A__ )
# this is a hack
a__ : Optional[int] = checkpoint['model_args']
if "input_vocab_size" not in model_args:
a__ : List[str] = model_args['vocab_size']
a__ : Optional[int] = model_args['vocab_size']
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
a__ : Tuple = model_args.pop('n_head' )
a__ : List[Any] = model_args.pop('n_embd' )
a__ : Union[str, Any] = model_args.pop('n_layer' )
a__ : Union[str, Any] = ConfigClass(**checkpoint['model_args'] )
a__ : List[str] = ModelClass(config=A__ )
a__ : int = GenerationConfigClass()
a__ : Dict = model_generation_config
a__ : Union[str, Any] = checkpoint['model']
# fixup checkpoint
a__ : Tuple = '_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
a__ : Dict = k[len(A__ ) :]
for old_layer_name in new_layer_name_dict:
a__ : List[str] = new_k.replace(A__ , new_layer_name_dict[old_layer_name] )
a__ : Optional[int] = state_dict.pop(A__ )
a__ : int = set(state_dict.keys() ) - set(model.state_dict().keys() )
a__ : Optional[int] = {k for k in extra_keys if not k.endswith('.attn.bias' )}
a__ : int = set(model.state_dict().keys() ) - set(state_dict.keys() )
a__ : Union[str, Any] = {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__ )
a__ : Union[str, Any] = model.num_parameters(exclude_embeddings=A__ )
a__ : Optional[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 A_ ( A__ , A__=False , A__="text" ) -> Tuple:
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
a__ : Union[str, Any] = 'cpu' # do conversion on cpu
a__ : List[str] = _get_ckpt_path(A__ , use_small=A__ )
a__ : Any = _load_model(A__ , A__ , model_type=A__ , use_small=A__ )
# load bark initial model
a__ : Any = _bark_load_model(A__ , 'cpu' , model_type=A__ , use_small=A__ )
if model_type == "text":
a__ : Optional[Any] = 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
a__ : Any = 5
a__ : str = 10
if model_type in ["text", "coarse"]:
a__ : List[Any] = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
a__ : str = bark_model(A__ )[0]
a__ : Any = model(A__ )
# take last logits
a__ : Optional[Any] = output_new_model_total.logits[:, [-1], :]
else:
a__ : Optional[Any] = 3
a__ : List[str] = 8
a__ : Any = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
a__ : Optional[int] = model(A__ , A__ )
a__ : Union[str, Any] = bark_model(A__ , A__ )
a__ : Dict = 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 A_ ( A__ , A__ , A__ , A__ , A__ , A__ , ) -> int:
a__ : Optional[Any] = os.path.join(A__ , A__ )
a__ : Optional[int] = BarkSemanticConfig.from_pretrained(os.path.join(A__ , 'config.json' ) )
a__ : List[Any] = BarkCoarseConfig.from_pretrained(os.path.join(A__ , 'config.json' ) )
a__ : Optional[int] = BarkFineConfig.from_pretrained(os.path.join(A__ , 'config.json' ) )
a__ : Union[str, Any] = EncodecConfig.from_pretrained('facebook/encodec_24khz' )
a__ : List[str] = BarkSemanticModel.from_pretrained(A__ )
a__ : Dict = BarkCoarseModel.from_pretrained(A__ )
a__ : Optional[Any] = BarkFineModel.from_pretrained(A__ )
a__ : Dict = EncodecModel.from_pretrained('facebook/encodec_24khz' )
a__ : int = BarkConfig.from_sub_model_configs(
A__ , A__ , A__ , A__ )
a__ : List[str] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
a__ : Optional[int] = BarkModel(A__ )
a__ : Dict = semantic
a__ : List[Any] = coarseAcoustic
a__ : Dict = fineAcoustic
a__ : str = codec
a__ : str = bark_generation_config
Path(A__ ).mkdir(exist_ok=A__ )
bark.save_pretrained(A__ , repo_id=A__ , push_to_hub=A__ )
if __name__ == "__main__":
lowercase : Union[str, Any] = 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.""")
lowercase : Dict = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 225 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase : Union[str, Any] = data_utils.TransfoXLTokenizer
lowercase : Optional[int] = data_utils.TransfoXLCorpus
lowercase : List[Any] = data_utils
lowercase : Tuple = data_utils
def A_ ( A__ , A__ , A__ , A__ ) -> Optional[Any]:
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(A__ , 'rb' ) as fp:
a__ : int = pickle.load(A__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
a__ : int = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F'Save vocabulary to {pytorch_vocab_dump_path}' )
a__ : List[Any] = corpus.vocab.__dict__
torch.save(A__ , A__ )
a__ : Dict = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , A__ )
a__ : Optional[int] = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F'Save dataset to {pytorch_dataset_dump_path}' )
torch.save(A__ , A__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
a__ : Union[str, Any] = os.path.abspath(A__ )
a__ : Optional[Any] = os.path.abspath(A__ )
print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
a__ : Dict = TransfoXLConfig()
else:
a__ : Dict = TransfoXLConfig.from_json_file(A__ )
print(F'Building PyTorch model from configuration: {config}' )
a__ : Optional[int] = TransfoXLLMHeadModel(A__ )
a__ : int = load_tf_weights_in_transfo_xl(A__ , A__ , A__ )
# Save pytorch-model
a__ : Any = os.path.join(A__ , A__ )
a__ : Dict = os.path.join(A__ , A__ )
print(F'Save PyTorch model to {os.path.abspath(A__ )}' )
torch.save(model.state_dict() , A__ )
print(F'Save configuration file to {os.path.abspath(A__ )}' )
with open(A__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
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(
"""--tf_checkpoint_path""",
default="""""",
type=str,
help="""An optional path to a TensorFlow checkpoint path to be converted.""",
)
parser.add_argument(
"""--transfo_xl_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--transfo_xl_dataset_file""",
default="""""",
type=str,
help="""An optional dataset file to be converted in a vocabulary.""",
)
lowercase : Any = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 225 | 1 |
'''simple docstring'''
class _lowerCamelCase : # Public class to implement a graph
'''simple docstring'''
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
__magic_name__ : Tuple = row
__magic_name__ : str = col
__magic_name__ : Optional[Any] = graph
def __lowerCAmelCase ( self : Any , _A : int , _A : int , _A : list[list[bool]] ) -> bool:
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def __lowerCAmelCase ( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
# Checking all 8 elements surrounding nth element
__magic_name__ : List[str] = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
__magic_name__ : List[str] = [-1, 0, 1, -1, 1, -1, 0, 1]
__magic_name__ : Optional[int] = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _A ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _A )
def __lowerCAmelCase ( self : int ) -> int: # And finally, count all islands.
__magic_name__ : List[str] = [[False for j in range(self.COL )] for i in range(self.ROW )]
__magic_name__ : Any = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_A , _A , _A )
count += 1
return count
| 331 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Optional[Any] = ["""flax""", """transformers"""]
def __init__( self : Union[str, Any] , *_A : Dict , **_A : Any ) -> int:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , *_A : List[Any] , **_A : Any ) -> List[str]:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : List[str] , *_A : Tuple , **_A : Optional[int] ) -> int:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Union[str, Any] = ["""flax""", """transformers"""]
def __init__( self : Union[str, Any] , *_A : Any , **_A : int ) -> List[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Union[str, Any] , *_A : Optional[int] , **_A : Dict ) -> Optional[Any]:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Tuple , *_A : Any , **_A : Union[str, Any] ) -> Dict:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Dict = ["""flax""", """transformers"""]
def __init__( self : int , *_A : Optional[int] , **_A : Any ) -> List[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Any , *_A : int , **_A : str ) -> Any:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , *_A : Union[str, Any] , **_A : List[str] ) -> Optional[int]:
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Optional[int] = ["""flax""", """transformers"""]
def __init__( self : Tuple , *_A : Dict , **_A : str ) -> Optional[Any]:
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : str , *_A : Dict , **_A : Optional[Any] ) -> Dict:
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def __lowerCAmelCase ( cls : Any , *_A : List[str] , **_A : str ) -> Optional[int]:
requires_backends(cls , ['flax', 'transformers'] )
| 331 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : Optional[Any] = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPConfig''',
'''CLIPOnnxConfig''',
'''CLIPTextConfig''',
'''CLIPVisionConfig''',
],
'''processing_clip''': ['''CLIPProcessor'''],
'''tokenization_clip''': ['''CLIPTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['''CLIPTokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['''CLIPFeatureExtractor''']
A : Any = ['''CLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPModel''',
'''CLIPPreTrainedModel''',
'''CLIPTextModel''',
'''CLIPTextModelWithProjection''',
'''CLIPVisionModel''',
'''CLIPVisionModelWithProjection''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCLIPModel''',
'''TFCLIPPreTrainedModel''',
'''TFCLIPTextModel''',
'''TFCLIPVisionModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
'''FlaxCLIPModel''',
'''FlaxCLIPPreTrainedModel''',
'''FlaxCLIPTextModel''',
'''FlaxCLIPTextPreTrainedModel''',
'''FlaxCLIPVisionModel''',
'''FlaxCLIPVisionPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
A : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 276 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 276 | 1 |
from math import isclose, sqrt
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstring"""
__a =point_y / 4 / point_x
__a =2 * normal_gradient / (1 + normal_gradient * normal_gradient)
__a =(1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
__a =(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
__a =outgoing_gradient**2 + 4
__a =2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
__a =(point_y - outgoing_gradient * point_x) ** 2 - 100
__a =(
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
__a =(
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
__a =x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus
__a =point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCamelCase_( _snake_case : float = 1.4 , _snake_case : float = -9.6 ):
"""simple docstring"""
__a =0
__a =first_x_coord
__a =first_y_coord
__a =(10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
__a =next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 218 |
'''simple docstring'''
from math import isclose, sqrt
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]:
UpperCAmelCase : Optional[int] = point_y / 4 / point_x
UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4
UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100
UpperCAmelCase : List[str] = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
UpperCAmelCase : Optional[int] = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus
UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int:
UpperCAmelCase : int = 0
UpperCAmelCase : float = first_x_coord
UpperCAmelCase : float = first_y_coord
UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x)
while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"{solution() = }")
| 23 | 0 |
lowerCamelCase_ : dict[tuple[int, int, int], int] = {}
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
__a = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
__a = _calculate(days - 1 , __lowerCamelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
__a = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
__a = _calculate(days - 1 , __lowerCamelCase , 0 )
__a = state_late + state_absent + state_ontime
__a = prizestrings
return prizestrings
def lowerCAmelCase( __lowerCamelCase = 30 ):
return _calculate(__lowerCamelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 197 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class a__ ( __snake_case ):
A__ : Any = 'Wav2Vec2FeatureExtractor'
A__ : str = 'AutoTokenizer'
def __init__( self , UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
super().__init__(UpperCAmelCase , UpperCAmelCase )
__a = self.feature_extractor
__a = False
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , UpperCAmelCase , **UpperCAmelCase ) -> Dict:
try:
return super().from_pretrained(UpperCAmelCase , **UpperCAmelCase )
except OSError:
warnings.warn(
f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ' , UpperCAmelCase , )
__a = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
__a = WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
return cls(feature_extractor=UpperCAmelCase , tokenizer=UpperCAmelCase )
def __call__( self , *UpperCAmelCase , **UpperCAmelCase ) -> List[Any]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase , **UpperCAmelCase )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
__a = kwargs.pop('raw_speech' )
else:
__a = kwargs.pop('audio' , UpperCAmelCase )
__a = kwargs.pop('sampling_rate' , UpperCAmelCase )
__a = kwargs.pop('text' , UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
__a = args[0]
__a = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
__a = self.feature_extractor(UpperCAmelCase , *UpperCAmelCase , sampling_rate=UpperCAmelCase , **UpperCAmelCase )
if text is not None:
__a = self.tokenizer(UpperCAmelCase , **UpperCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
__a = encodings['input_ids']
return inputs
def __SCREAMING_SNAKE_CASE ( self , *UpperCAmelCase , **UpperCAmelCase ) -> Tuple:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*UpperCAmelCase , **UpperCAmelCase )
__a = kwargs.pop('input_features' , UpperCAmelCase )
__a = kwargs.pop('labels' , UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
__a = args[0]
__a = args[1:]
if input_features is not None:
__a = self.feature_extractor.pad(UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase )
if labels is not None:
__a = self.tokenizer.pad(UpperCAmelCase , **UpperCAmelCase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
__a = labels['input_ids']
return input_features
def __SCREAMING_SNAKE_CASE ( self , *UpperCAmelCase , **UpperCAmelCase ) -> List[str]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@contextmanager
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
__a = True
__a = self.tokenizer
yield
__a = self.feature_extractor
__a = False
| 197 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f"could not parse string as bool {string}" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
__A : Dict = parser.parse_args()
__A : Any = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 138 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A : Dict = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
__A : Optional[int] = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
__A : Dict = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def lowercase__ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ) , id='references' ),
} ) , )
def lowercase__ ( self : List[str] , UpperCAmelCase_ : List[List[List[str]]] , UpperCAmelCase_ : List[List[str]] , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=UpperCAmelCase_ , hypotheses=UpperCAmelCase_ , min_len=UpperCAmelCase_ , max_len=UpperCAmelCase_ )
}
| 138 | 1 |
__UpperCamelCase : Tuple = range(2, 20 + 1)
__UpperCamelCase : Tuple = [10**k for k in range(ks[-1] + 1)]
__UpperCamelCase : dict[int, dict[int, list[list[int]]]] = {}
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
a = sum(a_i[j] for j in range(__lowerCamelCase , len(__lowerCamelCase ) ) )
a = sum(a_i[j] * base[j] for j in range(min(len(__lowerCamelCase ) , __lowerCamelCase ) ) )
a , a = 0, 0
a = n - i
a = memo.get(__lowerCamelCase )
if sub_memo is not None:
a = sub_memo.get(__lowerCamelCase )
if jumps is not None and len(__lowerCamelCase ) > 0:
# find and make the largest jump without going over
a = -1
for _k in range(len(__lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
a = _k
break
if max_jump >= 0:
a , a , a = jumps[max_jump]
# since the difference between jumps is cached, add c
a = diff + c
for j in range(min(__lowerCamelCase , len(__lowerCamelCase ) ) ):
a , a = divmod(__lowerCamelCase , 10 )
if new_c > 0:
add(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
a = []
else:
a = {c: []}
a = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
a , a = next_term(__lowerCamelCase , k - 1 , i + dn , __lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
a , a = compute(__lowerCamelCase , __lowerCamelCase , i + dn , __lowerCamelCase )
diff += _diff
dn += terms_jumped
a = sub_memo[c]
# keep jumps sorted by # of terms skipped
a = 0
while j < len(__lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(__lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Tuple:
if i >= n:
return 0, i
if k > len(__lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(__lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
a = i
a , a , a = 0, 0, 0
for j in range(len(__lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
a = ds_c + ds_b
diff += addend
a = 0
for j in range(__lowerCamelCase ):
a = a_i[j] + addend
a , a = divmod(__lowerCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return diff, i - start_i
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Tuple:
for j in range(__lowerCamelCase , len(__lowerCamelCase ) ):
a = digits[j] + addend
if s >= 10:
a , a = divmod(__lowerCamelCase , 10 )
a = addend // 10 + quotient
else:
a = s
a = addend // 10
if addend == 0:
break
while addend > 0:
a , a = divmod(__lowerCamelCase , 10 )
digits.append(__lowerCamelCase )
def __A ( __lowerCamelCase = 10**15 ) -> int:
a = [1]
a = 1
a = 0
while True:
a , a = next_term(__lowerCamelCase , 20 , i + dn , __lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
a = 0
for j in range(len(__lowerCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'{solution() = }')
| 347 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :List[str] , __magic_name__ :List[str] , __magic_name__ :List[Any]=13 , __magic_name__ :Any=7 , __magic_name__ :Optional[int]=True , __magic_name__ :List[Any]=True , __magic_name__ :Optional[int]=True , __magic_name__ :Union[str, Any]=True , __magic_name__ :Any=99 , __magic_name__ :List[str]=32 , __magic_name__ :List[str]=5 , __magic_name__ :str=4 , __magic_name__ :str=37 , __magic_name__ :Optional[int]="gelu" , __magic_name__ :int=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :List[str]=512 , __magic_name__ :Tuple=16 , __magic_name__ :Tuple=2 , __magic_name__ :List[str]=0.02 , __magic_name__ :Any=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_choices
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = FlaxRoFormerModelTester(self )
@slow
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=__magic_name__ )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
a = jnp.array([[0, 1, 2, 3, 4, 5]] )
a = model(__magic_name__ )[0]
a = 5_0000
a = (1, 6, vocab_size)
self.assertEqual(output.shape , __magic_name__ )
a = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __magic_name__ , atol=1E-4 ) )
| 347 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case__ : List[str] = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `" / "`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `" // "`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `"train"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `"compressed"`)\n The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n `"compressed"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a "dummy" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n'
@add_start_docstrings(_lowerCamelCase )
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = """rag"""
lowerCAmelCase__ = True
def __init__(self :List[Any] , _UpperCamelCase :str=None , _UpperCamelCase :Dict=True , _UpperCamelCase :str=None , _UpperCamelCase :Any=None , _UpperCamelCase :Any=None , _UpperCamelCase :Optional[Any]=None , _UpperCamelCase :int=None , _UpperCamelCase :Union[str, Any]=" / " , _UpperCamelCase :Any=" // " , _UpperCamelCase :Optional[Any]=5 , _UpperCamelCase :Any=300 , _UpperCamelCase :List[str]=768 , _UpperCamelCase :Optional[int]=8 , _UpperCamelCase :Union[str, Any]="wiki_dpr" , _UpperCamelCase :int="train" , _UpperCamelCase :Any="compressed" , _UpperCamelCase :int=None , _UpperCamelCase :Union[str, Any]=None , _UpperCamelCase :List[Any]=False , _UpperCamelCase :Any=False , _UpperCamelCase :List[str]=0.0 , _UpperCamelCase :Any=True , _UpperCamelCase :Tuple=False , _UpperCamelCase :Optional[int]=False , _UpperCamelCase :int=False , _UpperCamelCase :List[Any]=True , _UpperCamelCase :List[Any]=None , **_UpperCamelCase :List[str] , )-> List[Any]:
super().__init__(
bos_token_id=_UpperCamelCase , pad_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , decoder_start_token_id=_UpperCamelCase , forced_eos_token_id=_UpperCamelCase , is_encoder_decoder=_UpperCamelCase , prefix=_UpperCamelCase , vocab_size=_UpperCamelCase , **_UpperCamelCase , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
__A = kwargs.pop('''question_encoder''' )
__A = question_encoder_config.pop('''model_type''' )
__A = kwargs.pop('''generator''' )
__A = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
__A = AutoConfig.for_model(_UpperCamelCase , **_UpperCamelCase )
__A = AutoConfig.for_model(_UpperCamelCase , **_UpperCamelCase )
__A = reduce_loss
__A = label_smoothing
__A = exclude_bos_score
__A = do_marginalize
__A = title_sep
__A = doc_sep
__A = n_docs
__A = max_combined_length
__A = dataset
__A = dataset_split
__A = index_name
__A = retrieval_vector_size
__A = retrieval_batch_size
__A = passages_path
__A = index_path
__A = use_dummy_dataset
__A = output_retrieved
__A = do_deduplication
__A = use_cache
if self.forced_eos_token_id is None:
__A = getattr(self.generator , '''forced_eos_token_id''' , _UpperCamelCase )
@classmethod
def _lowerCAmelCase (cls :List[str] , _UpperCamelCase :PretrainedConfig , _UpperCamelCase :PretrainedConfig , **_UpperCamelCase :int )-> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **_UpperCamelCase )
def _lowerCAmelCase (self :List[Any] )-> int:
__A = copy.deepcopy(self.__dict__ )
__A = self.question_encoder.to_dict()
__A = self.generator.to_dict()
__A = self.__class__.model_type
return output
| 117 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
snake_case__ : int = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def _a ( lowerCamelCase: List[Any] ) -> List[Any]:
'''simple docstring'''
if isinstance(lowerCamelCase , torch.Tensor ):
return image
elif isinstance(lowerCamelCase , PIL.Image.Image ):
__A = [image]
__A = [trans(img.convert('''RGB''' ) ) for img in image]
__A = torch.stack(lowerCamelCase )
return image
class A_ ( _lowerCamelCase ):
def __init__(self :List[str] , _UpperCamelCase :List[Any] , _UpperCamelCase :List[str] )-> List[Any]:
super().__init__()
# make sure scheduler can always be converted to DDIM
__A = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_UpperCamelCase , scheduler=_UpperCamelCase )
def _lowerCAmelCase (self :int , _UpperCamelCase :Optional[Any] )-> Union[str, Any]:
if strength < 0 or strength > 1:
raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )
def _lowerCAmelCase (self :Optional[Any] , _UpperCamelCase :Dict , _UpperCamelCase :List[str] , _UpperCamelCase :List[str] )-> Union[str, Any]:
# get the original timestep using init_timestep
__A = min(int(num_inference_steps * strength ) , _UpperCamelCase )
__A = max(num_inference_steps - init_timestep , 0 )
__A = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _lowerCAmelCase (self :str , _UpperCamelCase :Tuple , _UpperCamelCase :List[str] , _UpperCamelCase :int , _UpperCamelCase :List[str] , _UpperCamelCase :int , _UpperCamelCase :Dict=None )-> List[str]:
if not isinstance(_UpperCamelCase , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )
__A = image.to(device=_UpperCamelCase , dtype=_UpperCamelCase )
if isinstance(_UpperCamelCase , _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
__A = init_latents.shape
__A = randn_tensor(_UpperCamelCase , generator=_UpperCamelCase , device=_UpperCamelCase , dtype=_UpperCamelCase )
# get latents
print('''add noise to latents at timestep''' , _UpperCamelCase )
__A = self.scheduler.add_noise(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
__A = init_latents
return latents
@torch.no_grad()
def __call__(self :List[str] , _UpperCamelCase :Union[torch.FloatTensor, PIL.Image.Image] = None , _UpperCamelCase :float = 0.8 , _UpperCamelCase :int = 1 , _UpperCamelCase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _UpperCamelCase :float = 0.0 , _UpperCamelCase :int = 50 , _UpperCamelCase :Optional[bool] = None , _UpperCamelCase :Optional[str] = "pil" , _UpperCamelCase :bool = True , )-> Union[ImagePipelineOutput, Tuple]:
self.check_inputs(_UpperCamelCase )
# 2. Preprocess image
__A = preprocess(_UpperCamelCase )
# 3. set timesteps
self.scheduler.set_timesteps(_UpperCamelCase , device=self.device )
__A , __A = self.get_timesteps(_UpperCamelCase , _UpperCamelCase , self.device )
__A = timesteps[:1].repeat(_UpperCamelCase )
# 4. Prepare latent variables
__A = self.prepare_latents(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , self.unet.dtype , self.device , _UpperCamelCase )
__A = latents
# 5. Denoising loop
for t in self.progress_bar(_UpperCamelCase ):
# 1. predict noise model_output
__A = self.unet(_UpperCamelCase , _UpperCamelCase ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__A = self.scheduler.step(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , eta=_UpperCamelCase , use_clipped_model_output=_UpperCamelCase , generator=_UpperCamelCase , ).prev_sample
__A = (image / 2 + 0.5).clamp(0 , 1 )
__A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__A = self.numpy_to_pil(_UpperCamelCase )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_UpperCamelCase )
| 117 | 1 |
"""simple docstring"""
from math import pi
def lowerCamelCase (a_ :int , a_ :int) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 172 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
UpperCAmelCase = {'''facebook/blenderbot-3B''': 128}
class __magic_name__ ( __UpperCAmelCase ):
__A : Any = VOCAB_FILES_NAMES
__A : List[str] = PRETRAINED_VOCAB_FILES_MAP
__A : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Optional[int] = ["input_ids", "attention_mask"]
__A : Optional[Any] = BlenderbotTokenizer
def __init__( self : Optional[Any] , snake_case__ : List[str]=None , snake_case__ : List[str]=None , snake_case__ : List[Any]=None , snake_case__ : Dict="replace" , snake_case__ : Union[str, Any]="<s>" , snake_case__ : Tuple="</s>" , snake_case__ : Any="</s>" , snake_case__ : Any="<s>" , snake_case__ : Tuple="<unk>" , snake_case__ : str="<pad>" , snake_case__ : List[str]="<mask>" , snake_case__ : int=False , snake_case__ : List[Any]=True , **snake_case__ : Any , ):
'''simple docstring'''
super().__init__(
snake_case__ , snake_case__ , tokenizer_file=snake_case__ , errors=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , sep_token=snake_case__ , cls_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , add_prefix_space=snake_case__ , trim_offsets=snake_case__ , **snake_case__ , )
lowercase :Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , snake_case__ ) != add_prefix_space:
lowercase :int = getattr(snake_case__ , pre_tok_state.pop('''type''' ) )
lowercase :List[str] = add_prefix_space
lowercase :Any = pre_tok_class(**snake_case__ )
lowercase :Tuple = add_prefix_space
lowercase :List[Any] = '''post_processor'''
lowercase :Optional[Any] = getattr(self.backend_tokenizer , snake_case__ , snake_case__ )
if tokenizer_component_instance:
lowercase :int = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowercase :List[Any] = tuple(state['''sep'''] )
if "cls" in state:
lowercase :List[str] = tuple(state['''cls'''] )
lowercase :Dict = False
if state.get('''add_prefix_space''' , snake_case__ ) != add_prefix_space:
lowercase :str = add_prefix_space
lowercase :int = True
if state.get('''trim_offsets''' , snake_case__ ) != trim_offsets:
lowercase :List[str] = trim_offsets
lowercase :Optional[Any] = True
if changes_to_apply:
lowercase :Optional[Any] = getattr(snake_case__ , state.pop('''type''' ) )
lowercase :List[Any] = component_class(**snake_case__ )
setattr(self.backend_tokenizer , snake_case__ , snake_case__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __snake_case ( self : Dict ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __snake_case ( self : Dict , snake_case__ : Union[str, Any] ):
'''simple docstring'''
lowercase :Tuple = AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else value
lowercase :List[str] = value
def __snake_case ( self : int , *snake_case__ : Optional[int] , **snake_case__ : Tuple ):
'''simple docstring'''
lowercase :int = kwargs.get('''is_split_into_words''' , snake_case__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*snake_case__ , **snake_case__ )
def __snake_case ( self : List[Any] , *snake_case__ : Optional[Any] , **snake_case__ : str ):
'''simple docstring'''
lowercase :int = kwargs.get('''is_split_into_words''' , snake_case__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*snake_case__ , **snake_case__ )
def __snake_case ( self : Union[str, Any] , snake_case__ : str , snake_case__ : Optional[str] = None ):
'''simple docstring'''
lowercase :Union[str, Any] = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
def __snake_case ( self : str , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
lowercase :Optional[Any] = [self.sep_token_id]
lowercase :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __snake_case ( self : Optional[Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def __snake_case ( self : List[str] , snake_case__ : "Conversation" ):
'''simple docstring'''
lowercase :str = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(snake_case__ )
lowercase :Tuple = ''' '''.join(snake_case__ )
lowercase :Optional[int] = self.encode(snake_case__ )
if len(snake_case__ ) > self.model_max_length:
lowercase :Optional[int] = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 172 | 1 |
from __future__ import annotations
from typing import Any
def a_ ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 )
def a_ ( _A , _A , _A ) -> None:
"""simple docstring"""
if index == len(_A ):
print(_A )
return
create_state_space_tree(_A , _A , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(_A , _A , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
__UpperCamelCase : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["""A""", """B""", """C"""])
generate_all_subsequences(seq)
| 307 |
import os
import string
import sys
__UpperCamelCase : List[Any] = 1 << 8
__UpperCamelCase : Union[str, Any] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
__UpperCamelCase : Optional[Any] = KEYMAP["""up"""]
__UpperCamelCase : Tuple = KEYMAP["""left"""]
if sys.platform == "win32":
__UpperCamelCase : List[Any] = []
__UpperCamelCase : int = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
__UpperCamelCase : List[str] = ord(str(i))
def a_ ( ) -> Optional[int]:
"""simple docstring"""
if os.name == "nt":
import msvcrt
snake_case__ = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_A ) == 0:
# Read the keystroke
snake_case__ = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
snake_case__ = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
snake_case__ = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(_A )
if ord(_A ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
snake_case__ = chr(KEYMAP['esc'] )
except KeyError:
snake_case__ = cha[1]
else:
snake_case__ = ch.decode(_A )
else:
snake_case__ = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
snake_case__ = sys.stdin.fileno()
snake_case__ = termios.tcgetattr(_A )
try:
tty.setraw(_A )
snake_case__ = sys.stdin.read(1 )
finally:
termios.tcsetattr(_A , termios.TCSADRAIN , _A )
return ch
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ = get_raw_chars()
if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_A ) == KEYMAP["esc"]:
snake_case__ = get_raw_chars()
if ord(_A ) == KEYMAP["mod_int"]:
snake_case__ = get_raw_chars()
if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_A ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 307 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowercase : Tuple = Mapping[str, np.ndarray]
lowercase : List[Any] = Mapping[str, Any] # Is a nested dict.
lowercase : int = 0.01
@dataclasses.dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase_ :
'''simple docstring'''
A : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
A : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
A : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
A : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
A : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
A : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
A : Optional[str] = None
# Templates used to generate this protein (prediction-only)
A : Optional[Sequence[str]] = None
# Chain corresponding to each parent
A : Optional[Sequence[int]] = None
def lowerCAmelCase__ ( _a : str ):
snake_case_ : List[str] = R"(\[[A-Z]+\]\n)"
snake_case_ : List[str] = [tag.strip() for tag in re.split(_a , _a ) if len(_a ) > 0]
snake_case_ : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("\n" ) for l in tags[1::2]] )
snake_case_ : List[str] = ["N", "CA", "C"]
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
snake_case_ : List[str] = None
for g in groups:
if "[PRIMARY]" == g[0]:
snake_case_ : Any = g[1][0].strip()
for i in range(len(_a ) ):
if seq[i] not in residue_constants.restypes:
snake_case_ : Tuple = "X" # FIXME: strings are immutable
snake_case_ : int = np.array(
[residue_constants.restype_order.get(_a , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
snake_case_ : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_a , g[1][axis].split() ) ) )
snake_case_ : Union[str, Any] = np.array(_a )
snake_case_ : Union[str, Any] = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_a ):
snake_case_ : str = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
snake_case_ : Tuple = np.array(list(map({"-": 0, "+": 1}.get , g[1][0].strip() ) ) )
snake_case_ : int = np.zeros(
(
len(_a ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_a ):
snake_case_ : Dict = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_a , atom_mask=_a , aatype=_a , residue_index=np.arange(len(_a ) ) , b_factors=_a , )
def lowerCAmelCase__ ( _a : Protein , _a : int = 0 ):
snake_case_ : List[str] = []
snake_case_ : Union[str, Any] = prot.remark
if remark is not None:
pdb_headers.append(F'''REMARK {remark}''' )
snake_case_ : Union[str, Any] = prot.parents
snake_case_ : Union[str, Any] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
snake_case_ : List[Any] = [p for i, p in zip(_a , _a ) if i == chain_id]
if parents is None or len(_a ) == 0:
snake_case_ : List[Any] = ["N/A"]
pdb_headers.append(F'''PARENT {' '.join(_a )}''' )
return pdb_headers
def lowerCAmelCase__ ( _a : Protein , _a : str ):
snake_case_ : List[str] = []
snake_case_ : Tuple = pdb_str.split("\n" )
snake_case_ : Dict = prot.remark
if remark is not None:
out_pdb_lines.append(F'''REMARK {remark}''' )
snake_case_ : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
snake_case_ : Any = []
if prot.parents_chain_index is not None:
snake_case_ : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_a ) , [] )
parent_dict[str(_a )].append(_a )
snake_case_ : Tuple = max([int(_a ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
snake_case_ : str = parent_dict.get(str(_a ) , ["N/A"] )
parents_per_chain.append(_a )
else:
parents_per_chain.append(list(prot.parents ) )
else:
snake_case_ : Optional[Any] = [["N/A"]]
def make_parent_line(_a : Sequence[str] ) -> str:
return F'''PARENT {' '.join(_a )}'''
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
snake_case_ : List[str] = 0
for i, l in enumerate(_a ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_a )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_a ):
snake_case_ : str = parents_per_chain[chain_counter]
else:
snake_case_ : Union[str, Any] = ["N/A"]
out_pdb_lines.append(make_parent_line(_a ) )
return "\n".join(_a )
def lowerCAmelCase__ ( _a : Protein ):
snake_case_ : str = residue_constants.restypes + ["X"]
def res_atoa(_a : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , "UNK" )
snake_case_ : Any = residue_constants.atom_types
snake_case_ : List[str] = []
snake_case_ : Any = prot.atom_mask
snake_case_ : Optional[int] = prot.aatype
snake_case_ : Tuple = prot.atom_positions
snake_case_ : Union[str, Any] = prot.residue_index.astype(np.intaa )
snake_case_ : Dict = prot.b_factors
snake_case_ : List[str] = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
snake_case_ : List[str] = get_pdb_headers(_a )
if len(_a ) > 0:
pdb_lines.extend(_a )
snake_case_ : Optional[Any] = aatype.shape[0]
snake_case_ : Union[str, Any] = 1
snake_case_ : Any = 0
snake_case_ : Any = string.ascii_uppercase
snake_case_ : Any = None
# Add all atom sites.
for i in range(_a ):
snake_case_ : Any = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_a , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
snake_case_ : Tuple = "ATOM"
snake_case_ : List[str] = atom_name if len(_a ) == 4 else F''' {atom_name}'''
snake_case_ : Union[str, Any] = ""
snake_case_ : List[Any] = ""
snake_case_ : Dict = 1.00
snake_case_ : Dict = atom_name[0] # Protein supports only C, N, O, S, this works.
snake_case_ : List[str] = ""
snake_case_ : Tuple = "A"
if chain_index is not None:
snake_case_ : Optional[Any] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
snake_case_ : Tuple = (
F'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
F'''{res_name_a:>3} {chain_tag:>1}'''
F'''{residue_index[i]:>4}{insertion_code:>1} '''
F'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
F'''{occupancy:>6.2f}{b_factor:>6.2f} '''
F'''{element:>2}{charge:>2}'''
)
pdb_lines.append(_a )
atom_index += 1
snake_case_ : Dict = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
snake_case_ : Union[str, Any] = True
snake_case_ : Tuple = chain_index[i + 1]
if should_terminate:
# Close the chain.
snake_case_ : str = "TER"
snake_case_ : str = (
F'''{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'''
)
pdb_lines.append(_a )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_a , _a ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(_a )
def lowerCAmelCase__ ( _a : Protein ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def lowerCAmelCase__ ( _a : FeatureDict , _a : ModelOutput , _a : Optional[np.ndarray] = None , _a : Optional[np.ndarray] = None , _a : Optional[str] = None , _a : Optional[Sequence[str]] = None , _a : Optional[Sequence[int]] = None , ):
return Protein(
aatype=features["aatype"] , atom_positions=result["final_atom_positions"] , atom_mask=result["final_atom_mask"] , residue_index=features["residue_index"] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ) , chain_index=_a , remark=_a , parents=_a , parents_chain_index=_a , )
| 36 |
import argparse
import copy
def lowerCAmelCase__ ( _a : List[Any] ):
snake_case_ : List[Any] = {}
with open(_a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case_ : int = []
_list.append([line.split()[1], line.split()[2]] )
snake_case_ : Dict = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case_ : Dict = []
_list.append([line.split()[0], line.split()[2]] )
snake_case_ : int = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCAmelCase__ ( _a : Optional[Any] , _a : Optional[int] ):
with open(_a ) as f:
snake_case_ : List[str] = f.read(1 )
snake_case_ : Optional[Any] = start_node
snake_case_ : Optional[Any] = []
snake_case_ : Optional[int] = start_node
snake_case_ : int = 0
while visiting not in first_solution:
snake_case_ : List[str] = 1_00_00
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_a ) and k[0] not in first_solution:
snake_case_ : List[str] = k[1]
snake_case_ : Dict = k[0]
first_solution.append(_a )
snake_case_ : Dict = distance_of_first_solution + int(_a )
snake_case_ : Optional[int] = best_node
first_solution.append(_a )
snake_case_ : Optional[Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case_ : str = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_00_00
)
return first_solution, distance_of_first_solution
def lowerCAmelCase__ ( _a : Optional[int] , _a : List[str] ):
snake_case_ : Optional[Any] = []
for n in solution[1:-1]:
snake_case_ : Any = solution.index(_a )
for kn in solution[1:-1]:
snake_case_ : Any = solution.index(_a )
if n == kn:
continue
snake_case_ : Optional[int] = copy.deepcopy(_a )
snake_case_ : int = kn
snake_case_ : Any = n
snake_case_ : List[Any] = 0
for k in _tmp[:-1]:
snake_case_ : str = _tmp[_tmp.index(_a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case_ : Any = distance + int(i[1] )
_tmp.append(_a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case_ : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCAmelCase__ ( _a : Dict , _a : Optional[int] , _a : Optional[Any] , _a : Union[str, Any] , _a : int ):
snake_case_ : str = 1
snake_case_ : List[str] = first_solution
snake_case_ : int = []
snake_case_ : Optional[Any] = distance_of_first_solution
snake_case_ : int = solution
while count <= iters:
snake_case_ : Optional[Any] = find_neighborhood(_a , _a )
snake_case_ : Union[str, Any] = 0
snake_case_ : List[Any] = neighborhood[index_of_best_solution]
snake_case_ : Dict = len(_a ) - 1
snake_case_ : List[Any] = False
while not found:
snake_case_ : int = 0
while i < len(_a ):
if best_solution[i] != solution[i]:
snake_case_ : str = best_solution[i]
snake_case_ : Any = solution[i]
break
snake_case_ : Dict = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case_ : Optional[Any] = True
snake_case_ : Optional[int] = best_solution[:-1]
snake_case_ : List[Any] = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case_ : Union[str, Any] = cost
snake_case_ : Optional[int] = solution
else:
snake_case_ : Union[str, Any] = index_of_best_solution + 1
snake_case_ : int = neighborhood[index_of_best_solution]
if len(_a ) >= size:
tabu_list.pop(0 )
snake_case_ : List[str] = count + 1
return best_solution_ever, best_cost
def lowerCAmelCase__ ( _a : str=None ):
snake_case_ : Optional[Any] = generate_neighbours(args.File )
snake_case_ , snake_case_ : List[Any] = generate_first_solution(
args.File , _a )
snake_case_ , snake_case_ : int = tabu_search(
_a , _a , _a , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser(description='''Tabu Search''')
parser.add_argument(
'''-f''',
'''--File''',
type=str,
help='''Path to the file containing the data''',
required=True,
)
parser.add_argument(
'''-i''',
'''--Iterations''',
type=int,
help='''How many iterations the algorithm should perform''',
required=True,
)
parser.add_argument(
'''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36 | 1 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
self.assertEqual(len(_UpperCAmelCase ) , len(_UpperCAmelCase ) )
for a, b in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertAlmostEqual(_UpperCAmelCase , _UpperCAmelCase , delta=_UpperCAmelCase )
def _snake_case ( self ):
lowercase__: Union[str, Any] = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(_UpperCAmelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def _snake_case ( self ):
lowercase__: Tuple = None
ops.enable_eager_execution_internal()
lowercase__: List[str] = tf.config.list_physical_devices('''CPU''' )
if len(_UpperCAmelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowercase__: Tuple = tf.config.list_logical_devices(device_type='''CPU''' )
lowercase__: str = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowercase__: Optional[int] = GradientAccumulator()
lowercase__: Any = tf.Variable([4.0, 3.0] )
lowercase__, lowercase__: Optional[Any] = create_optimizer(5e-5 , 10 , 5 )
lowercase__: Optional[Any] = tf.Variable([0.0, 0.0] , trainable=_UpperCAmelCase )
def accumulate_on_replica(_UpperCAmelCase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(_UpperCAmelCase , _UpperCAmelCase ):
with strategy.scope():
lowercase__: List[str] = strategy.experimental_local_results(_UpperCAmelCase )
local_variables[0].assign(_UpperCAmelCase )
local_variables[1].assign(_UpperCAmelCase )
strategy.run(_UpperCAmelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(_UpperCAmelCase )
def _check_local_values(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__: List[str] = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , _UpperCAmelCase , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , _UpperCAmelCase , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 177 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=3 , _UpperCAmelCase=32 , _UpperCAmelCase=3 , _UpperCAmelCase=10 , _UpperCAmelCase=[10, 20, 30, 40] , _UpperCAmelCase=[1, 1, 2, 1] , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase="relu" , _UpperCAmelCase=3 , _UpperCAmelCase=None , ):
lowercase__: Optional[Any] = parent
lowercase__: Union[str, Any] = batch_size
lowercase__: int = image_size
lowercase__: Optional[Any] = num_channels
lowercase__: Optional[int] = embeddings_size
lowercase__: Dict = hidden_sizes
lowercase__: Union[str, Any] = depths
lowercase__: str = is_training
lowercase__: Optional[int] = use_labels
lowercase__: List[str] = hidden_act
lowercase__: Dict = num_labels
lowercase__: Any = scope
lowercase__: Optional[Any] = len(_UpperCAmelCase )
def _snake_case ( self ):
lowercase__: Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__: List[Any] = None
if self.use_labels:
lowercase__: Any = ids_tensor([self.batch_size] , self.num_labels )
lowercase__: Optional[int] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Optional[Any] = TFResNetModel(config=_UpperCAmelCase )
lowercase__: Dict = model(_UpperCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: str = self.num_labels
lowercase__: int = TFResNetForImageClassification(_UpperCAmelCase )
lowercase__: Optional[Any] = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self ):
lowercase__: int = self.prepare_config_and_inputs()
lowercase__, lowercase__, lowercase__: Optional[Any] = config_and_inputs
lowercase__: Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase (_UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Any = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_UpperCAmelCase :List[str] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase :Any = False
_UpperCAmelCase :List[str] = False
_UpperCAmelCase :Optional[Any] = False
_UpperCAmelCase :Tuple = False
_UpperCAmelCase :List[Any] = False
def _snake_case ( self ):
lowercase__: Union[str, Any] = TFResNetModelTester(self )
lowercase__: Optional[int] = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase )
def _snake_case ( self ):
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 _snake_case ( self ):
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def _snake_case ( self ):
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def _snake_case ( self ):
pass
def _snake_case ( self ):
lowercase__, lowercase__: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__: Optional[Any] = model_class(_UpperCAmelCase )
lowercase__: str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__: int = [*signature.parameters.keys()]
lowercase__: Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def _snake_case ( self ):
lowercase__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def _snake_case ( self ):
def check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Union[str, Any] = model_class(_UpperCAmelCase )
lowercase__: List[Any] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__: Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__: Tuple = self.model_tester.num_stages
self.assertEqual(len(_UpperCAmelCase ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase__, lowercase__: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__: Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase__: Tuple = layer_type
lowercase__: Any = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__: List[str] = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def _snake_case ( self ):
lowercase__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
@slow
def _snake_case ( self ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Dict = TFResNetModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
lowercase__: List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _snake_case ( self ):
lowercase__: Tuple = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase__: Any = self.default_image_processor
lowercase__: List[Any] = prepare_img()
lowercase__: List[Any] = image_processor(images=_UpperCAmelCase , return_tensors='''tf''' )
# forward pass
lowercase__: Dict = model(**_UpperCAmelCase )
# verify the logits
lowercase__: int = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowercase__: Dict = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _UpperCAmelCase , atol=1e-4 ) )
| 177 | 1 |
'''simple docstring'''
def __a ( _UpperCamelCase: Any , _UpperCamelCase: Dict ) -> int:
"""simple docstring"""
while b:
_snake_case = b, a % b
return a
def __a ( _UpperCamelCase: Optional[int] , _UpperCamelCase: List[Any] ) -> int:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase , a % b )
def __a ( ) -> Union[str, Any]:
"""simple docstring"""
print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 362 |
'''simple docstring'''
def __a ( _UpperCamelCase: int ) -> None:
"""simple docstring"""
_snake_case = generate_pascal_triangle(_UpperCamelCase )
for row_idx in range(_UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=" " )
else:
print(triangle[row_idx][col_idx] , end="" )
print()
def __a ( _UpperCamelCase: int ) -> list[list[int]]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
_snake_case = []
for current_row_idx in range(_UpperCamelCase ):
_snake_case = populate_current_row(_UpperCamelCase , _UpperCamelCase )
triangle.append(_UpperCamelCase )
return triangle
def __a ( _UpperCamelCase: list[list[int]] , _UpperCamelCase: int ) -> list[int]:
"""simple docstring"""
_snake_case = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
_snake_case , _snake_case = 1, 1
for current_col_idx in range(1 , _UpperCamelCase ):
calculate_current_element(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
return current_row
def __a ( _UpperCamelCase: list[list[int]] , _UpperCamelCase: list[int] , _UpperCamelCase: int , _UpperCamelCase: int , ) -> None:
"""simple docstring"""
_snake_case = triangle[current_row_idx - 1][current_col_idx - 1]
_snake_case = triangle[current_row_idx - 1][current_col_idx]
_snake_case = above_to_left_elt + above_to_right_elt
def __a ( _UpperCamelCase: int ) -> list[list[int]]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
_snake_case = [[1]]
for row_index in range(1 , _UpperCamelCase ):
_snake_case = [0] + result[-1] + [0]
_snake_case = row_index + 1
# Calculate the number of distinct elements in a row
_snake_case = sum(divmod(_UpperCamelCase , 2 ) )
_snake_case = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
_snake_case = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
_snake_case = row_first_half + row_second_half
result.append(_UpperCamelCase )
return result
def __a ( ) -> None:
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_UpperCamelCase: Callable , _UpperCamelCase: int ) -> None:
_snake_case = F"""{func.__name__}({value})"""
_snake_case = timeit(F"""__main__.{call}""" , setup="import __main__" )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(F"""{call:38} -- {timing:.4f} seconds""" )
for value in range(15 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(_UpperCamelCase , _UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 142 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = "cpu" , _SCREAMING_SNAKE_CASE : Union[str, None] = None ):
"""simple docstring"""
__a = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAMING_SNAKE_CASE )
for k, v in tqdm(state_dict.items() ):
if not isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
__a = v.half()
if save_path is None: # overwrite src_path
__a = src_path
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
fire.Fire(convert)
| 302 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
__a = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
__a = 128
elif "12-12" in model_name:
__a = 12
__a = 12
elif "14-14" in model_name:
__a = 14
__a = 14
elif "16-16" in model_name:
__a = 16
__a = 16
else:
raise ValueError("""Model not supported""" )
__a = """huggingface/label-files"""
if "speech-commands" in model_name:
__a = 35
__a = """speech-commands-v2-id2label.json"""
else:
__a = 527
__a = """audioset-id2label.json"""
__a = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
__a = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__a = idalabel
__a = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if "module.v" in name:
__a = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
__a = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
__a = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
__a = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__a = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
__a = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
__a = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__a = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__a = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__a = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__a = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__a = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
__a = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
__a = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
__a = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "qkv" in key:
__a = key.split(""".""" )
__a = int(key_split[3] )
__a = config.hidden_size
if "weight" in key:
__a = val[:dim, :]
__a = val[dim : dim * 2, :]
__a = val[-dim:, :]
else:
__a = val[:dim]
__a = val[dim : dim * 2]
__a = val[-dim:]
else:
__a = val
return orig_state_dict
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
__a = [
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@torch.no_grad()
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str]=False ):
"""simple docstring"""
__a = get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE )
__a = {
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
__a = model_name_to_url[model_name]
__a = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# remove some keys
remove_keys(_SCREAMING_SNAKE_CASE )
# rename some keys
__a = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# load 🤗 model
__a = ASTForAudioClassification(_SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
__a = -4.267_7393 if """speech-commands""" not in model_name else -6.84_5978
__a = 4.568_9974 if """speech-commands""" not in model_name else 5.565_4526
__a = 1024 if """speech-commands""" not in model_name else 128
__a = ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
if "speech-commands" in model_name:
__a = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
__a = dataset[0]["""audio"""]["""array"""]
else:
__a = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
__a , __a = torchaudio.load(_SCREAMING_SNAKE_CASE )
__a = waveform.squeeze().numpy()
__a = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=1_6000 , return_tensors="""pt""" )
# forward pass
__a = model(**_SCREAMING_SNAKE_CASE )
__a = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
__a = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
__a = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
__a = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
__a = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
__a = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
__a = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
__a = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
__a = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ):
raise ValueError("""Logits don't match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f"MIT/{model_name}" )
feature_extractor.push_to_hub(f"MIT/{model_name}" )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer 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."""
)
lowerCamelCase__ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(__lowercase )
class SCREAMING_SNAKE_CASE ( __lowercase ):
"""simple docstring"""
def __init__( self : Dict , **lowerCAmelCase : int ) -> List[str]:
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , """vision""" )
self.check_model_type(lowerCAmelCase )
def __call__( self : List[str] , lowerCAmelCase : Union[str, "Image.Image", List[Dict[str, Any]]] , lowerCAmelCase : Union[str, List[str]] = None , **lowerCAmelCase : Union[str, Any] , ) -> Union[str, Any]:
"""simple docstring"""
if "text_queries" in kwargs:
__lowerCAmelCase : str = kwargs.pop("""text_queries""" )
if isinstance(lowerCAmelCase , (str, Image.Image) ):
__lowerCAmelCase : Optional[Any] = {"""image""": image, """candidate_labels""": candidate_labels}
else:
__lowerCAmelCase : List[str] = image
__lowerCAmelCase : Dict = super().__call__(lowerCAmelCase , **lowerCAmelCase )
return results
def SCREAMING_SNAKE_CASE ( self : Any , **lowerCAmelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = {}
if "threshold" in kwargs:
__lowerCAmelCase : Optional[int] = kwargs["""threshold"""]
if "top_k" in kwargs:
__lowerCAmelCase : Dict = kwargs["""top_k"""]
return {}, {}, postprocess_params
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : int = load_image(inputs["""image"""] )
__lowerCAmelCase : Tuple = inputs["""candidate_labels"""]
if isinstance(lowerCAmelCase , lowerCAmelCase ):
__lowerCAmelCase : Union[str, Any] = candidate_labels.split(""",""" )
__lowerCAmelCase : Optional[int] = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(lowerCAmelCase ):
__lowerCAmelCase : int = self.tokenizer(lowerCAmelCase , return_tensors=self.framework )
__lowerCAmelCase : Union[str, Any] = self.image_processor(lowerCAmelCase , return_tensors=self.framework )
yield {
"is_last": i == len(lowerCAmelCase ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = model_inputs.pop("""target_size""" )
__lowerCAmelCase : Dict = model_inputs.pop("""candidate_label""" )
__lowerCAmelCase : List[str] = model_inputs.pop("""is_last""" )
__lowerCAmelCase : str = self.model(**lowerCAmelCase )
__lowerCAmelCase : Dict = {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
return model_outputs
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : Tuple=None ) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = []
for model_output in model_outputs:
__lowerCAmelCase : Dict = model_output["""candidate_label"""]
__lowerCAmelCase : Union[str, Any] = BaseModelOutput(lowerCAmelCase )
__lowerCAmelCase : int = self.image_processor.post_process_object_detection(
outputs=lowerCAmelCase , threshold=lowerCAmelCase , target_sizes=model_output["""target_size"""] )[0]
for index in outputs["scores"].nonzero():
__lowerCAmelCase : Tuple = outputs["""scores"""][index].item()
__lowerCAmelCase : str = self._get_bounding_box(outputs["""boxes"""][index][0] )
__lowerCAmelCase : List[str] = {"""score""": score, """label""": label, """box""": box}
results.append(lowerCAmelCase )
__lowerCAmelCase : Dict = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x["score"] , reverse=lowerCAmelCase )
if top_k:
__lowerCAmelCase : str = results[:top_k]
return results
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase : "torch.Tensor" ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""" )
__lowerCAmelCase : List[str] = box.int().tolist()
__lowerCAmelCase : List[Any] = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 363 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def snake_case_ (__A : Optional[int] , __A : Any ) -> Any:
__lowerCAmelCase : Union[str, Any] = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
] )
return rename_keys
def snake_case_ (__A : List[str] , __A : str ) -> Optional[Any]:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__lowerCAmelCase : Optional[Any] = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
__lowerCAmelCase : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
__lowerCAmelCase : str = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__lowerCAmelCase : str = in_proj_weight[
-encoder_config.hidden_size :, :
]
def snake_case_ (__A : Union[str, Any] , __A : str , __A : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase : Any = dct.pop(__A )
__lowerCAmelCase : str = val
def snake_case_ (__A : int ) -> Tuple:
if "handwritten" in checkpoint_url:
__lowerCAmelCase : Tuple = """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg""" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Optional[Any] = """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
__lowerCAmelCase : Dict = Image.open(requests.get(__A , stream=__A ).raw ).convert("""RGB""" )
return im
@torch.no_grad()
def snake_case_ (__A : Any , __A : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase : List[Any] = ViTConfig(image_size=3_8_4 , qkv_bias=__A )
__lowerCAmelCase : List[Any] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__lowerCAmelCase : Union[str, Any] = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
__lowerCAmelCase : Any = 1_0_2_4
__lowerCAmelCase : Any = 4_0_9_6
__lowerCAmelCase : Optional[int] = 2_4
__lowerCAmelCase : str = 1_6
__lowerCAmelCase : List[Any] = 1_0_2_4
else:
raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Union[str, Any] = """relu"""
__lowerCAmelCase : List[Any] = 1_0_2_4
__lowerCAmelCase : Any = True
__lowerCAmelCase : List[Any] = False
__lowerCAmelCase : Dict = False
# load HuggingFace model
__lowerCAmelCase : Dict = ViTModel(__A , add_pooling_layer=__A )
__lowerCAmelCase : Union[str, Any] = TrOCRForCausalLM(__A )
__lowerCAmelCase : Any = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
# load state_dict of original model, rename some keys
__lowerCAmelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" , check_hash=__A )["""model"""]
__lowerCAmelCase : Any = create_rename_keys(__A , __A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
read_in_q_k_v(__A , __A )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__lowerCAmelCase : Tuple = state_dict.pop(__A )
if key.startswith("""decoder""" ) and "output_projection" not in key:
__lowerCAmelCase : str = val
else:
__lowerCAmelCase : Tuple = val
# load state dict
model.load_state_dict(__A )
# Check outputs on an image
__lowerCAmelCase : List[Any] = ViTImageProcessor(size=encoder_config.image_size )
__lowerCAmelCase : List[str] = RobertaTokenizer.from_pretrained("""roberta-large""" )
__lowerCAmelCase : List[Any] = TrOCRProcessor(__A , __A )
__lowerCAmelCase : List[str] = processor(images=prepare_img(__A ) , return_tensors="""pt""" ).pixel_values
# verify logits
__lowerCAmelCase : List[str] = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__lowerCAmelCase : List[str] = model(pixel_values=__A , decoder_input_ids=__A )
__lowerCAmelCase : Optional[Any] = outputs.logits
__lowerCAmelCase : Union[str, Any] = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
__lowerCAmelCase : Dict = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
__lowerCAmelCase : Tuple = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , __A , atol=1e-3 ), "First elements of logits not as expected"
Path(__A ).mkdir(exist_ok=__A )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__A )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
__UpperCAmelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 139 | 0 |
from __future__ import annotations
from collections import deque
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,A : list[str] ):
__A = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []} )
for keyword in keywords:
self.add_keyword(A )
self.set_fail_transitions()
def UpperCamelCase_ ( self : Dict ,A : int ,A : str ):
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def UpperCamelCase_ ( self : str ,A : str ):
__A = 0
for character in keyword:
__A = self.find_next_state(A ,A )
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
__A = len(self.adlist ) - 1
else:
__A = next_state
self.adlist[current_state]["output"].append(A )
def UpperCamelCase_ ( self : str ):
__A = deque()
for node in self.adlist[0]["next_states"]:
q.append(A )
__A = 0
while q:
__A = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(A )
__A = self.adlist[r]["fail_state"]
while (
self.find_next_state(A ,self.adlist[child]["value"] ) is None
and state != 0
):
__A = self.adlist[state]["fail_state"]
__A = self.find_next_state(
A ,self.adlist[child]["value"] )
if self.adlist[child]["fail_state"] is None:
__A = 0
__A = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def UpperCamelCase_ ( self : Optional[int] ,A : str ):
__A = {} # returns a dict with keywords and list of its occurrences
__A = 0
for i in range(len(A ) ):
while (
self.find_next_state(A ,string[i] ) is None
and current_state != 0
):
__A = self.adlist[current_state]["fail_state"]
__A = self.find_next_state(A ,string[i] )
if next_state is None:
__A = 0
else:
__A = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
__A = []
result[key].append(i - len(A ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 15 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
UpperCamelCase_ = AlbertConfig.from_json_file(_A )
print(f"Building PyTorch model from configuration: {config}" )
UpperCamelCase_ = AlbertForPreTraining(_A )
# Load weights from tf checkpoint
load_tf_weights_in_albert(_A , _A , _A )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _A )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Optional[Any] = 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(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 355 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE :Optional[int] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John\",
booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
month = aug # \" 8-12\",
year = \"2006\",
address = \"Cambridge, Massachusetts, USA\",
publisher = \"Association for Machine Translation in the Americas\",
url = \"https://aclanthology.org/2006.amta-papers.25\",
pages = \"223--231\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
"""
SCREAMING_SNAKE_CASE :Optional[int] = """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""
SCREAMING_SNAKE_CASE :Tuple = """
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
'score' (float): TER score (num_edits / sum_ref_lengths * 100)
'num_edits' (int): The cumulative number of edits
'ref_length' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}
Example 2:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}
Example 3:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}
Example 4:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}
Example 5:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def UpperCAmelCase_ ( self )-> Tuple:
if version.parse(scb.__version__ ) < version.parse("1.4.12" ):
raise ImportWarning(
"To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
"You can install it with `pip install \"sacrebleu>=1.4.12\"`." )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="http://www.cs.umd.edu/~snover/tercom/" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"] , reference_urls=[
"https://github.com/jhclark/tercom",
] , )
def UpperCAmelCase_ ( self , _lowercase , _lowercase , _lowercase = False , _lowercase = False , _lowercase = False , _lowercase = False , )-> int:
UpperCamelCase_ = len(references[0] )
if any(len(_lowercase ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
UpperCamelCase_ = [[refs[i] for refs in references] for i in range(_lowercase )]
UpperCamelCase_ = TER(
normalized=_lowercase , no_punct=_lowercase , asian_support=_lowercase , case_sensitive=_lowercase , )
UpperCamelCase_ = sb_ter.corpus_score(_lowercase , _lowercase )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 60 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
_lowerCamelCase : Optional[int] = TypeVar("""T""")
class UpperCamelCase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict) ->Any:
'''simple docstring'''
A__ = data
A__ = self
A__ = 0
class UpperCamelCase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Union[str, Any]) ->Union[str, Any]:
'''simple docstring'''
A__ = {}
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : List[str]) ->List[Any]:
'''simple docstring'''
A__ = DisjointSetTreeNode(_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : List[str]) ->Dict:
'''simple docstring'''
A__ = self.map[data]
if elem_ref != elem_ref.parent:
A__ = self.find_set(elem_ref.parent.data)
return elem_ref.parent
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict) ->Optional[int]:
'''simple docstring'''
if nodea.rank > nodea.rank:
A__ = nodea
else:
A__ = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any) ->Optional[int]:
'''simple docstring'''
self.link(self.find_set(_UpperCAmelCase) , self.find_set(_UpperCAmelCase))
class UpperCamelCase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Dict) ->str:
'''simple docstring'''
A__ = {}
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Optional[int]) ->Optional[Any]:
'''simple docstring'''
if node not in self.connections:
A__ = {}
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[int]) ->Optional[Any]:
'''simple docstring'''
self.add_node(_UpperCAmelCase)
self.add_node(_UpperCAmelCase)
A__ = weight
A__ = weight
def SCREAMING_SNAKE_CASE ( self : str) ->List[str]:
'''simple docstring'''
A__ = []
A__ = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start))
edges.append((start, end, self.connections[start][end]))
edges.sort(key=lambda UpperCAmelCase__: x[2])
# creating the disjoint set
A__ = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(_UpperCAmelCase)
# MST generation
A__ = 0
A__ = 0
A__ = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections) - 1:
A__ = edges[index]
index += 1
A__ = disjoint_set.find_set(_UpperCAmelCase)
A__ = disjoint_set.find_set(_UpperCAmelCase)
if parent_u != parent_v:
num_edges += 1
graph.add_edge(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
disjoint_set.union(_UpperCAmelCase , _UpperCAmelCase)
return graph
| 14 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
A = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
},
}
# TODO(PVP) - this should be removed in Transformers v5
A = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ['''input_ids''', '''attention_mask''']
__lowerCAmelCase = TaTokenizer
__lowerCAmelCase = []
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="</s>" , _UpperCAmelCase="<unk>" , _UpperCAmelCase="<pad>" , _UpperCAmelCase=100 , _UpperCAmelCase=None , **_UpperCAmelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__a : Dict = [f"""<extra_id_{i}>""" for i in range(_UpperCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
__a : Union[str, Any] = len(set(filter(lambda _UpperCAmelCase : bool('''extra_id_''' in str(_UpperCAmelCase ) ) , _UpperCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , extra_ids=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , **_UpperCAmelCase , )
__a : Union[str, Any] = vocab_file
__a : int = False if not self.vocab_file else True
__a : List[str] = extra_ids
@staticmethod
def _lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
__a : Any = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f""" {pretrained_model_name_or_path} automatically truncating your input to"""
f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCAmelCase , )
return max_model_length
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a : Optional[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
logger.info(f"""Copy vocab file to {out_vocab_file}""" )
return (out_vocab_file,)
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : str = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
__a : List[str] = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : Tuple = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCamelCase ( self ):
return list(
set(filter(lambda _UpperCAmelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def _lowerCamelCase ( self ):
return [self.convert_tokens_to_ids(_UpperCAmelCase ) for token in self.get_sentinel_tokens()]
| 160 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : int = ['''image_processor''', '''tokenizer''']
snake_case__ : Optional[int] = '''AutoImageProcessor'''
snake_case__ : int = '''AutoTokenizer'''
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : List[Any]=None , **SCREAMING_SNAKE_CASE__ : Tuple ) -> Optional[Any]:
a_ : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , SCREAMING_SNAKE_CASE__ , )
a_ : Dict = kwargs.pop('feature_extractor' )
a_ : List[str] = 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__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = self.image_processor
a_ : Union[str, Any] = False
def __call__( self : Optional[Any] , *SCREAMING_SNAKE_CASE__ : List[Any] , **SCREAMING_SNAKE_CASE__ : Tuple ) -> Optional[int]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
a_ : Dict = kwargs.pop('images' , SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = kwargs.pop('text' , SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
a_ : Optional[Any] = args[0]
a_ : int = args[1:]
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.' )
if images is not None:
a_ : List[str] = self.image_processor(SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if text is not None:
a_ : Dict = self.tokenizer(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
a_ : Optional[int] = encodings['input_ids']
return inputs
def SCREAMING_SNAKE_CASE ( self : List[Any] , *SCREAMING_SNAKE_CASE__ : Optional[Any] , **SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Optional[int]:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : int , *SCREAMING_SNAKE_CASE__ : List[Any] , **SCREAMING_SNAKE_CASE__ : List[str] ) -> Union[str, Any]:
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@contextmanager
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your images inputs, or in a separate call.' )
a_ : Any = True
a_ : Optional[int] = self.tokenizer
yield
a_ : List[Any] = self.image_processor
a_ : Optional[int] = False
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : Dict=None ) -> int:
if added_vocab is None:
a_ : Optional[Any] = self.tokenizer.get_added_vocab()
a_ : List[Any] = {}
while tokens:
a_ : int = re.search(r'<s_(.*?)>' , SCREAMING_SNAKE_CASE__ , re.IGNORECASE )
if start_token is None:
break
a_ : Dict = start_token.group(1 )
a_ : Union[str, Any] = re.search(rF"""</s_{key}>""" , SCREAMING_SNAKE_CASE__ , re.IGNORECASE )
a_ : Any = start_token.group()
if end_token is None:
a_ : str = tokens.replace(SCREAMING_SNAKE_CASE__ , '' )
else:
a_ : Any = end_token.group()
a_ : Tuple = re.escape(SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = re.escape(SCREAMING_SNAKE_CASE__ )
a_ : int = re.search(F"""{start_token_escaped}(.*?){end_token_escaped}""" , SCREAMING_SNAKE_CASE__ , re.IGNORECASE )
if content is not None:
a_ : int = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
a_ : Optional[int] = self.tokenajson(SCREAMING_SNAKE_CASE__ , is_inner_value=SCREAMING_SNAKE_CASE__ , added_vocab=SCREAMING_SNAKE_CASE__ )
if value:
if len(SCREAMING_SNAKE_CASE__ ) == 1:
a_ : Optional[Any] = value[0]
a_ : int = value
else: # leaf nodes
a_ : Tuple = []
for leaf in content.split(r'<sep/>' ):
a_ : Tuple = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
a_ : Union[str, Any] = leaf[1:-2] # for categorical special tokens
output[key].append(SCREAMING_SNAKE_CASE__ )
if len(output[key] ) == 1:
a_ : Dict = output[key][0]
a_ : Dict = tokens[tokens.find(SCREAMING_SNAKE_CASE__ ) + len(SCREAMING_SNAKE_CASE__ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=SCREAMING_SNAKE_CASE__ , added_vocab=SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , SCREAMING_SNAKE_CASE__ , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , SCREAMING_SNAKE_CASE__ , )
return self.image_processor
| 363 |
def SCREAMING_SNAKE_CASE_ ( __A : list ) -> list:
"""simple docstring"""
a_ : int = len(__A )
for _ in range(__A ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
a_ , a_ : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCAmelCase_ : int = list(range(10, 0, -1))
print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 120 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( snake_case , snake_case ) -> str:
assert isinstance(a_ , a_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Optional[Any]:
lowercase__: Optional[int] = tmp_path / '''cache'''
lowercase__: List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase__: List[Any] = JsonDatasetReader(a_ , cache_dir=a_ , keep_in_memory=a_ ).read()
_check_json_dataset(a_ , a_ )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Any:
lowercase__: Dict = tmp_path / '''cache'''
lowercase__: int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase__: Optional[Any] = features.copy() if features else default_expected_features
lowercase__: Union[str, Any] = (
Features({feature: Value(a_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__: Optional[int] = JsonDatasetReader(a_ , features=a_ , cache_dir=a_ ).read()
_check_json_dataset(a_ , a_ )
@pytest.mark.parametrize(
'features' , [
None,
{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
] , )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Any:
lowercase__: int = tmp_path / '''cache'''
lowercase__: Union[str, Any] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
lowercase__: Tuple = features.copy() if features else default_expected_features
lowercase__: Dict = (
Features({feature: Value(a_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__: Tuple = JsonDatasetReader(a_ , features=a_ , cache_dir=a_ ).read()
assert isinstance(a_ , a_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def snake_case_ ( snake_case , snake_case ) -> Union[str, Any]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
lowercase__: List[Any] = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
lowercase__: Any = features.copy()
lowercase__: Optional[Any] = (
Features({feature: Value(a_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__: Dict = tmp_path / '''cache'''
lowercase__: Union[str, Any] = JsonDatasetReader(a_ , features=a_ , cache_dir=a_ ).read()
assert isinstance(a_ , a_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def snake_case_ ( snake_case , snake_case , snake_case ) -> List[Any]:
lowercase__: int = tmp_path / '''cache'''
lowercase__: Optional[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase__: Dict = JsonDatasetReader(a_ , cache_dir=a_ , split=a_ ).read()
_check_json_dataset(a_ , a_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Dict:
if issubclass(a_ , a_ ):
lowercase__: Any = jsonl_path
elif issubclass(a_ , a_ ):
lowercase__: Dict = [jsonl_path]
lowercase__: List[str] = tmp_path / '''cache'''
lowercase__: int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase__: str = JsonDatasetReader(a_ , cache_dir=a_ ).read()
_check_json_dataset(a_ , a_ )
def snake_case_ ( snake_case , snake_case , snake_case=("train",) ) -> str:
assert isinstance(a_ , a_ )
for split in splits:
lowercase__: Union[str, Any] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Optional[Any]:
lowercase__: Union[str, Any] = tmp_path / '''cache'''
lowercase__: Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase__: Any = JsonDatasetReader({'train': jsonl_path} , cache_dir=a_ , keep_in_memory=a_ ).read()
_check_json_datasetdict(a_ , a_ )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Optional[Any]:
lowercase__: List[Any] = tmp_path / '''cache'''
lowercase__: Optional[int] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase__: Dict = features.copy() if features else default_expected_features
lowercase__: Tuple = (
Features({feature: Value(a_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__: List[Any] = JsonDatasetReader({'train': jsonl_path} , features=a_ , cache_dir=a_ ).read()
_check_json_datasetdict(a_ , a_ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def snake_case_ ( snake_case , snake_case , snake_case ) -> Any:
if split:
lowercase__: Any = {split: jsonl_path}
else:
lowercase__: Tuple = '''train'''
lowercase__: Union[str, Any] = {'''train''': jsonl_path, '''test''': jsonl_path}
lowercase__: str = tmp_path / '''cache'''
lowercase__: Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase__: Dict = JsonDatasetReader(a_ , cache_dir=a_ ).read()
_check_json_datasetdict(a_ , a_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def snake_case_ ( snake_case ) -> Tuple:
return json.load(a_ )
def snake_case_ ( snake_case ) -> Optional[int]:
return [json.loads(a_ ) for line in buffer]
class __a :
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ ).write()
buffer.seek(0 )
lowercase__: Union[str, Any] = load_json_function(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , orient=SCREAMING_SNAKE_CASE__ ).write()
buffer.seek(0 )
lowercase__: Union[str, Any] = load_json(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE__ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(SCREAMING_SNAKE_CASE__ ) == 10
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , num_proc=2 ).write()
buffer.seek(0 )
lowercase__: Union[str, Any] = load_json_function(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , orient=SCREAMING_SNAKE_CASE__ , num_proc=2 ).write()
buffer.seek(0 )
lowercase__: Optional[int] = load_json(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE__ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(SCREAMING_SNAKE_CASE__ ) == 10
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , num_proc=0 )
@pytest.mark.parametrize('compression, extension' , [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')] )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
lowercase__: Optional[Any] = tmp_path_factory.mktemp('data' ) / F'test.json.{extension}'
lowercase__: int = str(shared_datadir / F'test_file.json.{extension}' )
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , compression=SCREAMING_SNAKE_CASE__ ).write()
with fsspec.open(SCREAMING_SNAKE_CASE__ , 'rb' , compression='infer' ) as f:
lowercase__: List[Any] = f.read()
with fsspec.open(SCREAMING_SNAKE_CASE__ , 'rb' , compression='infer' ) as f:
lowercase__: Union[str, Any] = f.read()
assert exported_content == original_content
| 196 |
"""simple docstring"""
from manim import *
class _SCREAMING_SNAKE_CASE( A ):
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
__SCREAMING_SNAKE_CASE :List[str] = Rectangle(height=0.4_6 ,width=0.4_6 ).set_stroke(width=0 )
__SCREAMING_SNAKE_CASE :List[str] = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE :List[str] = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE :Optional[int] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0 )
__SCREAMING_SNAKE_CASE :Optional[Any] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0 )
__SCREAMING_SNAKE_CASE :Any = VGroup(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0 )
__SCREAMING_SNAKE_CASE :Tuple = Text('''CPU''' ,font_size=24 )
__SCREAMING_SNAKE_CASE :Optional[Any] = Group(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0.5 ,aligned_edge=SCREAMING_SNAKE_CASE__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :List[Any] = [mem.copy() for i in range(1 )]
__SCREAMING_SNAKE_CASE :str = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0 )
__SCREAMING_SNAKE_CASE :Union[str, Any] = Text('''GPU''' ,font_size=24 )
__SCREAMING_SNAKE_CASE :int = Group(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0.5 ,aligned_edge=SCREAMING_SNAKE_CASE__ )
gpu.align_to(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :str = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE :int = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0 )
__SCREAMING_SNAKE_CASE :List[Any] = Text('''Model''' ,font_size=24 )
__SCREAMING_SNAKE_CASE :int = Group(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ ,buff=0.5 ,aligned_edge=SCREAMING_SNAKE_CASE__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(SCREAMING_SNAKE_CASE__ ,run_time=1 ) ,Create(SCREAMING_SNAKE_CASE__ ,run_time=1 ) ,Create(SCREAMING_SNAKE_CASE__ ,run_time=1 ) ,)
__SCREAMING_SNAKE_CASE :List[str] = MarkupText(
f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''' ,font_size=24 ,)
__SCREAMING_SNAKE_CASE :List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__SCREAMING_SNAKE_CASE :Optional[Any] = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' ,font_size=18 ,)
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE__ ,run_time=2.5 ) ,Write(SCREAMING_SNAKE_CASE__ ) ,Write(SCREAMING_SNAKE_CASE__ ) )
self.add(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Union[str, Any] = []
__SCREAMING_SNAKE_CASE :int = []
__SCREAMING_SNAKE_CASE :List[Any] = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE :Any = Rectangle(height=0.4_6 ,width=0.4_6 ).set_stroke(width=0.0 ).set_fill(SCREAMING_SNAKE_CASE__ ,opacity=0.7 )
cpu_target.move_to(SCREAMING_SNAKE_CASE__ )
cpu_target.generate_target()
__SCREAMING_SNAKE_CASE :Union[str, Any] = 0.4_6 / 4
__SCREAMING_SNAKE_CASE :Tuple = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.0_2 ,direction=SCREAMING_SNAKE_CASE__ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target ,direction=SCREAMING_SNAKE_CASE__ ,buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target ,direction=SCREAMING_SNAKE_CASE__ ,buff=0.0 )
cpu_targs.append(SCREAMING_SNAKE_CASE__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(SCREAMING_SNAKE_CASE__ ) )
second_animations.append(MoveToTarget(SCREAMING_SNAKE_CASE__ ,run_time=1.5 ) )
self.play(*SCREAMING_SNAKE_CASE__ )
self.play(*SCREAMING_SNAKE_CASE__ )
self.wait()
| 191 | 0 |
"""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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Optional[int] = "▁"
__A : List[Any] = {"vocab_file": "spiece.model"}
__A : Union[str, Any] = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
__A : Tuple = {
"google/reformer-crime-and-punishment": 524288,
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : str , __UpperCamelCase : Dict , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : str="<unk>" , __UpperCamelCase : Optional[int]=[] , __UpperCamelCase : Optional[Dict[str, Any]] = None , **__UpperCamelCase : Tuple , )->None:
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , additional_special_tokens=__UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCamelCase , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCamelCase )
@property
def lowercase__ ( self : Optional[int] )->Any:
return self.sp_model.get_piece_size()
def lowercase__ ( self : Union[str, Any] )->Dict[str, int]:
_UpperCAmelCase = {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict )->Union[str, Any]:
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self : str , __UpperCamelCase : Optional[int] )->int:
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : str )->List[str]:
return self.sp_model.encode(__UpperCamelCase , out_type=__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : List[Any] )->List[Any]:
return self.sp_model.piece_to_id(__UpperCamelCase )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : List[str] )->Dict:
if index < self.sp_model.get_piece_size():
_UpperCAmelCase = self.sp_model.IdToPiece(__UpperCamelCase )
return token
def lowercase__ ( self : Any , __UpperCamelCase : Optional[Any] )->Tuple:
_UpperCAmelCase = []
_UpperCAmelCase = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__UpperCamelCase ) + token
_UpperCAmelCase = []
else:
current_sub_tokens.append(__UpperCamelCase )
out_string += self.sp_model.decode(__UpperCamelCase )
return out_string.strip()
def lowercase__ ( self : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None )->Tuple[str]:
if not os.path.isdir(__UpperCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
__UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCamelCase , '''wb''' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(__UpperCamelCase )
return (out_vocab_file,)
| 326 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = int(number**0.5 )
return number == sq * sq
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
_UpperCAmelCase = x_den * y_den * z_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
top //= hcf
bottom //= hcf
return top, bottom
def lowercase ( _SCREAMING_SNAKE_CASE : int = 35 ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = 42
_UpperCAmelCase = Fraction(0 )
_UpperCAmelCase = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
_UpperCAmelCase = x_num * y_den + x_den * y_num
_UpperCAmelCase = x_den * y_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
_UpperCAmelCase = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
_UpperCAmelCase = x_den * x_den * y_den * y_den
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=-1
_UpperCAmelCase = x_num * y_num
_UpperCAmelCase = x_den * y_num + x_num * y_den
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
_UpperCAmelCase = x_num * x_num * y_num * y_num
_UpperCAmelCase = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = int(sqrt(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_UpperCAmelCase = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
for num, den in unique_s:
total += Fraction(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 326 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
UpperCamelCase__ : Dict = sys.version_info >= (3, 10)
def lowerCAmelCase_ ( _lowerCamelCase: List[str]=None , _lowerCamelCase: Union[str, Any]=None ):
return field(default_factory=lambda: default , metadata=lowerCAmelCase__ )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : int
_A : float
_A : str
_A : bool
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : int = 42
_A : str = field(default='''toto''' , metadata={'''help''': '''help message'''} )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : bool = False
_A : bool = True
_A : Optional[bool] = None
class _UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_A : Dict = "titi"
_A : Optional[Any] = "toto"
class _UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_A : Tuple = "titi"
_A : List[str] = "toto"
_A : List[str] = 42
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : BasicEnum = "toto"
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = BasicEnum(self.foo )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : MixedTypeEnum = "toto"
def UpperCamelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = MixedTypeEnum(self.foo )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : Optional[int] = None
_A : Optional[float] = field(default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''help message'''} )
_A : Optional[str] = None
_A : Optional[List[str]] = list_field(default=[] )
_A : Optional[List[int]] = list_field(default=[] )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : List[int] = list_field(default=[] )
_A : List[int] = list_field(default=[1, 2, 3] )
_A : List[str] = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
_A : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : List[int] = field()
_A : str = field()
_A : BasicEnum = field()
def UpperCamelCase__ ( self : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = BasicEnum(self.required_enum )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : int
_A : "BasicEnum" = field()
_A : "Optional[bool]" = None
_A : "str" = field(default='''toto''' , metadata={'''help''': '''help message'''} )
_A : "List[str]" = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : bool = False
_A : bool = True
_A : bool | None = None
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : int | None = None
_A : float | None = field(default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''help message'''} )
_A : str | None = None
_A : list[str] | None = list_field(default=[] )
_A : list[int] | None = list_field(default=[] )
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__SCREAMING_SNAKE_CASE : Dict = {k: v for k, v in vars(__UpperCAmelCase ).items() if k != """container"""}
__SCREAMING_SNAKE_CASE : Any = {k: v for k, v in vars(__UpperCAmelCase ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , __UpperCAmelCase ) and yy.get("""choices""" , __UpperCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](__UpperCAmelCase ) , yy["""type"""](__UpperCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("""--bar""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("""--baz""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("""--flag""" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , nargs="""?""" )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Tuple = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
(__SCREAMING_SNAKE_CASE ) : Optional[Any] = parser.parse_args_into_dataclasses(__UpperCAmelCase , look_for_args_file=__UpperCAmelCase )
self.assertFalse(example.flag )
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=4_2 , type=__UpperCAmelCase )
expected.add_argument("""--baz""" , default="""toto""" , type=__UpperCAmelCase , help="""help message""" )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , nargs="""?""" )
expected.add_argument("""--baz""" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=__UpperCAmelCase , dest="""baz""" )
expected.add_argument("""--opt""" , type=__UpperCAmelCase , default=__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCAmelCase )
for dataclass_type in dataclass_types:
__SCREAMING_SNAKE_CASE : Optional[Any] = HfArgumentParser(__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Tuple = parser.parse_args([] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : Any = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : Dict = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 4_2] , type=make_choice_type_function(["""titi""", """toto""", 4_2] ) , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 4_2 )
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : Literal["titi", "toto", 42] = "toto"
__SCREAMING_SNAKE_CASE : Dict = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 4_2) , type=make_choice_type_function(["""titi""", """toto""", 4_2] ) , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__SCREAMING_SNAKE_CASE : Dict = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__SCREAMING_SNAKE_CASE : int = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 4_2 )
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=__UpperCAmelCase )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=__UpperCAmelCase )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=__UpperCAmelCase )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args([] )
self.assertEqual(
__UpperCAmelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
__SCREAMING_SNAKE_CASE : int = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(__UpperCAmelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=__UpperCAmelCase , type=__UpperCAmelCase )
expected.add_argument("""--bar""" , default=__UpperCAmelCase , type=__UpperCAmelCase , help="""help message""" )
expected.add_argument("""--baz""" , default=__UpperCAmelCase , type=__UpperCAmelCase )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=__UpperCAmelCase )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Any = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCAmelCase )
for dataclass_type in dataclass_types:
__SCREAMING_SNAKE_CASE : str = HfArgumentParser(__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args([] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , bar=__UpperCAmelCase , baz=__UpperCAmelCase , ces=[] , des=[] ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(__UpperCAmelCase , Namespace(foo=1_2 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def UpperCamelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("""--required_str""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=__UpperCAmelCase , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=__UpperCAmelCase , )
expected.add_argument("""--opt""" , type=__UpperCAmelCase , default=__UpperCAmelCase )
expected.add_argument("""--baz""" , default="""toto""" , type=__UpperCAmelCase , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
__SCREAMING_SNAKE_CASE : Tuple = parser.parse_dict(__UpperCAmelCase )[0]
__SCREAMING_SNAKE_CASE : Optional[Any] = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Dict = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 4_2,
}
self.assertRaises(__UpperCAmelCase , parser.parse_dict , __UpperCAmelCase , allow_extra_keys=__UpperCAmelCase )
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : str = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(__UpperCAmelCase , """temp_json""" )
os.mkdir(__UpperCAmelCase )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
__SCREAMING_SNAKE_CASE : int = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = HfArgumentParser(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : int = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(__UpperCAmelCase , """temp_yaml""" )
os.mkdir(__UpperCAmelCase )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
__SCREAMING_SNAKE_CASE : Optional[int] = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = HfArgumentParser(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
| 112 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PegasusXForConditionalGeneration''',
'''PegasusXModel''',
'''PegasusXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 254 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {'''vocab_file''': '''sentencepiece.model'''}
__lowerCAmelCase = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
}
__lowerCAmelCase = {
'''google/rembert''': 2_56,
}
class __a ( __UpperCamelCase ):
__lowercase : Any = VOCAB_FILES_NAMES
__lowercase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__="[CLS]" , lowerCAmelCase__="[SEP]" , lowerCAmelCase__="[UNK]" , lowerCAmelCase__="[SEP]" , lowerCAmelCase__="[PAD]" , lowerCAmelCase__="[CLS]" , lowerCAmelCase__="[MASK]" , **lowerCAmelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
do_lower_case=lowerCAmelCase__ , remove_space=lowerCAmelCase__ , keep_accents=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , **lowerCAmelCase__ , )
lowercase__: Tuple = do_lower_case
lowercase__: List[str] = remove_space
lowercase__: Optional[int] = keep_accents
lowercase__: Optional[int] = vocab_file
lowercase__: Any = spm.SentencePieceProcessor()
self.sp_model.Load(lowerCAmelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
return len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: int = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = self.__dict__.copy()
lowercase__: Any = None
return state
def __setstate__( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
lowercase__: Optional[int] = d
lowercase__: str = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Tuple = self.sp_model.EncodeAsPieces(lowerCAmelCase__ )
return pieces
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.sp_model.PieceToId(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return self.sp_model.IdToPiece(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
lowercase__: Any = self.sp_model.decode_pieces(lowerCAmelCase__ )
return out_string
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
'''simple docstring'''
lowercase__: Any = [self.sep_token_id]
lowercase__: Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
'''simple docstring'''
lowercase__: List[Any] = [self.sep_token_id]
lowercase__: Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error('Vocabulary path ({}) should be a directory'.format(lowerCAmelCase__ ) )
return
lowercase__: str = os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 288 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 ) -> Optional[int]:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowercase__: int = [[w, v]]
if not self.graph.get(lowerCAmelCase__ ):
lowercase__: Union[str, Any] = []
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
return list(self.graph )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Union[str, Any]:
'''simple docstring'''
if s == d:
return []
lowercase__: Tuple = []
lowercase__: Tuple = []
if s == -2:
lowercase__: Any = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Dict = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase__ ) != 0:
lowercase__: Optional[int] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Dict = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-1 ) -> List[str]:
'''simple docstring'''
if c == -1:
lowercase__: int = floor(random() * 10_000 ) + 10
for i in range(lowerCAmelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowercase__: str = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> Dict:
'''simple docstring'''
lowercase__: int = deque()
lowercase__: Dict = []
if s == -2:
lowercase__: Optional[int] = list(self.graph )[0]
d.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
while d:
lowercase__: str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
lowercase__: Tuple = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Tuple = []
lowercase__: str = []
if s == -2:
lowercase__: Dict = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: List[Any] = s
lowercase__: Any = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: List[str] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Dict = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCAmelCase__ ) != 0:
lowercase__: int = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[int] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return sorted_nodes
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: List[Any] = []
lowercase__: int = []
lowercase__: List[str] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = -2
lowercase__: Union[str, Any] = []
lowercase__: List[str] = s
lowercase__: Dict = False
lowercase__: Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: List[Any] = len(lowerCAmelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: Any = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Optional[Any] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Union[str, Any] = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: int = s
lowercase__: str = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return list(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Any = []
lowercase__: int = []
lowercase__: Dict = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Optional[int] = -2
lowercase__: List[Any] = []
lowercase__: List[str] = s
lowercase__: List[Any] = False
lowercase__: str = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Any = len(lowerCAmelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: Optional[Any] = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Any = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[Any] = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Dict = s
lowercase__: Dict = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return False
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Dict:
'''simple docstring'''
lowercase__: Union[str, Any] = time()
self.dfs(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: Optional[Any] = time()
return end - begin
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[str]:
'''simple docstring'''
lowercase__: str = time()
self.bfs(lowerCAmelCase__ )
lowercase__: List[str] = time()
return end - begin
class __a :
def __init__( self ) -> Tuple:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 ) -> List[Any]:
'''simple docstring'''
# check if the u exists
if self.graph.get(lowerCAmelCase__ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowercase__: str = [[w, v]]
# add the other way
if self.graph.get(lowerCAmelCase__ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowercase__: Union[str, Any] = [[w, u]]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase__ )
# the other way round
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
lowercase__: str = []
lowercase__: int = []
if s == -2:
lowercase__: Tuple = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: int = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: int = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase__ ) != 0:
lowercase__: Union[str, Any] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[Any] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
lowercase__: Any = floor(random() * 10_000 ) + 10
for i in range(lowerCAmelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowercase__: Optional[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[Any]:
'''simple docstring'''
lowercase__: str = deque()
lowercase__: List[Any] = []
if s == -2:
lowercase__: str = list(self.graph )[0]
d.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
while d:
lowercase__: Union[str, Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: str = []
lowercase__: Dict = []
lowercase__: Optional[int] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = -2
lowercase__: Dict = []
lowercase__: List[Any] = s
lowercase__: Union[str, Any] = False
lowercase__: List[str] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Any = len(lowerCAmelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: str = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Dict = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: int = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Tuple = s
lowercase__: List[Any] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return list(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: Tuple = []
lowercase__: Optional[int] = []
lowercase__: Optional[Any] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Tuple = -2
lowercase__: Any = []
lowercase__: int = s
lowercase__: Optional[int] = False
lowercase__: List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Union[str, Any] = len(lowerCAmelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: List[str] = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: List[str] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Dict = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Optional[Any] = s
lowercase__: Optional[int] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return False
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
return list(self.graph )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Dict = time()
self.dfs(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: List[Any] = time()
return end - begin
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[Any]:
'''simple docstring'''
lowercase__: str = time()
self.bfs(lowerCAmelCase__ )
lowercase__: List[str] = time()
return end - begin
| 288 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : jnp.ndarray
@flax_register_to_config
class __lowerCAmelCase ( nn.Module , __magic_name__ , __magic_name__ ):
"""simple docstring"""
_snake_case : int = 3_2
_snake_case : int = 4
_snake_case : int = 4
_snake_case : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_snake_case : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
_snake_case : Union[bool, Tuple[bool]] = False
_snake_case : Tuple[int] = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
_snake_case : int = 2
_snake_case : Union[int, Tuple[int]] = 8
_snake_case : Optional[Union[int, Tuple[int]]] = None
_snake_case : int = 1_2_8_0
_snake_case : float = 0.0
_snake_case : bool = False
_snake_case : jnp.dtype = jnp.floataa
_snake_case : bool = True
_snake_case : int = 0
_snake_case : bool = False
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : jax.random.KeyArray ) -> FrozenDict:
'''simple docstring'''
_UpperCamelCase = (1, self.in_channels, self.sample_size, self.sample_size)
_UpperCamelCase = jnp.zeros(lowerCAmelCase__ , dtype=jnp.floataa )
_UpperCamelCase = jnp.ones((1,) , dtype=jnp.intaa )
_UpperCamelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_UpperCamelCase , _UpperCamelCase = jax.random.split(lowerCAmelCase__ )
_UpperCamelCase = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )["params"]
def snake_case__ ( self : List[Any] ) -> Any:
'''simple docstring'''
_UpperCamelCase = self.block_out_channels
_UpperCamelCase = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_UpperCamelCase = self.num_attention_heads or self.attention_head_dim
# input
_UpperCamelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_UpperCamelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_UpperCamelCase = FlaxTimestepEmbedding(lowerCAmelCase__ , dtype=self.dtype )
_UpperCamelCase = self.only_cross_attention
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase = (num_attention_heads,) * len(self.down_block_types )
# down
_UpperCamelCase = []
_UpperCamelCase = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_UpperCamelCase = output_channel
_UpperCamelCase = block_out_channels[i]
_UpperCamelCase = i == len(lowerCAmelCase__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_UpperCamelCase = FlaxCrossAttnDownBlockaD(
in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_UpperCamelCase = FlaxDownBlockaD(
in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowerCAmelCase__ )
_UpperCamelCase = down_blocks
# mid
_UpperCamelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
_UpperCamelCase = []
_UpperCamelCase = list(reversed(lowerCAmelCase__ ) )
_UpperCamelCase = list(reversed(lowerCAmelCase__ ) )
_UpperCamelCase = list(reversed(lowerCAmelCase__ ) )
_UpperCamelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_UpperCamelCase = output_channel
_UpperCamelCase = reversed_block_out_channels[i]
_UpperCamelCase = reversed_block_out_channels[min(i + 1 , len(lowerCAmelCase__ ) - 1 )]
_UpperCamelCase = i == len(lowerCAmelCase__ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_UpperCamelCase = FlaxCrossAttnUpBlockaD(
in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , prev_output_channel=lowerCAmelCase__ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_UpperCamelCase = FlaxUpBlockaD(
in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , prev_output_channel=lowerCAmelCase__ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowerCAmelCase__ )
_UpperCamelCase = output_channel
_UpperCamelCase = up_blocks
# out
_UpperCamelCase = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_UpperCamelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : List[str] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : int=None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : bool = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , jnp.ndarray ):
_UpperCamelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowerCAmelCase__ , jnp.ndarray ) and len(timesteps.shape ) == 0:
_UpperCamelCase = timesteps.astype(dtype=jnp.floataa )
_UpperCamelCase = jnp.expand_dims(lowerCAmelCase__ , 0 )
_UpperCamelCase = self.time_proj(lowerCAmelCase__ )
_UpperCamelCase = self.time_embedding(lowerCAmelCase__ )
# 2. pre-process
_UpperCamelCase = jnp.transpose(lowerCAmelCase__ , (0, 2, 3, 1) )
_UpperCamelCase = self.conv_in(lowerCAmelCase__ )
# 3. down
_UpperCamelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase , _UpperCamelCase = down_block(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , deterministic=not train )
else:
_UpperCamelCase , _UpperCamelCase = down_block(lowerCAmelCase__ , lowerCAmelCase__ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_UpperCamelCase = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowerCAmelCase__ , lowerCAmelCase__ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_UpperCamelCase = new_down_block_res_samples
# 4. mid
_UpperCamelCase = self.mid_block(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_UpperCamelCase = down_block_res_samples[-(self.layers_per_block + 1) :]
_UpperCamelCase = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase = up_block(
lowerCAmelCase__ , temb=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , res_hidden_states_tuple=lowerCAmelCase__ , deterministic=not train , )
else:
_UpperCamelCase = up_block(lowerCAmelCase__ , temb=lowerCAmelCase__ , res_hidden_states_tuple=lowerCAmelCase__ , deterministic=not train )
# 6. post-process
_UpperCamelCase = self.conv_norm_out(lowerCAmelCase__ )
_UpperCamelCase = nn.silu(lowerCAmelCase__ )
_UpperCamelCase = self.conv_out(lowerCAmelCase__ )
_UpperCamelCase = jnp.transpose(lowerCAmelCase__ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowerCAmelCase__ )
| 324 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Optional[int] = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : int = 'audio-spectrogram-transformer'
def __init__( self : Optional[Any] , lowerCAmelCase__ : List[str]=768 , lowerCAmelCase__ : Optional[Any]=12 , lowerCAmelCase__ : int=12 , lowerCAmelCase__ : int=3072 , lowerCAmelCase__ : List[str]="gelu" , lowerCAmelCase__ : List[Any]=0.0 , lowerCAmelCase__ : Optional[Any]=0.0 , lowerCAmelCase__ : int=0.02 , lowerCAmelCase__ : Union[str, Any]=1e-1_2 , lowerCAmelCase__ : Any=16 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : List[str]=10 , lowerCAmelCase__ : int=10 , lowerCAmelCase__ : Dict=1024 , lowerCAmelCase__ : Optional[int]=128 , **lowerCAmelCase__ : List[Any] , ) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = initializer_range
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = patch_size
_UpperCamelCase = qkv_bias
_UpperCamelCase = frequency_stride
_UpperCamelCase = time_stride
_UpperCamelCase = max_length
_UpperCamelCase = num_mel_bins
| 324 | 1 |
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 __lowerCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = IFInpaintingPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {"latents"}
def A__ ( self ) -> List[str]:
'''simple docstring'''
return self._get_dummy_components()
def A__ ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> int:
'''simple docstring'''
if str(UpperCAmelCase ).startswith("mps" ):
lowercase_ = torch.manual_seed(UpperCAmelCase )
else:
lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
lowercase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
lowercase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
lowercase_ = {
"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 A__ ( self ) -> List[Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def A__ ( self ) -> str:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def A__ ( self ) -> Dict:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def A__ ( self ) -> Any:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
self._test_save_load_local()
def A__ ( self ) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 357 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = 1
lowercase_ = 3
lowercase_ = (32, 32)
lowercase_ = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCAmelCase )
return image
@property
def A__ ( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
return model
@property
def A__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def A__ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5006 , )
return RobertaSeriesModelWithTransformation(UpperCAmelCase )
@property
def A__ ( self ) -> Dict:
'''simple docstring'''
def extract(*UpperCAmelCase , **UpperCAmelCase ):
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = torch.ones([0] )
def A__ ( self , UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
self.pixel_values.to(UpperCAmelCase )
return self
return Out()
return extract
def A__ ( self ) -> str:
'''simple docstring'''
lowercase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase_ = self.dummy_cond_unet
lowercase_ = PNDMScheduler(skip_prk_steps=UpperCAmelCase )
lowercase_ = self.dummy_vae
lowercase_ = self.dummy_text_encoder
lowercase_ = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
lowercase_ = 77
lowercase_ = self.dummy_image.to(UpperCAmelCase )
lowercase_ = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowercase_ = AltDiffusionImgaImgPipeline(
unet=UpperCAmelCase , scheduler=UpperCAmelCase , vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , safety_checker=UpperCAmelCase , feature_extractor=self.dummy_extractor , )
lowercase_ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCAmelCase )
lowercase_ = alt_pipe.to(UpperCAmelCase )
alt_pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowercase_ = "A painting of a squirrel eating a burger"
lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(0 )
lowercase_ = alt_pipe(
[prompt] , generator=UpperCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=UpperCAmelCase , )
lowercase_ = output.images
lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(0 )
lowercase_ = alt_pipe(
[prompt] , generator=UpperCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=UpperCAmelCase , return_dict=UpperCAmelCase , )[0]
lowercase_ = image[0, -3:, -3:, -1]
lowercase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase_ = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def A__ ( self ) -> str:
'''simple docstring'''
lowercase_ = self.dummy_cond_unet
lowercase_ = PNDMScheduler(skip_prk_steps=UpperCAmelCase )
lowercase_ = self.dummy_vae
lowercase_ = self.dummy_text_encoder
lowercase_ = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
lowercase_ = 77
lowercase_ = self.dummy_image.to(UpperCAmelCase )
# put models in fp16
lowercase_ = unet.half()
lowercase_ = vae.half()
lowercase_ = bert.half()
# make sure here that pndm scheduler skips prk
lowercase_ = AltDiffusionImgaImgPipeline(
unet=UpperCAmelCase , scheduler=UpperCAmelCase , vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , safety_checker=UpperCAmelCase , feature_extractor=self.dummy_extractor , )
lowercase_ = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCAmelCase )
lowercase_ = alt_pipe.to(UpperCAmelCase )
alt_pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowercase_ = "A painting of a squirrel eating a burger"
lowercase_ = torch.manual_seed(0 )
lowercase_ = alt_pipe(
[prompt] , generator=UpperCAmelCase , num_inference_steps=2 , output_type="np" , image=UpperCAmelCase , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
# resize to resolution that is divisible by 8 but not 16 or 32
lowercase_ = init_image.resize((760, 504) )
lowercase_ = "BAAI/AltDiffusion"
lowercase_ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCAmelCase , safety_checker=UpperCAmelCase , )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
pipe.enable_attention_slicing()
lowercase_ = "A fantasy landscape, trending on artstation"
lowercase_ = torch.manual_seed(0 )
lowercase_ = pipe(
prompt=UpperCAmelCase , image=UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCAmelCase , output_type="np" , )
lowercase_ = output.images[0]
lowercase_ = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
lowercase_ = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
lowercase_ = init_image.resize((768, 512) )
lowercase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )
lowercase_ = "BAAI/AltDiffusion"
lowercase_ = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCAmelCase , safety_checker=UpperCAmelCase , )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
pipe.enable_attention_slicing()
lowercase_ = "A fantasy landscape, trending on artstation"
lowercase_ = torch.manual_seed(0 )
lowercase_ = pipe(
prompt=UpperCAmelCase , image=UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , generator=UpperCAmelCase , output_type="np" , )
lowercase_ = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 297 | 0 |
'''simple docstring'''
from math import pow
def UpperCamelCase_( snake_case : int , snake_case : int , snake_case : int , snake_case : int , snake_case : int , ):
'''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_ = int(pow(snake_case , snake_case ) )
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_ , snake_case_ = backtrack(
snake_case , snake_case , current_number + 1 , snake_case , snake_case )
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_ , snake_case_ = backtrack(
snake_case , snake_case , current_number + 1 , snake_case , snake_case )
return current_sum, solutions_count
def UpperCamelCase_( snake_case : int , snake_case : int ):
'''simple docstring'''
if not (1 <= needed_sum <= 1_0_0_0 and 2 <= power <= 1_0):
raise ValueError(
"Invalid input\n"
"needed_sum must be between 1 and 1000, power between 2 and 10." )
return backtrack(snake_case , snake_case , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {'vocab_file': 'spiece.model'}
_lowerCamelCase : Optional[int] = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
}
}
_lowerCamelCase : str = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
# Segments (not really needed)
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : Tuple = 1
_lowerCamelCase : int = 2
_lowerCamelCase : Dict = 3
_lowerCamelCase : Union[str, Any] = 4
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Any = """left"""
def __init__( self : Dict , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[int]=False , _lowerCamelCase : Optional[Any]=True , _lowerCamelCase : str=False , _lowerCamelCase : Optional[Any]="<s>" , _lowerCamelCase : List[str]="</s>" , _lowerCamelCase : Union[str, Any]="<unk>" , _lowerCamelCase : List[Any]="<sep>" , _lowerCamelCase : str="<pad>" , _lowerCamelCase : Dict="<cls>" , _lowerCamelCase : str="<mask>" , _lowerCamelCase : Optional[int]=["<eop>", "<eod>"] , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : Union[str, Any] , ):
"""simple docstring"""
A_ : Optional[int] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
A_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowerCamelCase , remove_space=_lowerCamelCase , keep_accents=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , pad_token=_lowerCamelCase , cls_token=_lowerCamelCase , mask_token=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : str = 3
A_ : Union[str, Any] = do_lower_case
A_ : Tuple = remove_space
A_ : int = keep_accents
A_ : Optional[Any] = vocab_file
A_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@property
def a_ ( self : int ):
"""simple docstring"""
return len(self.sp_model )
def a_ ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Any ):
"""simple docstring"""
A_ : str = self.__dict__.copy()
A_ : Tuple = None
return state
def __setstate__( self : Tuple , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A_ : List[Any] = {}
A_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a_ ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
if self.remove_space:
A_ : str = ''' '''.join(inputs.strip().split() )
else:
A_ : Any = inputs
A_ : List[str] = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
A_ : Any = unicodedata.normalize('''NFKD''' , _lowerCamelCase )
A_ : List[str] = ''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase )] )
if self.do_lower_case:
A_ : str = outputs.lower()
return outputs
def a_ ( self : List[str] , _lowerCamelCase : str ):
"""simple docstring"""
A_ : str = self.preprocess_text(_lowerCamelCase )
A_ : int = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
A_ : List[Any] = []
for piece in pieces:
if len(_lowerCamelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
A_ : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
A_ : Tuple = cur_pieces[1:]
else:
A_ : Tuple = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_lowerCamelCase )
else:
new_pieces.append(_lowerCamelCase )
return new_pieces
def a_ ( self : Any , _lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.sp_model.PieceToId(_lowerCamelCase )
def a_ ( self : Any , _lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.sp_model.IdToPiece(_lowerCamelCase )
def a_ ( self : List[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Any = ''''''.join(_lowerCamelCase ).replace(_lowerCamelCase , ''' ''' ).strip()
return out_string
def a_ ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : bool = False , _lowerCamelCase : bool = None , _lowerCamelCase : bool = True , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : int = kwargs.pop('''use_source_tokenizer''' , _lowerCamelCase )
A_ : List[str] = self.convert_ids_to_tokens(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
A_ : Any = []
A_ : List[Any] = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase ) )
A_ : int = []
sub_texts.append(_lowerCamelCase )
else:
current_sub_text.append(_lowerCamelCase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
A_ : Optional[int] = ''''''.join(_lowerCamelCase )
A_ : Any = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
A_ : Dict = self.clean_up_tokenization(_lowerCamelCase )
return clean_text
else:
return text
def a_ ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
A_ : Optional[int] = [self.sep_token_id]
A_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def a_ ( self : List[str] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
if token_ids_a is not None:
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1, 1]
return ([0] * len(_lowerCamelCase )) + [1, 1]
def a_ ( self : int , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
A_ : List[Any] = [self.sep_token_id]
A_ : Tuple = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def a_ ( self : int , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
A_ : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A_ : str = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 167 | 0 |
"""simple docstring"""
from __future__ import annotations
class A_ :
def __init__( self: Optional[Any] ,__lowerCAmelCase: str ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase, _lowerCamelCase : Any = text, pattern
_lowerCamelCase, _lowerCamelCase : Tuple = len(__SCREAMING_SNAKE_CASE ), len(__SCREAMING_SNAKE_CASE )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: str ):
'''simple docstring'''
for i in range(self.patLen - 1 ,-1 ,-1 ):
if char == self.pattern[i]:
return i
return -1
def _lowercase ( self: str ,__lowerCAmelCase: int ):
'''simple docstring'''
for i in range(self.patLen - 1 ,-1 ,-1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCamelCase : str = self.mismatch_in_text(__SCREAMING_SNAKE_CASE )
if mismatch_index == -1:
positions.append(__SCREAMING_SNAKE_CASE )
else:
_lowerCamelCase : List[str] = self.match_in_pattern(self.text[mismatch_index] )
_lowerCamelCase : Tuple = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_lowerCAmelCase : int = 'ABAABA'
_lowerCAmelCase : Optional[Any] = 'AB'
_lowerCAmelCase : Any = BoyerMooreSearch(text, pattern)
_lowerCAmelCase : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 363 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerCamelCase : Optional[int] = {}
_lowerCamelCase : Optional[int] = tokenizer(example["content"] , truncation=_lowerCamelCase )["input_ids"]
_lowerCamelCase : Dict = len(example["content"] ) / len(output["input_ids"] )
return output
_lowerCAmelCase : Tuple = HfArgumentParser(PretokenizationArguments)
_lowerCAmelCase : Optional[int] = parser.parse_args()
if args.num_workers is None:
_lowerCAmelCase : Any = multiprocessing.cpu_count()
_lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
_lowerCAmelCase : Union[str, Any] = time.time()
_lowerCAmelCase : Optional[int] = load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')
_lowerCAmelCase : Any = time.time()
_lowerCAmelCase : Dict = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')
_lowerCAmelCase : str = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 340 | 0 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : Any = '▁'
UpperCAmelCase__ : Optional[int] = {'vocab_file': 'prophetnet.tokenizer'}
UpperCAmelCase__ : List[Any] = {
'vocab_file': {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
),
}
}
UpperCAmelCase__ : Optional[Any] = {
'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}
UpperCAmelCase__ : List[str] = {
'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = collections.OrderedDict()
with open(__a ,"""r""" ,encoding="""utf-8""" ) as reader:
SCREAMING_SNAKE_CASE__ : int = reader.readlines()
for index, token in enumerate(__a ):
SCREAMING_SNAKE_CASE__ : Optional[int] = token.rstrip("""\n""" )
SCREAMING_SNAKE_CASE__ : List[Any] = index
return vocab
class lowerCAmelCase_ (snake_case_ ):
"""simple docstring"""
__UpperCamelCase : int = VOCAB_FILES_NAMES
__UpperCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : Any = ["input_ids", "attention_mask"]
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="[SEP]" , SCREAMING_SNAKE_CASE__="[SEP]" , SCREAMING_SNAKE_CASE__="[SEP]" , SCREAMING_SNAKE_CASE__="[UNK]" , SCREAMING_SNAKE_CASE__="[PAD]" , SCREAMING_SNAKE_CASE__="[CLS]" , SCREAMING_SNAKE_CASE__="[MASK]" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_A , eos_token=_A , sep_token=_A , unk_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
SCREAMING_SNAKE_CASE__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_A ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 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'
# put special tokens and [unused] tokens into the vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}
for i in range(10 ):
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''[unused{i}]'''
SCREAMING_SNAKE_CASE__ : str = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 12
SCREAMING_SNAKE_CASE__ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(_A )
def __getstate__(self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
return state
def __setstate__(self , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ : Optional[int] = {}
SCREAMING_SNAKE_CASE__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return ([0] * len(_A )) + [1]
return ([0] * len(_A )) + [1] + ([0] * len(_A )) + [1]
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
return self.sp_model.encode(_A , out_type=_A )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE__ : Optional[int] = self.sp_model.PieceToId(_A )
# 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 __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> int:
"""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 __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ''.join(_A ).replace(_A , """ """ ).strip()
return out_string
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 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:
SCREAMING_SNAKE_CASE__ : Dict = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 25 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = 42
def __init__( self , lowercase , lowercase) -> str:
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase)
@torch.no_grad()
def __call__( self , lowercase = 1 , lowercase = 20_00 , lowercase = None , lowercase = "pil" , lowercase = True , **lowercase , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
a__: List[Any] = self.unet.config.sample_size
a__: List[Any] = (batch_size, 3, img_size, img_size)
a__: List[str] = self.unet
a__: int = randn_tensor(lowercase , generator=lowercase) * self.scheduler.init_noise_sigma
a__: Tuple = sample.to(self.device)
self.scheduler.set_timesteps(lowercase)
self.scheduler.set_sigmas(lowercase)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
a__: int = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
a__: Union[str, Any] = self.unet(lowercase , lowercase).sample
a__: List[str] = self.scheduler.step_correct(lowercase , lowercase , generator=lowercase).prev_sample
# prediction step
a__: List[str] = model(lowercase , lowercase).sample
a__: List[Any] = self.scheduler.step_pred(lowercase , lowercase , lowercase , generator=lowercase)
a__ , a__: str = output.prev_sample, output.prev_sample_mean
a__: List[Any] = sample_mean.clamp(0 , 1)
a__: List[Any] = sample.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=lowercase)
| 203 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case :
def __init__( self , lowercase , lowercase=3 , lowercase=32 , lowercase=3 , lowercase=10 , lowercase=[10, 20, 30, 40] , lowercase=[1, 1, 2, 1] , lowercase=True , lowercase=True , lowercase="relu" , lowercase=3 , lowercase=None , ) -> Any:
'''simple docstring'''
a__: Union[str, Any] = parent
a__: int = batch_size
a__: List[Any] = image_size
a__: Any = num_channels
a__: Dict = embeddings_size
a__: str = hidden_sizes
a__: List[Any] = depths
a__: Optional[int] = is_training
a__: Optional[int] = use_labels
a__: Tuple = hidden_act
a__: Any = num_labels
a__: Union[str, Any] = scope
a__: Tuple = len(lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__: str = None
if self.use_labels:
a__: Dict = ids_tensor([self.batch_size] , self.num_labels)
a__: int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: Dict = RegNetModel(config=lowercase)
model.to(lowercase)
model.eval()
a__: List[str] = model(lowercase)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = self.num_labels
a__: Tuple = RegNetForImageClassification(lowercase)
model.to(lowercase)
model.eval()
a__: Any = model(lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: int = self.prepare_config_and_inputs()
a__ , a__ , a__: List[Any] = config_and_inputs
a__: Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
a__ = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
a__ = (
{"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
if is_torch_available()
else {}
)
a__ = False
a__ = False
a__ = False
a__ = False
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Dict = RegNetModelTester(self)
a__: Dict = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''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 lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return
@unittest.skip(reason='RegNet does not use inputs_embeds')
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason='RegNet does not support input and output embeddings')
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__ , a__: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__: int = model_class(lowercase)
a__: str = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__: int = [*signature.parameters.keys()]
a__: List[str] = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__ , a__: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__: int = model_class(config=lowercase)
for name, module in model.named_modules():
if isinstance(lowercase , (nn.BatchNormad, nn.GroupNorm)):
self.assertTrue(
torch.all(module.weight == 1) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
def check_hidden_states_output(lowercase , lowercase , lowercase):
a__: int = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
a__: int = model(**self._prepare_for_class(lowercase , lowercase))
a__: Any = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a__: List[str] = self.model_tester.num_stages
self.assertEqual(len(lowercase) , expected_num_stages + 1)
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
a__ , a__: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
a__: Dict = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
a__: Dict = layer_type
a__: Dict = True
check_hidden_states_output(lowercase , lowercase , lowercase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a__: str = True
check_hidden_states_output(lowercase , lowercase , lowercase)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase)
@slow
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__: Tuple = RegNetModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
def __a ( ) ->Dict:
a__: str = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: Tuple = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase)
a__: Tuple = self.default_image_processor
a__: str = prepare_img()
a__: Optional[int] = image_processor(images=lowercase , return_tensors='pt').to(lowercase)
# forward pass
with torch.no_grad():
a__: List[Any] = model(**lowercase)
# verify the logits
a__: Tuple = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , lowercase)
a__: Optional[Any] = torch.tensor([-0.4180, -1.5051, -3.4836]).to(lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4))
| 203 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : int = "bert-generation"
def __init__( self , a=5_0_3_5_8 , a=1_0_2_4 , a=2_4 , a=1_6 , a=4_0_9_6 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=0.02 , a=1e-12 , a=0 , a=2 , a=1 , a="absolute" , a=True , **a , ) -> Optional[int]:
super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a )
lowercase__ : List[Any] = vocab_size
lowercase__ : List[str] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : str = num_attention_heads
lowercase__ : List[str] = hidden_act
lowercase__ : str = intermediate_size
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = max_position_embeddings
lowercase__ : Optional[Any] = initializer_range
lowercase__ : str = layer_norm_eps
lowercase__ : Tuple = position_embedding_type
lowercase__ : Tuple = use_cache
| 77 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[Any]:
return None
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
return None
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
__lowercase : Dict = [
# (model_name, model_kwargs)
('''bert-base-cased''', {}),
('''gpt2''', {'''use_cache''': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> int:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Any:
from transformers import BertModel
lowerCAmelCase__ : Optional[int] = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
vocab_file.write("""\n""".join(__UpperCAmelCase ) )
vocab_file.flush()
lowerCAmelCase__ : Dict = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCAmelCase__ : Tuple = BertModel(BertConfig(vocab_size=len(__UpperCAmelCase ) ) )
model.save_pretrained(__UpperCAmelCase )
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,__UpperCAmelCase )
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> List[str]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Dict = self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : List[str] = quantize(Path(__UpperCAmelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> List[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Any = self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : Dict = quantize(__UpperCAmelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=None ,**__UpperCAmelCase ) -> Optional[Any]:
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCAmelCase__ : Optional[int] = Path(__UpperCAmelCase ).joinpath("""model.onnx""" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,**__UpperCAmelCase )
return path
except Exception as e:
self.fail(__UpperCAmelCase )
@require_torch
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
from transformers import BertModel
lowerCAmelCase__ : List[Any] = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""pt""" )
@require_tf
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Optional[int]:
from transformers import TFBertModel
lowerCAmelCase__ : int = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Optional[int] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""tf""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
lowerCAmelCase__ : Any = FeatureExtractionPipeline(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : List[str] = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = infer_shapes(__UpperCAmelCase ,__UpperCAmelCase )
# Assert all variables are present
self.assertEqual(len(__UpperCAmelCase ) ,len(__UpperCAmelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] ,__UpperCAmelCase )
self.assertSequenceEqual(variable_names[3:] ,__UpperCAmelCase )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] ,{0: """batch""", 1: """sequence"""} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] ,{0: """batch""", 1: """sequence"""} )
self.assertDictEqual(shapes["""output_1"""] ,{0: """batch"""} )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : List[str] = ["""input_ids""", """attention_mask""", """token_type_ids"""]
lowerCAmelCase__ : Union[str, Any] = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = ensure_valid_input(FuncContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__UpperCAmelCase ) ,3 )
# Should have exactly the same input names
self.assertEqual(set(__UpperCAmelCase ) ,set(__UpperCAmelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__UpperCAmelCase ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCAmelCase__ , lowerCAmelCase__ : int = ensure_valid_input(FuncNonContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__UpperCAmelCase ) ,1 )
self.assertEqual(len(__UpperCAmelCase ) ,1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] ,tokens["""input_ids"""] )
self.assertEqual(ordered_input_names[0] ,"""input_ids""" )
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Dict = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" ) ,"""-test""" )
self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,generated.as_posix() )
| 37 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "pixel_values"
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = TimmBackboneConfig
def __init__( self, lowerCAmelCase__, **lowerCAmelCase__) -> Tuple:
requires_backends(self, 'timm')
super().__init__(lowerCAmelCase__)
snake_case_ = config
if config.backbone is None:
raise ValueError('backbone is not set in the config. Please set it to a timm model name.')
if config.backbone not in timm.list_models():
raise ValueError(f'backbone {config.backbone} is not supported by timm.')
if hasattr(lowerCAmelCase__, 'out_features') and config.out_features is not None:
raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.')
snake_case_ = getattr(lowerCAmelCase__, 'use_pretrained_backbone', lowerCAmelCase__)
if pretrained is None:
raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.')
# We just take the final layer by default. This matches the default for the transformers models.
snake_case_ = config.out_indices if getattr(lowerCAmelCase__, 'out_indices', lowerCAmelCase__) is not None else (-1,)
snake_case_ = timm.create_model(
config.backbone, pretrained=lowerCAmelCase__, features_only=config.features_only, in_chans=config.num_channels, out_indices=lowerCAmelCase__, **lowerCAmelCase__, )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
snake_case_ = self._backbone.return_layers
snake_case_ = {layer['module']: str(lowerCAmelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
super()._init_backbone(lowerCAmelCase__)
@classmethod
def a_ ( cls, lowerCAmelCase__, *lowerCAmelCase__, **lowerCAmelCase__) -> Optional[Any]:
requires_backends(cls, ['vision', 'timm'])
from ...models.timm_backbone import TimmBackboneConfig
snake_case_ = kwargs.pop('config', TimmBackboneConfig())
snake_case_ = kwargs.pop('use_timm_backbone', lowerCAmelCase__)
if not use_timm:
raise ValueError('use_timm_backbone must be True for timm backbones')
snake_case_ = kwargs.pop('num_channels', config.num_channels)
snake_case_ = kwargs.pop('features_only', config.features_only)
snake_case_ = kwargs.pop('use_pretrained_backbone', config.use_pretrained_backbone)
snake_case_ = kwargs.pop('out_indices', config.out_indices)
snake_case_ = TimmBackboneConfig(
backbone=lowerCAmelCase__, num_channels=lowerCAmelCase__, features_only=lowerCAmelCase__, use_pretrained_backbone=lowerCAmelCase__, out_indices=lowerCAmelCase__, )
return super()._from_config(lowerCAmelCase__, **lowerCAmelCase__)
def a_ ( self, lowerCAmelCase__) -> Tuple:
pass
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__=None, lowerCAmelCase__=None, lowerCAmelCase__=None, **lowerCAmelCase__) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError('Cannot output attentions for timm backbones at the moment')
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
snake_case_ = self._all_layers
snake_case_ = self._backbone(lowerCAmelCase__, **lowerCAmelCase__)
snake_case_ = self._return_layers
snake_case_ = tuple(hidden_states[i] for i in self.out_indices)
else:
snake_case_ = self._backbone(lowerCAmelCase__, **lowerCAmelCase__)
snake_case_ = None
snake_case_ = tuple(lowerCAmelCase__)
snake_case_ = tuple(lowerCAmelCase__) if hidden_states is not None else None
if not return_dict:
snake_case_ = (feature_maps,)
if output_hidden_states:
snake_case_ = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=lowerCAmelCase__, hidden_states=lowerCAmelCase__, attentions=lowerCAmelCase__)
| 358 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dataset_infos.json'],
['full:README.md'],
] , )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> List[str]:
snake_case_ = tmp_path_factory.mktemp('dset_infos_dir' )
if "full:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('---\ndataset_info:\n dataset_size: 42\n---' )
if "empty:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / 'dataset_infos.json' , 'w' ) as f:
f.write('{"default": {"dataset_size": 42}}' )
snake_case_ = DatasetInfosDict.from_directory(UpperCAmelCase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'dataset_info' , [
DatasetInfo(),
DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , ),
] , )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
snake_case_ = str(UpperCAmelCase )
dataset_info.write_to_directory(UpperCAmelCase )
snake_case_ = DatasetInfo.from_directory(UpperCAmelCase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(UpperCAmelCase , 'dataset_info.json' ) )
def UpperCAmelCase ( ) -> Union[str, Any]:
snake_case_ = DatasetInfo(
description='foo' , citation='bar' , homepage='https://foo.bar' , license='CC0' , features=Features({'a': Value('int32' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train', 'num_examples': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
snake_case_ = dataset_info._to_yaml_dict()
assert sorted(UpperCAmelCase ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
snake_case_ = yaml.safe_dump(UpperCAmelCase )
snake_case_ = yaml.safe_load(UpperCAmelCase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase ( ) -> Optional[Any]:
snake_case_ = DatasetInfo()
snake_case_ = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'dataset_infos_dict' , [
DatasetInfosDict(),
DatasetInfosDict({'default': DatasetInfo()} ),
DatasetInfosDict({'my_config_name': DatasetInfo()} ),
DatasetInfosDict(
{
'default': DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'v1': DatasetInfo(dataset_size=42 ),
'v2': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> List[str]:
snake_case_ = str(UpperCAmelCase )
dataset_infos_dict.write_to_directory(UpperCAmelCase )
snake_case_ = DatasetInfosDict.from_directory(UpperCAmelCase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
snake_case_ = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
snake_case_ = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(UpperCAmelCase , 'README.md' ) )
| 312 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class snake_case :
"""simple docstring"""
def _lowerCamelCase ( self : Any , __A : Optional[Any] , __A : Any , __A : Union[str, Any] ):
return None
class snake_case :
"""simple docstring"""
def _lowerCamelCase ( self : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Optional[int] ):
return None
class snake_case ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str =[
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _lowerCamelCase ( self : Optional[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__A , 'tf' , 1_2 , **__A )
@require_torch
@slow
def _lowerCamelCase ( self : Optional[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__A , 'pt' , 1_2 , **__A )
@require_torch
@slow
def _lowerCamelCase ( self : Any ):
from transformers import BertModel
__UpperCamelCase = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(__A ) )
vocab_file.flush()
__UpperCamelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
__UpperCamelCase = BertModel(BertConfig(vocab_size=len(__A ) ) )
model.save_pretrained(__A )
self._test_export(__A , 'pt' , 1_2 , __A )
@require_tf
@slow
def _lowerCamelCase ( self : List[str] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
__UpperCamelCase = self._test_export(__A , 'tf' , 1_2 , **__A )
__UpperCamelCase = quantize(Path(__A ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__A ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def _lowerCamelCase ( self : int ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
__UpperCamelCase = self._test_export(__A , 'pt' , 1_2 , **__A )
__UpperCamelCase = quantize(__A )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__A ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def _lowerCamelCase ( self : List[str] , __A : Union[str, Any] , __A : Any , __A : str , __A : Tuple=None , **__A : Tuple ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
__UpperCamelCase = Path(__A ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__A , __A , __A , __A , __A , **__A )
return path
except Exception as e:
self.fail(__A )
@require_torch
@require_tokenizers
@slow
def _lowerCamelCase ( self : Optional[Any] ):
from transformers import BertModel
__UpperCamelCase = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
__UpperCamelCase = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(__A , __A , 'pt' )
@require_tf
@require_tokenizers
@slow
def _lowerCamelCase ( self : Optional[Any] ):
from transformers import TFBertModel
__UpperCamelCase = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
__UpperCamelCase = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(__A , __A , 'tf' )
def _lowerCamelCase ( self : Tuple , __A : int , __A : Optional[int] , __A : int ):
__UpperCamelCase = FeatureExtractionPipeline(__A , __A )
__UpperCamelCase = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = infer_shapes(__A , __A )
# Assert all variables are present
self.assertEqual(len(__A ) , len(__A ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __A )
self.assertSequenceEqual(variable_names[3:] , __A )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: 'batch', 1: 'sequence'} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['output_0'] , {0: 'batch', 1: 'sequence'} )
self.assertDictEqual(shapes['output_1'] , {0: 'batch'} )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = ['input_ids', 'attention_mask', 'token_type_ids']
__UpperCamelCase = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
__UpperCamelCase , __UpperCamelCase = ensure_valid_input(FuncContiguousArgs() , __A , __A )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__A ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__A ) , set(__A ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__A , (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
__UpperCamelCase , __UpperCamelCase = ensure_valid_input(FuncNonContiguousArgs() , __A , __A )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__A ) , 1 )
self.assertEqual(len(__A ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['input_ids'] )
self.assertEqual(ordered_input_names[0] , 'input_ids' )
def _lowerCamelCase ( self : Any ):
__UpperCamelCase = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ) , '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx' , generated.as_posix() )
| 53 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : List[str] = '''▁'''
UpperCAmelCase : Optional[Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class _A( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Optional[int] = BertGenerationTokenizer
UpperCamelCase : str = False
UpperCamelCase : Tuple = True
def UpperCAmelCase_ ( self ):
super().setUp()
__A : Tuple = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase_ ( self ):
__A : str = '<s>'
__A : str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCAmelCase_ ( self ):
__A : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '<pad>' )
self.assertEqual(len(_A ) , 1002 )
def UpperCAmelCase_ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCAmelCase_ ( self ):
__A : str = BertGenerationTokenizer(_A , keep_accents=_A )
__A : Dict = tokenizer.tokenize('This is a test' )
self.assertListEqual(_A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
__A : int = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_A , [
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',
'.',
] , )
__A : Dict = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__A : Optional[int] = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
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>',
'.',
] , )
@cached_property
def UpperCAmelCase_ ( self ):
return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
@slow
def UpperCAmelCase_ ( self ):
__A : List[Any] = 'Hello World!'
__A : Optional[Any] = [18536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCAmelCase_ ( self ):
__A : Dict = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
__A : int = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
34324,
497,
391,
408,
11342,
1244,
385,
100,
938,
985,
456,
574,
362,
12597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCAmelCase_ ( self ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
__A : Tuple = list(self.big_tokenizer.get_vocab().keys() )[:10]
__A : List[Any] = ' '.join(_A )
__A : Union[str, Any] = self.big_tokenizer.encode_plus(_A , return_tensors='pt' , return_token_type_ids=_A )
__A : Optional[Any] = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] , return_tensors='pt' , return_token_type_ids=_A )
__A : int = BertGenerationConfig()
__A : List[str] = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCAmelCase_ ( self ):
# fmt: off
__A : str = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='google/bert_for_seq_generation_L-24_bbc_encoder' , revision='c817d1fd1be2ffa69431227a1fe320544943d4db' , )
| 280 | 0 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowerCamelCase__ ():
print(sum_of_series(1 , 1 , 10))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
a_ : Tuple = '\\n@inproceedings{lin-2004-rouge,\n title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n author = "Lin, Chin-Yew",\n booktitle = "Text Summarization Branches Out",\n month = jul,\n year = "2004",\n address = "Barcelona, Spain",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W04-1013",\n pages = "74--81",\n}\n'
a_ : List[Any] = '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'
a_ : List[str] = '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n `"rougeL"`: Longest common subsequence based scoring.\n `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric(\'rouge\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n >>> print(results["rouge1"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results["rouge1"].mid.fmeasure)\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge'] , reference_urls=[
'https://en.wikipedia.org/wiki/ROUGE_(metric)',
'https://github.com/google-research/google-research/tree/master/rouge',
] , )
def SCREAMING_SNAKE_CASE__ ( self , a , a , a=None , a=True , a=False) -> Optional[Any]:
if rouge_types is None:
SCREAMING_SNAKE_CASE = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
SCREAMING_SNAKE_CASE = rouge_scorer.RougeScorer(rouge_types=a , use_stemmer=a)
if use_aggregator:
SCREAMING_SNAKE_CASE = scoring.BootstrapAggregator()
else:
SCREAMING_SNAKE_CASE = []
for ref, pred in zip(a , a):
SCREAMING_SNAKE_CASE = scorer.score(a , a)
if use_aggregator:
aggregator.add_scores(a)
else:
scores.append(a)
if use_aggregator:
SCREAMING_SNAKE_CASE = aggregator.aggregate()
else:
SCREAMING_SNAKE_CASE = {}
for key in scores[0]:
SCREAMING_SNAKE_CASE = [score[key] for score in scores]
return result
| 327 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = logging.getLogger(__name__)
@dataclass(frozen=UpperCAmelCase__ )
class __lowercase :
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
@dataclass(frozen=UpperCAmelCase__ )
class __lowercase :
_UpperCamelCase = 42
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class __lowercase (UpperCAmelCase__ ):
_UpperCamelCase = 42
def __init__( self , A_ , A_ , A_ , A_ = None , A_=False , A_ = False , ) ->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : Tuple = hans_processors[task]()
__lowerCAmelCase : Tuple = os.path.join(
_UpperCAmelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(_UpperCAmelCase ) , _UpperCAmelCase , ) , )
__lowerCAmelCase : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowerCAmelCase, __lowerCAmelCase : str = label_list[2], label_list[1]
__lowerCAmelCase : int = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase : Optional[int] = cached_features_file + '''.lock'''
with FileLock(_UpperCAmelCase ):
if os.path.exists(_UpperCAmelCase ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
__lowerCAmelCase : Optional[int] = torch.load(_UpperCAmelCase )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
__lowerCAmelCase : Optional[Any] = (
processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
)
logger.info('''Training examples: %s''' , len(_UpperCAmelCase ) )
__lowerCAmelCase : List[Any] = hans_convert_examples_to_features(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
logger.info('''Saving features into cached file %s''' , _UpperCAmelCase )
torch.save(self.features , _UpperCAmelCase )
def __len__( self ) ->int:
'''simple docstring'''
return len(self.features )
def __getitem__( self , A_ ) ->InputFeatures:
'''simple docstring'''
return self.features[i]
def UpperCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class __lowercase :
_UpperCamelCase = 42
def __init__( self , A_ , A_ , A_ , A_ = 128 , A_=False , A_ = False , ) ->Dict:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = hans_processors[task]()
__lowerCAmelCase : int = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowerCAmelCase, __lowerCAmelCase : str = label_list[2], label_list[1]
__lowerCAmelCase : int = label_list
__lowerCAmelCase : Optional[Any] = processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
__lowerCAmelCase : Dict = hans_convert_examples_to_features(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 1_0000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(_UpperCAmelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
__lowerCAmelCase : Optional[int] = tf.data.Dataset.from_generator(
_UpperCAmelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def UpperCamelCase__ ( self ) ->Optional[Any]:
'''simple docstring'''
return self.dataset
def __len__( self ) ->Dict:
'''simple docstring'''
return len(self.features )
def __getitem__( self , A_ ) ->InputFeatures:
'''simple docstring'''
return self.features[i]
def UpperCamelCase__ ( self ) ->Any:
'''simple docstring'''
return self.label_list
class __lowercase (UpperCAmelCase__ ):
def UpperCamelCase__ ( self , A_ ) ->Optional[Any]:
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def UpperCamelCase__ ( self , A_ ) ->str:
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def UpperCamelCase__ ( self ) ->Any:
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def UpperCamelCase__ ( self , A_ , A_ ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = []
for i, line in enumerate(_UpperCAmelCase ):
if i == 0:
continue
__lowerCAmelCase : Union[str, Any] = '''%s-%s''' % (set_type, line[0])
__lowerCAmelCase : Any = line[5]
__lowerCAmelCase : Dict = line[6]
__lowerCAmelCase : List[str] = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
__lowerCAmelCase : Dict = line[0]
examples.append(InputExample(guid=_UpperCAmelCase , text_a=_UpperCAmelCase , text_b=_UpperCAmelCase , label=_UpperCAmelCase , pairID=_UpperCAmelCase ) )
return examples
def _lowercase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
__lowerCAmelCase : int = {label: i for i, label in enumerate(lowercase__ )}
__lowerCAmelCase : Tuple = []
for ex_index, example in tqdm.tqdm(enumerate(lowercase__ ) , desc='''convert examples to features''' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d''' % (ex_index) )
__lowerCAmelCase : List[Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowercase__ , max_length=lowercase__ , padding='''max_length''' , truncation=lowercase__ , return_overflowing_tokens=lowercase__ , )
__lowerCAmelCase : Tuple = label_map[example.label] if example.label in label_map else 0
__lowerCAmelCase : List[str] = int(example.pairID )
features.append(InputFeatures(**lowercase__ , label=lowercase__ , pairID=lowercase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
_UpperCamelCase = {
'hans': 3,
}
_UpperCamelCase = {
'hans': HansProcessor,
}
| 275 |
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 IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.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 , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
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 , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
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(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305 | 0 |
'''simple docstring'''
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = arr.split(""",""" )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = [int(self.array[0] )] * len(self.array )
_lowerCAmelCase = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
_lowerCAmelCase = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
_lowerCAmelCase = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
_lowercase = input("""please input some numbers:""")
_lowercase = SubArray(whole_array)
_lowercase = array.solve_sub_array()
print(("""the results is:""", re))
| 355 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = str(id_ )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = []
_lowerCAmelCase = {} # {vertex:distance}
def __lt__( self , _lowercase ):
"""simple docstring"""
return self.key < other.key
def __repr__( self ):
"""simple docstring"""
return self.id
def _lowercase ( self , _lowercase ):
"""simple docstring"""
self.neighbors.append(_lowercase )
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = weight
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :Union[str, Any] , __lowerCamelCase :Dict , __lowerCamelCase :Optional[int] ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __lowerCamelCase )
graph[b - 1].add_edge(graph[a - 1] , __lowerCamelCase )
def A (__lowerCamelCase :list , __lowerCamelCase :Vertex ):
_lowerCAmelCase = []
for u in graph:
_lowerCAmelCase = math.inf
_lowerCAmelCase = None
_lowerCAmelCase = 0
_lowerCAmelCase = graph[:]
while q:
_lowerCAmelCase = min(__lowerCamelCase )
q.remove(__lowerCamelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_lowerCAmelCase = u
_lowerCAmelCase = u.edges[v.id]
for i in range(1 , len(__lowerCamelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def A (__lowerCamelCase :list , __lowerCamelCase :Vertex ):
for u in graph:
_lowerCAmelCase = math.inf
_lowerCAmelCase = None
_lowerCAmelCase = 0
_lowerCAmelCase = list(__lowerCamelCase )
hq.heapify(__lowerCamelCase )
while h:
_lowerCAmelCase = hq.heappop(__lowerCamelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_lowerCAmelCase = u
_lowerCAmelCase = u.edges[v.id]
hq.heapify(__lowerCamelCase )
for i in range(1 , len(__lowerCamelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def A ():
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 229 | 0 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if n == 1 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
return 0
elif n == 2:
return 1
else:
_lowerCAmelCase : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : List[str] = 2
while digits < n:
index += 1
_lowerCAmelCase : int = len(str(fibonacci(_lowerCamelCase ) ) )
return index
def A ( _lowerCamelCase = 1_000 ):
'''simple docstring'''
return fibonacci_digits_index(_lowerCamelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 36 |
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"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 UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'swin'
lowerCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=[2, 2, 6, 2], __a=[3, 6, 12, 24], __a=7, __a=4.0, __a=True, __a=0.0, __a=0.0, __a=0.1, __a="gelu", __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = image_size
_lowerCAmelCase : Union[str, Any] = patch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : List[Any] = embed_dim
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Optional[Any] = len(__a)
_lowerCAmelCase : int = num_heads
_lowerCAmelCase : int = window_size
_lowerCAmelCase : int = mlp_ratio
_lowerCAmelCase : List[Any] = qkv_bias
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Optional[int] = layer_norm_eps
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Tuple = 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
_lowerCAmelCase : List[str] = int(embed_dim * 2 ** (len(__a) - 1))
_lowerCAmelCase : List[Any] = ["stem"] + [f"stage{idx}" for idx in range(1, len(__a) + 1)]
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCAmelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = torch.load(lowercase_ , map_location='cpu' )
__snake_case : Optional[int] = chkpt['model']
# We have the base model one level deeper than the original XLM repository
__snake_case : List[Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
__snake_case : List[str] = v
else:
__snake_case : int = v
__snake_case : List[str] = chkpt['params']
__snake_case : Tuple = {n: v for n, v in config.items() if not isinstance(lowercase_ , (torch.FloatTensor, numpy.ndarray) )}
__snake_case : str = chkpt['dico_word2id']
__snake_case : Optional[Any] = {s + '</w>' if s.find('@@' ) == -1 and i > 13 else s.replace('@@' , '' ): i for s, i in vocab.items()}
# Save pytorch-model
__snake_case : Any = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__snake_case : Tuple = pytorch_dump_folder_path + '/' + CONFIG_NAME
__snake_case : Optional[int] = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(lowercase_ , lowercase_ )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(lowercase_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(lowercase_ , indent=2 ) + '\n' )
print(F"Save vocab file to {pytorch_config_dump_path}" )
with open(lowercase_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(lowercase_ , indent=2 ) + '\n' )
if __name__ == "__main__":
_a : str= argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_a : str= parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 362 |
"""simple docstring"""
from __future__ import annotations
import math
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> bool:
'''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(UpperCAmelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
_a : Any= [num for num in range(3, 100_001, 2) if not is_prime(num)]
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> list[int]:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('n must be an integer' )
if n <= 0:
raise ValueError('n must be >= 0' )
__snake_case : int = []
for num in range(len(UpperCAmelCase_ ) ):
__snake_case : List[str] = 0
while 2 * i * i <= odd_composites[num]:
__snake_case : List[Any] = odd_composites[num] - 2 * i * i
if is_prime(UpperCAmelCase_ ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(UpperCAmelCase_ ) == n:
return list_nums
return []
def __UpperCAmelCase ( ) -> int:
'''simple docstring'''
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 95 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
A_ :List[Any] = logging.get_logger(__name__)
def A ( a_ ,a_ ) -> List[Any]:
__UpperCamelCase : int =nn.functional.normalize(a_ )
__UpperCamelCase : List[Any] =nn.functional.normalize(a_ )
return torch.mm(a_ ,normalized_text_embeds.t() )
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : Dict =CLIPConfig
UpperCamelCase__ : Union[str, Any] =["""CLIPEncoderLayer"""]
def __init__( self , lowerCamelCase__ ):
"""simple docstring"""
super().__init__(lowerCamelCase__ )
__UpperCamelCase : List[Any] =CLIPVisionModel(config.vision_config )
__UpperCamelCase : str =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=lowerCamelCase__ )
__UpperCamelCase : Dict =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=lowerCamelCase__ )
__UpperCamelCase : Optional[int] =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=lowerCamelCase__ )
__UpperCamelCase : Dict =nn.Parameter(torch.ones(17 ) , requires_grad=lowerCamelCase__ )
__UpperCamelCase : Dict =nn.Parameter(torch.ones(3 ) , requires_grad=lowerCamelCase__ )
@torch.no_grad()
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =self.vision_model(lowerCamelCase__ )[1] # pooled_output
__UpperCamelCase : Any =self.visual_projection(lowerCamelCase__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__UpperCamelCase : Optional[int] =cosine_distance(lowerCamelCase__ , self.special_care_embeds ).cpu().float().numpy()
__UpperCamelCase : Union[str, Any] =cosine_distance(lowerCamelCase__ , self.concept_embeds ).cpu().float().numpy()
__UpperCamelCase : Optional[Any] =[]
__UpperCamelCase : Tuple =image_embeds.shape[0]
for i in range(lowerCamelCase__ ):
__UpperCamelCase : Dict ={'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
__UpperCamelCase : int =0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
__UpperCamelCase : Tuple =special_cos_dist[i][concept_idx]
__UpperCamelCase : Optional[Any] =self.special_care_embeds_weights[concept_idx].item()
__UpperCamelCase : Tuple =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]} )
__UpperCamelCase : Optional[int] =0.01
for concept_idx in range(len(cos_dist[0] ) ):
__UpperCamelCase : Optional[int] =cos_dist[i][concept_idx]
__UpperCamelCase : str =self.concept_embeds_weights[concept_idx].item()
__UpperCamelCase : Any =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowerCamelCase__ )
result.append(lowerCamelCase__ )
__UpperCamelCase : Optional[Any] =[len(res['bad_concepts'] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Any =self.vision_model(lowerCamelCase__ )[1] # pooled_output
__UpperCamelCase : List[str] =self.visual_projection(lowerCamelCase__ )
__UpperCamelCase : List[Any] =cosine_distance(lowerCamelCase__ , self.special_care_embeds )
__UpperCamelCase : List[Any] =cosine_distance(lowerCamelCase__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
__UpperCamelCase : List[Any] =0.0
__UpperCamelCase : Union[str, Any] =special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
__UpperCamelCase : Dict =torch.any(special_scores > 0 , dim=1 )
__UpperCamelCase : Optional[int] =special_care * 0.01
__UpperCamelCase : Dict =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
__UpperCamelCase : Optional[Any] =(cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
__UpperCamelCase : Optional[int] =torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 71 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.environ['GITHUB_TOKEN'] )
__UpperCamelCase : Union[str, Any] =g.get_repo('huggingface/accelerate' )
__UpperCamelCase : Tuple =repo.get_issues(state='open' )
for issue in open_issues:
__UpperCamelCase : List[Any] =sorted([comment for comment in issue.get_comments()] ,key=lambda a_ : i.created_at ,reverse=a_ )
__UpperCamelCase : str =comments[0] if len(a_ ) > 0 else None
__UpperCamelCase : Any =dt.utcnow()
__UpperCamelCase : List[str] =(current_time - issue.updated_at).days
__UpperCamelCase : Union[str, Any] =(current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 71 | 1 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a__ ( __SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase: Any = checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[str] = flatten_dict(__SCREAMING_SNAKE_CASE )
return flax_params
def a__ ( __SCREAMING_SNAKE_CASE ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = {}
__lowerCAmelCase: Optional[Any] = {
"token_embedder": "embeddings",
"encoder_norm": "layernorm",
"kernel": "weight",
".out": ".output",
"scale": "weight",
"embedders_0.pos_embedding": "row_embedder.weight",
"embedders_1.pos_embedding": "column_embedder.weight",
}
__lowerCAmelCase: Optional[Any] = {
"query": "attention.query",
"key": "attention.key",
"value": "attention.value",
"output.dense": "output",
"encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
"pre_self_attention_layer_norm": "self_attention.layer_norm",
"pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
"mlp.": "mlp.DenseReluDense.",
"pre_mlp_layer_norm": "mlp.layer_norm",
"self_attention.o": "self_attention.attention.o",
"decoder.embeddings.embedding": "decoder.embed_tokens.weight",
"decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
"decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
"decoder.logits_dense.weight": "decoder.lm_head.weight",
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
__lowerCAmelCase: str = ".".join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
__lowerCAmelCase: int = new_key.replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
__lowerCAmelCase: Optional[Any] = new_key.replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
__lowerCAmelCase: Union[str, Any] = re.sub(R"layers_(\d+)" , R"layer.\1" , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = new_key.replace("encoder" , "encoder.encoder" )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
__lowerCAmelCase: Tuple = re.sub(R"layers_(\d+)" , R"layer.\1" , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = flax_dict[key]
__lowerCAmelCase: Dict = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
__lowerCAmelCase: Optional[Any] = torch.from_numpy(converted_dict[key].T )
else:
__lowerCAmelCase: str = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False ) -> Dict:
__lowerCAmelCase: str = get_flax_param(__SCREAMING_SNAKE_CASE )
if not use_large:
__lowerCAmelCase: List[Any] = PixaStructVisionConfig()
__lowerCAmelCase: Union[str, Any] = PixaStructTextConfig()
else:
__lowerCAmelCase: str = PixaStructVisionConfig(
hidden_size=1_5_3_6 , d_ff=3_9_6_8 , num_attention_heads=2_4 , num_hidden_layers=1_8 )
__lowerCAmelCase: Union[str, Any] = PixaStructTextConfig(hidden_size=1_5_3_6 , d_ff=3_9_6_8 , num_heads=2_4 , num_layers=1_8 )
__lowerCAmelCase: List[Any] = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer" )
__lowerCAmelCase: Any = PixaStructImageProcessor()
__lowerCAmelCase: Union[str, Any] = PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
if use_large:
__lowerCAmelCase: Union[str, Any] = 4_0_9_6
__lowerCAmelCase: Union[str, Any] = True
# mkdir if needed
os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
print("Model saved in {}".format(__SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--use_large", action="store_true", help="Use large model.")
parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
__A = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 108 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase: int = args.log_outputs
__lowerCAmelCase: Tuple = "_".join(args.dataset.split("/" ) + [args.config, args.split] )
# load metric
__lowerCAmelCase: str = load_metric("wer" )
__lowerCAmelCase: int = load_metric("cer" )
# compute metrics
__lowerCAmelCase: Optional[Any] = wer.compute(references=result["target"] , predictions=result["prediction"] )
__lowerCAmelCase: Dict = cer.compute(references=result["target"] , predictions=result["prediction"] )
# print & log results
__lowerCAmelCase: Optional[int] = F"WER: {wer_result}\nCER: {cer_result}"
print(__SCREAMING_SNAKE_CASE )
with open(F"{dataset_id}_eval_results.txt" , "w" ) as f:
f.write(__SCREAMING_SNAKE_CASE )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
__lowerCAmelCase: int = F"log_{dataset_id}_predictions.txt"
__lowerCAmelCase: Tuple = F"log_{dataset_id}_targets.txt"
with open(__SCREAMING_SNAKE_CASE , "w" ) as p, open(__SCREAMING_SNAKE_CASE , "w" ) as t:
# mapping function to write output
def write_to_file(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
p.write(F"{i}" + "\n" )
p.write(batch["prediction"] + "\n" )
t.write(F"{i}" + "\n" )
t.write(batch["target"] + "\n" )
result.map(__SCREAMING_SNAKE_CASE , with_indices=__SCREAMING_SNAKE_CASE )
def a__ ( __SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase: Any = "[,?.!\-\;\:\"“%‘”�—’…–]" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
__lowerCAmelCase: List[Any] = re.sub(__SCREAMING_SNAKE_CASE , "" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
__lowerCAmelCase: Optional[int] = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
__lowerCAmelCase: str = " ".join(text.split(__SCREAMING_SNAKE_CASE ) )
return text
def a__ ( __SCREAMING_SNAKE_CASE ) -> Dict:
# load dataset
__lowerCAmelCase: List[str] = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__SCREAMING_SNAKE_CASE )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
__lowerCAmelCase: Dict = AutoFeatureExtractor.from_pretrained(args.model_id )
__lowerCAmelCase: str = feature_extractor.sampling_rate
# resample audio
__lowerCAmelCase: Tuple = dataset.cast_column("audio" , Audio(sampling_rate=__SCREAMING_SNAKE_CASE ) )
# load eval pipeline
if args.device is None:
__lowerCAmelCase: List[Any] = 0 if torch.cuda.is_available() else -1
__lowerCAmelCase: Optional[int] = pipeline("automatic-speech-recognition" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Union[str, Any] = asr(
batch["audio"]["array"] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
__lowerCAmelCase: Any = prediction["text"]
__lowerCAmelCase: List[str] = normalize_text(batch["sentence"] )
return batch
# run inference on all examples
__lowerCAmelCase: str = dataset.map(__SCREAMING_SNAKE_CASE , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
__A = parser.parse_args()
main(args)
| 108 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
A_ = TypeVar("T")
class _snake_case ( Generic[T] ):
def __init__( self : int ,SCREAMING_SNAKE_CASE__ : T ):
SCREAMING_SNAKE_CASE:str = data
SCREAMING_SNAKE_CASE:Node[T] | None = None
def __str__( self : Optional[Any] ):
return F'''{self.data}'''
class _snake_case ( Generic[T] ):
def __init__( self : Optional[int] ):
SCREAMING_SNAKE_CASE:Node[T] | None = None
def __iter__( self : str ):
SCREAMING_SNAKE_CASE:Any = self.top
while node:
yield node.data
SCREAMING_SNAKE_CASE:str = node.next
def __str__( self : str ):
return "->".join([str(SCREAMING_SNAKE_CASE__ ) for item in self] )
def __len__( self : List[str] ):
return len(tuple(iter(self ) ) )
def __UpperCamelCase ( self : str ):
return self.top is None
def __UpperCamelCase ( self : str ,SCREAMING_SNAKE_CASE__ : T ):
SCREAMING_SNAKE_CASE:List[Any] = Node(SCREAMING_SNAKE_CASE__ )
if not self.is_empty():
SCREAMING_SNAKE_CASE:Union[str, Any] = self.top
SCREAMING_SNAKE_CASE:Any = node
def __UpperCamelCase ( self : Tuple ):
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:int = self.top
SCREAMING_SNAKE_CASE:Optional[Any] = self.top.next
return pop_node.data
def __UpperCamelCase ( self : Dict ):
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def __UpperCamelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE:Any = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 139 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case , snake_case , snake_case , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
raise ValueError("Stress cannot be negative" )
elif tangential_force < 0:
raise ValueError("Tangential Force cannot be negative" )
elif area < 0:
raise ValueError("Area cannot be negative" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 139 | 1 |
'''simple docstring'''
import random
class lowercase :
"""simple docstring"""
@staticmethod
def _snake_case ( a_ ) -> Dict:
_UpperCAmelCase : int = [ord(a_ ) for i in text]
_UpperCAmelCase : int = []
_UpperCAmelCase : str = []
for i in plain:
_UpperCAmelCase : Tuple = random.randint(1 ,300 )
_UpperCAmelCase : List[Any] = (i + k) * k
cipher.append(a_ )
key.append(a_ )
return cipher, key
@staticmethod
def _snake_case ( a_ ,a_ ) -> int:
_UpperCAmelCase : Optional[int] = []
for i in range(len(a_ ) ):
_UpperCAmelCase : List[str] = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(a_ ) )
return "".join(a_ )
if __name__ == "__main__":
A_ : Optional[int] = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 359 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
SCREAMING_SNAKE_CASE : Dict = getLogger(__name__)
SCREAMING_SNAKE_CASE : int = "cuda" if torch.cuda.is_available() else "cpu"
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 8 , lowerCamelCase_ = DEFAULT_DEVICE , lowerCamelCase_=False , lowerCamelCase_="summarization" , lowerCamelCase_=None , **lowerCamelCase_ , ) -> Dict:
_lowercase : Any = Path(UpperCamelCase_ ).open('w' , encoding='utf-8' )
_lowercase : Tuple = str(UpperCamelCase_ )
_lowercase : Dict = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_ ).to(UpperCamelCase_ )
if fpaa:
_lowercase : List[str] = model.half()
_lowercase : Union[str, Any] = AutoTokenizer.from_pretrained(UpperCamelCase_ )
logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
_lowercase : Tuple = time.time()
# update config with task specific params
use_task_specific_params(UpperCamelCase_ , UpperCamelCase_ )
if prefix is None:
_lowercase : List[Any] = prefix or getattr(model.config , 'prefix' , '' ) or ''
for examples_chunk in tqdm(list(chunks(UpperCamelCase_ , UpperCamelCase_ ) ) ):
_lowercase : List[Any] = [prefix + text for text in examples_chunk]
_lowercase : str = tokenizer(UpperCamelCase_ , return_tensors='pt' , truncation=UpperCamelCase_ , padding='longest' ).to(UpperCamelCase_ )
_lowercase : Optional[int] = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **UpperCamelCase_ , )
_lowercase : str = tokenizer.batch_decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
_lowercase : Union[str, Any] = int(time.time() - start_time ) # seconds
_lowercase : Union[str, Any] = len(UpperCamelCase_ )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def UpperCamelCase_( ) -> List[Any]:
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def UpperCamelCase_( lowerCamelCase_=True ) -> Any:
_lowercase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('model_name' , type=UpperCamelCase_ , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path' , type=UpperCamelCase_ , help='like cnn_dm/test.source' )
parser.add_argument('save_path' , type=UpperCamelCase_ , help='where to save summaries' )
parser.add_argument('--reference_path' , type=UpperCamelCase_ , required=UpperCamelCase_ , help='like cnn_dm/test.target' )
parser.add_argument('--score_path' , type=UpperCamelCase_ , required=UpperCamelCase_ , default='metrics.json' , help='where to save metrics' )
parser.add_argument('--device' , type=UpperCamelCase_ , required=UpperCamelCase_ , default=UpperCamelCase_ , help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix' , type=UpperCamelCase_ , required=UpperCamelCase_ , default=UpperCamelCase_ , help='will be added to the begininng of src examples' )
parser.add_argument('--task' , type=UpperCamelCase_ , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=UpperCamelCase_ , default=8 , required=UpperCamelCase_ , help='batch size' )
parser.add_argument(
'--n_obs' , type=UpperCamelCase_ , default=-1 , required=UpperCamelCase_ , help='How many observations. Defaults to all.' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--dump-args' , action='store_true' , help='print the custom hparams with the results' )
parser.add_argument(
'--info' , nargs='?' , type=UpperCamelCase_ , const=datetime_now() , help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
_lowercase , _lowercase : str = parser.parse_known_args()
_lowercase : List[Any] = parse_numeric_n_bool_cl_kwargs(UpperCamelCase_ )
if parsed_args and verbose:
print(F'''parsed the following generate kwargs: {parsed_args}''' )
_lowercase : Tuple = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
_lowercase : List[str] = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=UpperCamelCase_ )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
_lowercase : List[str] = generate_summaries_or_translations(
UpperCamelCase_ , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **UpperCamelCase_ , )
if args.reference_path is None:
return {}
# Compute scores
_lowercase : Optional[Any] = calculate_bleu if 'translation' in args.task else calculate_rouge
_lowercase : List[Any] = [x.rstrip() for x in open(args.save_path ).readlines()]
_lowercase : str = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(UpperCamelCase_ )]
_lowercase : Dict = score_fn(UpperCamelCase_ , UpperCamelCase_ )
scores.update(UpperCamelCase_ )
if args.dump_args:
scores.update(UpperCamelCase_ )
if args.info:
_lowercase : List[str] = args.info
if verbose:
print(UpperCamelCase_ )
if args.score_path is not None:
json.dump(UpperCamelCase_ , open(args.score_path , 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 21 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(UpperCamelCase_ ) * abs(UpperCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 343 | 0 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCamelCase_ ( A__ : str , A__ : Union[str, Any] , A__ : Union[str, Any] , A__ : Any=5 ):
'''simple docstring'''
assert masked_input.count("""<mask>""" ) == 1
lowerCAmelCase_ : Tuple = torch.tensor(tokenizer.encode(A__ , add_special_tokens=A__ ) ).unsqueeze(0 ) # Batch size 1
lowerCAmelCase_ : str = model(A__ )[0] # The last hidden-state is the first element of the output tuple
lowerCAmelCase_ : int = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
lowerCAmelCase_ : Optional[Any] = logits[0, masked_index, :]
lowerCAmelCase_ : List[Any] = logits.softmax(dim=0 )
lowerCAmelCase_, lowerCAmelCase_ : List[str] = prob.topk(k=A__ , dim=0 )
lowerCAmelCase_ : Union[str, Any] = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(A__ ) )] )
lowerCAmelCase_ : Tuple = tokenizer.mask_token
lowerCAmelCase_ : Optional[int] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
lowerCAmelCase_ : Optional[int] = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(A__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(A__ ) , A__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(A__ , A__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
__A : Union[str, Any] = CamembertTokenizer.from_pretrained("camembert-base")
__A : List[str] = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
__A : Dict = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 89 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : Optional[Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( A__ : List[Any] , A__ : str=False ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith("""head""" ):
lowerCAmelCase_ : str = """segformer.encoder.""" + key
if key.startswith("""backbone""" ):
lowerCAmelCase_ : str = key.replace("""backbone""" , """segformer.encoder""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ : List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ : List[Any] = key.replace(f'patch_embed{idx}' , f'patch_embeddings.{int(A__ )-1}' )
if "norm" in key:
lowerCAmelCase_ : Any = key.replace("""norm""" , """layer_norm""" )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ : Tuple = key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
lowerCAmelCase_ : int = key.replace(f'layer_norm{idx}' , f'layer_norm.{int(A__ )-1}' )
if "layer_norm1" in key:
lowerCAmelCase_ : str = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ : Union[str, Any] = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ : Any = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ : str = key.replace(f'block{idx}' , f'block.{int(A__ )-1}' )
if "attn.q" in key:
lowerCAmelCase_ : List[Any] = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ : Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ : str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ : Optional[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ : Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ : List[Any] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ : Optional[Any] = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ : Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ : str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ : Dict = key.replace(f'linear_c{idx}' , f'linear_c.{int(A__ )-1}' )
if key.startswith("""head""" ):
lowerCAmelCase_ : int = key.replace("""head""" , """classifier""" )
lowerCAmelCase_ : int = value
return new_state_dict
def UpperCamelCase_ ( A__ : int , A__ : Union[str, Any] ):
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ : int = state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
lowerCAmelCase_ : Optional[int] = state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ : List[str] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ : Optional[Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ : Union[str, Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ : str = kv_bias[
config.hidden_sizes[i] :
]
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ : Optional[Any] = Image.open(requests.get(A__ , stream=A__ ).raw )
return image
@torch.no_grad()
def UpperCamelCase_ ( A__ : Optional[Any] , A__ : List[Any] , A__ : Tuple ):
'''simple docstring'''
lowerCAmelCase_ : str = SegformerConfig()
lowerCAmelCase_ : Optional[Any] = False
# set attributes based on model_name
lowerCAmelCase_ : int = """huggingface/label-files"""
if "segformer" in model_name:
lowerCAmelCase_ : Optional[int] = model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
if "ade" in model_name:
lowerCAmelCase_ : List[Any] = 1_50
lowerCAmelCase_ : int = """ade20k-id2label.json"""
lowerCAmelCase_ : Tuple = (1, 1_50, 1_28, 1_28)
elif "city" in model_name:
lowerCAmelCase_ : List[str] = 19
lowerCAmelCase_ : Dict = """cityscapes-id2label.json"""
lowerCAmelCase_ : List[str] = (1, 19, 1_28, 1_28)
else:
raise ValueError(f'Model {model_name} not supported' )
elif "mit" in model_name:
lowerCAmelCase_ : Dict = True
lowerCAmelCase_ : Optional[int] = model_name[4:6]
lowerCAmelCase_ : Union[str, Any] = 10_00
lowerCAmelCase_ : int = """imagenet-1k-id2label.json"""
lowerCAmelCase_ : Optional[Any] = (1, 10_00)
else:
raise ValueError(f'Model {model_name} not supported' )
# set config attributes
lowerCAmelCase_ : Optional[Any] = json.load(open(hf_hub_download(A__ , A__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase_ : List[Any] = {int(A__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ : List[str] = idalabel
lowerCAmelCase_ : List[Any] = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
lowerCAmelCase_ : Any = [64, 1_28, 3_20, 5_12]
lowerCAmelCase_ : int = 2_56
elif size == "b2":
lowerCAmelCase_ : Any = [64, 1_28, 3_20, 5_12]
lowerCAmelCase_ : List[str] = 7_68
lowerCAmelCase_ : Any = [3, 4, 6, 3]
elif size == "b3":
lowerCAmelCase_ : List[str] = [64, 1_28, 3_20, 5_12]
lowerCAmelCase_ : Union[str, Any] = 7_68
lowerCAmelCase_ : Union[str, Any] = [3, 4, 18, 3]
elif size == "b4":
lowerCAmelCase_ : Tuple = [64, 1_28, 3_20, 5_12]
lowerCAmelCase_ : Tuple = 7_68
lowerCAmelCase_ : Tuple = [3, 8, 27, 3]
elif size == "b5":
lowerCAmelCase_ : Union[str, Any] = [64, 1_28, 3_20, 5_12]
lowerCAmelCase_ : str = 7_68
lowerCAmelCase_ : Any = [3, 6, 40, 3]
else:
raise ValueError(f'Size {size} not supported' )
# load image processor (only resize + normalize)
lowerCAmelCase_ : List[Any] = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=A__ , align=A__ , do_random_crop=A__ )
# prepare image
lowerCAmelCase_ : Optional[Any] = prepare_img()
lowerCAmelCase_ : Union[str, Any] = image_processor(images=A__ , return_tensors="""pt""" ).pixel_values
logger.info(f'Converting model {model_name}...' )
# load original state dict
if encoder_only:
lowerCAmelCase_ : str = torch.load(A__ , map_location=torch.device("""cpu""" ) )
else:
lowerCAmelCase_ : List[str] = torch.load(A__ , map_location=torch.device("""cpu""" ) )["""state_dict"""]
# rename keys
lowerCAmelCase_ : Dict = rename_keys(A__ , encoder_only=A__ )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(A__ , A__ )
# create HuggingFace model and load state dict
if encoder_only:
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : List[Any] = SegformerForImageClassification(A__ )
else:
lowerCAmelCase_ : str = SegformerForSemanticSegmentation(A__ )
model.load_state_dict(A__ )
model.eval()
# forward pass
lowerCAmelCase_ : Tuple = model(A__ )
lowerCAmelCase_ : Union[str, Any] = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
lowerCAmelCase_ : Tuple = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
[[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
[[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
[[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
[[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
[[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
[[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
lowerCAmelCase_ : List[str] = torch.tensor(
[
[[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
[[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
[[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
lowerCAmelCase_ : List[str] = torch.tensor(
[
[[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
[[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
[[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
lowerCAmelCase_ : Dict = torch.tensor(
[
[[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
[[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
[[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
[[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
[[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
lowerCAmelCase_ : Dict = torch.tensor(
[
[
[-1.13_72E01, -1.27_87E01, -1.34_77E01],
[-1.25_36E01, -1.41_94E01, -1.44_09E01],
[-1.32_17E01, -1.48_88E01, -1.53_27E01],
],
[
[-1.47_91E01, -1.71_22E01, -1.82_77E01],
[-1.71_63E01, -1.91_92E01, -1.95_33E01],
[-1.78_97E01, -1.99_91E01, -2.03_15E01],
],
[
[7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
[4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
[3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
lowerCAmelCase_ : str = torch.tensor(
[
[[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
[[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
[[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
lowerCAmelCase_ : Optional[int] = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
[[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
[[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
lowerCAmelCase_ : int = torch.tensor(
[
[[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
[[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
[[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
lowerCAmelCase_ : Union[str, Any] = torch.tensor(
[
[[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
[[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
[[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
lowerCAmelCase_ : List[Any] = torch.tensor(
[
[[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
[[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
[[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
] )
else:
lowerCAmelCase_ : Optional[Any] = logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , A__ , atol=1E-2 )
# finally, save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(A__ ).mkdir(exist_ok=A__ )
model.save_pretrained(A__ )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="segformer.b0.512x512.ade.160k",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
__A : Tuple = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 89 | 1 |
a : Union[str, Any] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : List[Any] = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class a ( lowercase__ ):
"""simple docstring"""
a : List[Any] = 'xglm'
a : str = ['past_key_values']
a : Any = {
'num_attention_heads': 'attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Optional[int] , __lowercase : int=256008 , __lowercase : Tuple=2048 , __lowercase : List[Any]=1024 , __lowercase : str=4096 , __lowercase : Optional[Any]=24 , __lowercase : Optional[int]=16 , __lowercase : List[Any]="gelu" , __lowercase : str=0.1 , __lowercase : Dict=0.1 , __lowercase : Tuple=0.0 , __lowercase : Optional[int]=0.0 , __lowercase : Dict=0.02 , __lowercase : Optional[int]=True , __lowercase : Any=True , __lowercase : Dict=2 , __lowercase : Optional[Any]=1 , __lowercase : List[Any]=0 , __lowercase : Optional[Any]=2 , **__lowercase : List[str] , ) -> Optional[int]:
__UpperCAmelCase : List[str] = vocab_size
__UpperCAmelCase : Optional[Any] = max_position_embeddings
__UpperCAmelCase : Optional[Any] = d_model
__UpperCAmelCase : str = ffn_dim
__UpperCAmelCase : List[str] = num_layers
__UpperCAmelCase : Dict = attention_heads
__UpperCAmelCase : str = activation_function
__UpperCAmelCase : Optional[Any] = dropout
__UpperCAmelCase : Any = attention_dropout
__UpperCAmelCase : int = activation_dropout
__UpperCAmelCase : Tuple = layerdrop
__UpperCAmelCase : Tuple = init_std
__UpperCAmelCase : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True
__UpperCAmelCase : Union[str, Any] = use_cache
super().__init__(
pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , decoder_start_token_id=__lowercase , **__lowercase , )
| 114 | 1 |
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
lowerCamelCase : Optional[int] =logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] ={'''vocab_file''': '''spiece.model'''}
lowerCamelCase : str ={
'''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''',
}
}
lowerCamelCase : int ={
'''AI-Sweden/gpt-sw3-126m''': 2048,
'''AI-Sweden/gpt-sw3-350m''': 2048,
'''AI-Sweden/gpt-sw3-1.6b''': 2048,
'''AI-Sweden/gpt-sw3-6.7b''': 2048,
'''AI-Sweden/gpt-sw3-20b''': 2048,
}
class __a ( A__ ):
_lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
_lowerCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Dict = ['''input_ids''', '''attention_mask''']
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any=False , SCREAMING_SNAKE_CASE : Any=False , SCREAMING_SNAKE_CASE : Dict=False , SCREAMING_SNAKE_CASE : Union[str, Any]=None , SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **SCREAMING_SNAKE_CASE : List[Any] , ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCamelCase__ : Tuple = 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" )
UpperCamelCase__ : str = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCamelCase__ : Any = "<|endoftext|>" if eos_token is None else eos_token
UpperCamelCase__ : int = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCamelCase__ : Optional[int] = unk_token if pad_token is None else pad_token
UpperCamelCase__ : Any = eos_token if bos_token is None else bos_token
else:
UpperCamelCase__ : List[Any] = "<pad>" if pad_token is None else pad_token
UpperCamelCase__ : Any = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=SCREAMING_SNAKE_CASE , remove_space=SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE , bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE , )
UpperCamelCase__ : int = do_lower_case
UpperCamelCase__ : Any = remove_space
UpperCamelCase__ : Any = keep_accents
UpperCamelCase__ : List[Any] = vocab_file
UpperCamelCase__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE )
# Used for whitespace normalization in input texts
# fmt : off
UpperCamelCase__ : Dict = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCamelCase__ : Any = re.compile(
F'[{"".join(map(SCREAMING_SNAKE_CASE , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(1_27 , 1_60 ) ) + [1_60, 1_73, 82_03] ) )}]' )
def __getstate__( self : int ):
'''simple docstring'''
UpperCamelCase__ : str = self.__dict__.copy()
UpperCamelCase__ : Optional[int] = None
return state
def __setstate__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase__ : List[str] = {}
UpperCamelCase__ : List[str] = 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 __lowercase ( self : str ):
'''simple docstring'''
return len(self.sp_model )
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.non_printing_characters_re.sub("" , SCREAMING_SNAKE_CASE )
# Normalize whitespaces
UpperCamelCase__ : Union[str, Any] = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCamelCase__ : Any = unicodedata.normalize("NFC" , SCREAMING_SNAKE_CASE )
return text
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
UpperCamelCase__ : Any = self.preprocess_text(SCREAMING_SNAKE_CASE )
return self.sp_model.encode(SCREAMING_SNAKE_CASE , out_type=SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE )
@staticmethod
def __lowercase ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return out_string
def __lowercase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : List[Any] = ""
UpperCamelCase__ : Tuple = 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(SCREAMING_SNAKE_CASE ) + token
UpperCamelCase__ : List[str] = True
UpperCamelCase__ : List[str] = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE )
return out_string
def __lowercase ( self : int ):
'''simple docstring'''
UpperCamelCase__ : 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 __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
UpperCamelCase__ : 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:
UpperCamelCase__ : Any = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, List[str]] , SCREAMING_SNAKE_CASE : Union[str, bool] = False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : int = self.preprocess_text(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = self.sp_model.encode(SCREAMING_SNAKE_CASE )
else:
UpperCamelCase__ : List[str] = [self.preprocess_text(SCREAMING_SNAKE_CASE ) for t in text]
UpperCamelCase__ : Tuple = self.sp_model.encode(SCREAMING_SNAKE_CASE )
if return_tensors is True or return_tensors == "pt":
UpperCamelCase__ : Any = torch.tensor(SCREAMING_SNAKE_CASE )
return token_ids
def __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : Union[int, List[int]] ):
'''simple docstring'''
return self.sp_model.decode(SCREAMING_SNAKE_CASE )
def __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : "Conversation" ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
UpperCamelCase__ : List[str] = (
F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(SCREAMING_SNAKE_CASE ) + F'{self.bos_token}Bot:'
)
return self.encode(text=SCREAMING_SNAKE_CASE )
| 360 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : List[Any] =logging.get_logger(__name__)
lowerCamelCase : Optional[Any] ={
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class __a ( A__ , A__ ):
_lowerCAmelCase : Union[str, Any] = '''bit'''
_lowerCAmelCase : List[str] = ['''preactivation''', '''bottleneck''']
_lowerCAmelCase : Any = ['''SAME''', '''VALID''']
def __init__( self : str , SCREAMING_SNAKE_CASE : Tuple=3 , SCREAMING_SNAKE_CASE : str=64 , SCREAMING_SNAKE_CASE : List[Any]=[2_56, 5_12, 10_24, 20_48] , SCREAMING_SNAKE_CASE : Union[str, Any]=[3, 4, 6, 3] , SCREAMING_SNAKE_CASE : str="preactivation" , SCREAMING_SNAKE_CASE : Any="relu" , SCREAMING_SNAKE_CASE : Dict=None , SCREAMING_SNAKE_CASE : List[Any]=32 , SCREAMING_SNAKE_CASE : Tuple=0.0 , SCREAMING_SNAKE_CASE : Dict=False , SCREAMING_SNAKE_CASE : int=32 , SCREAMING_SNAKE_CASE : str=1 , SCREAMING_SNAKE_CASE : List[str]=None , SCREAMING_SNAKE_CASE : Optional[int]=None , **SCREAMING_SNAKE_CASE : int , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
if layer_type not in self.layer_types:
raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
UpperCamelCase__ : Any = global_padding.upper()
else:
raise ValueError(F'Padding strategy {global_padding} not supported' )
UpperCamelCase__ : Dict = num_channels
UpperCamelCase__ : Dict = embedding_size
UpperCamelCase__ : Tuple = hidden_sizes
UpperCamelCase__ : Any = depths
UpperCamelCase__ : Optional[int] = layer_type
UpperCamelCase__ : int = hidden_act
UpperCamelCase__ : str = global_padding
UpperCamelCase__ : Any = num_groups
UpperCamelCase__ : str = drop_path_rate
UpperCamelCase__ : Optional[Any] = embedding_dynamic_padding
UpperCamelCase__ : Tuple = output_stride
UpperCamelCase__ : List[str] = width_factor
UpperCamelCase__ : Any = ["stem"] + [F'stage{idx}' for idx in range(1 , len(SCREAMING_SNAKE_CASE ) + 1 )]
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE , out_indices=SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
| 196 | 0 |
'''simple docstring'''
import operator as op
lowerCamelCase_ = '''scaler.pt'''
lowerCamelCase_ = '''pytorch_model'''
lowerCamelCase_ = '''random_states'''
lowerCamelCase_ = '''optimizer'''
lowerCamelCase_ = '''scheduler'''
lowerCamelCase_ = '''pytorch_model.bin'''
lowerCamelCase_ = '''pytorch_model.bin.index.json'''
lowerCamelCase_ = '''model.safetensors'''
lowerCamelCase_ = '''model.safetensors.index.json'''
lowerCamelCase_ = '''1.10.2'''
lowerCamelCase_ = '''py38'''
lowerCamelCase_ = '''4.17.0'''
lowerCamelCase_ = ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
lowerCamelCase_ = ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
lowerCamelCase_ = ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
lowerCamelCase_ = ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
lowerCamelCase_ = ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
lowerCamelCase_ = '''2.0.1'''
lowerCamelCase_ = ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
lowerCamelCase_ = ['''default''', '''reduce-overhead''', '''max-autotune''']
lowerCamelCase_ = {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCamelCase_ = [
'''nnodes''',
'''nproc_per_node''',
'''rdzv_backend''',
'''rdzv_endpoint''',
'''rdzv_id''',
'''rdzv_conf''',
'''standalone''',
'''max_restarts''',
'''monitor_interval''',
'''start_method''',
'''role''',
'''module''',
'''m''',
'''no_python''',
'''run_path''',
'''log_dir''',
'''r''',
'''redirects''',
'''t''',
'''tee''',
'''node_rank''',
'''master_addr''',
'''master_port''',
]
lowerCamelCase_ = ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
lowerCamelCase_ = ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
| 79 |
def A_ ( snake_case : list ) -> list:
'''simple docstring'''
__UpperCamelCase = len(snake_case )
for i in range(1 , snake_case ):
__UpperCamelCase = collection[i]
__UpperCamelCase = 0
__UpperCamelCase = i - 1
while low <= high:
__UpperCamelCase = (low + high) // 2
if val < collection[mid]:
__UpperCamelCase = mid - 1
else:
__UpperCamelCase = mid + 1
for j in range(snake_case , snake_case , -1 ):
__UpperCamelCase = collection[j - 1]
__UpperCamelCase = val
return collection
if __name__ == "__main__":
lowercase__ : List[Any] = input("Enter numbers separated by a comma:\n").strip()
lowercase__ : str = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 328 | 0 |
'''simple docstring'''
from itertools import product
def a_ ( __snake_case : int , __snake_case : int ) -> list[int]:
"""simple docstring"""
lowerCamelCase_ =sides_number
lowerCamelCase_ =max_face_number * dice_number
lowerCamelCase_ =[0] * (max_total + 1)
lowerCamelCase_ =1
lowerCamelCase_ =range(lowerCAmelCase__ , max_face_number + 1 )
for dice_numbers in product(lowerCAmelCase__ , repeat=lowerCAmelCase__ ):
lowerCamelCase_ =sum(lowerCAmelCase__ )
totals_frequencies[total] += 1
return totals_frequencies
def a_ ( ) -> float:
"""simple docstring"""
lowerCamelCase_ =total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCamelCase_ =total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCamelCase_ =0
lowerCamelCase_ =9
lowerCamelCase_ =4 * 9
lowerCamelCase_ =6
for peter_total in range(lowerCAmelCase__ , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCamelCase_ =(4**9) * (6**6)
lowerCamelCase_ =peter_wins_count / total_games_number
lowerCamelCase_ =round(lowerCAmelCase__ , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 360 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ : Any = logging.get_logger(__name__)
a_ : Optional[int] = """https://openaipublic.azureedge.net/jukebox/models/"""
a_ : Any = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def a_ ( __snake_case : int ) -> Any:
"""simple docstring"""
if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
lowerCamelCase_ =key.replace('''.model.1.bias''' , '''.conv1d_1.bias''' )
elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
lowerCamelCase_ =key.replace('''.model.1.weight''' , '''.conv1d_1.weight''' )
elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
lowerCamelCase_ =key.replace('''.model.3.bias''' , '''.conv1d_2.bias''' )
elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
lowerCamelCase_ =key.replace('''.model.3.weight''' , '''.conv1d_2.weight''' )
if "conditioner_blocks.0." in key:
lowerCamelCase_ =key.replace('''conditioner_blocks.0''' , '''conditioner_blocks''' )
if "prime_prior" in key:
lowerCamelCase_ =key.replace('''prime_prior''' , '''encoder''' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
lowerCamelCase_ =key.replace('''.emb.''' , '''.''' )
if key.endswith('''k''' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('''.k''' , '''.codebook''' )
if "y_emb." in key:
return key.replace('''y_emb.''' , '''metadata_embedding.''' )
if "x_emb.emb." in key:
lowerCamelCase_ =key.replace('''0.x_emb.emb''' , '''embed_tokens''' )
if "prime_state_ln" in key:
return key.replace('''prime_state_ln''' , '''encoder.final_layer_norm''' )
if ".ln" in key:
return key.replace('''.ln''' , '''.layer_norm''' )
if "_ln" in key:
return key.replace('''_ln''' , '''_layer_norm''' )
if "prime_state_proj" in key:
return key.replace('''prime_state_proj''' , '''encoder.proj_in''' )
if "prime_x_out" in key:
return key.replace('''prime_x_out''' , '''encoder.lm_head''' )
if "prior.x_out" in key:
return key.replace('''x_out''' , '''fc_proj_out''' )
if "x_emb" in key:
return key.replace('''x_emb''' , '''embed_tokens''' )
return key
def a_ ( __snake_case : Dict , __snake_case : int , __snake_case : Dict , __snake_case : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ ={}
import re
lowerCamelCase_ =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowerCamelCase_ =re.compile(
r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCamelCase_ =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowerCamelCase_ =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowerCamelCase_ =re.compile(
r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCamelCase_ =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowerCamelCase_ =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
lowerCamelCase_ =re.compile(
r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCamelCase_ =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(__snake_case ):
lowerCamelCase_ =re_encoder_block_conv_in.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[2] ) * 2 + int(groups[3] )
lowerCamelCase_ =F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
lowerCamelCase_ =re_encoder_block_conv_in.sub(__snake_case , __snake_case )
elif re_encoder_block_resnet.fullmatch(__snake_case ):
lowerCamelCase_ =re_encoder_block_resnet.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[2] ) * 2 + int(groups[3] )
lowerCamelCase_ ={'''1''': 1, '''3''': 2}[groups[-2]]
lowerCamelCase_ =F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
lowerCamelCase_ =F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCamelCase_ =prefix + resnet_block
lowerCamelCase_ =re_encoder_block_resnet.sub(__snake_case , __snake_case )
elif re_encoder_block_proj_out.fullmatch(__snake_case ):
lowerCamelCase_ =re_encoder_block_proj_out.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
lowerCamelCase_ =re_encoder_block_proj_out.sub(__snake_case , __snake_case )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(__snake_case ):
lowerCamelCase_ =re_decoder_block_conv_out.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCamelCase_ =F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
lowerCamelCase_ =re_decoder_block_conv_out.sub(__snake_case , __snake_case )
elif re_decoder_block_resnet.fullmatch(__snake_case ):
lowerCamelCase_ =re_decoder_block_resnet.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCamelCase_ ={'''1''': 1, '''3''': 2}[groups[-2]]
lowerCamelCase_ =F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
lowerCamelCase_ =F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCamelCase_ =prefix + resnet_block
lowerCamelCase_ =re_decoder_block_resnet.sub(__snake_case , __snake_case )
elif re_decoder_block_proj_in.fullmatch(__snake_case ):
lowerCamelCase_ =re_decoder_block_proj_in.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
lowerCamelCase_ =re_decoder_block_proj_in.sub(__snake_case , __snake_case )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(__snake_case ):
lowerCamelCase_ =re_prior_cond_conv_out.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCamelCase_ =F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
lowerCamelCase_ =re_prior_cond_conv_out.sub(__snake_case , __snake_case )
elif re_prior_cond_resnet.fullmatch(__snake_case ):
lowerCamelCase_ =re_prior_cond_resnet.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCamelCase_ ={'''1''': 1, '''3''': 2}[groups[-2]]
lowerCamelCase_ =F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
lowerCamelCase_ =F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCamelCase_ =prefix + resnet_block
lowerCamelCase_ =re_prior_cond_resnet.sub(__snake_case , __snake_case )
elif re_prior_cond_proj_in.fullmatch(__snake_case ):
lowerCamelCase_ =re_prior_cond_proj_in.match(__snake_case )
lowerCamelCase_ =regex_match.groups()
lowerCamelCase_ =F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
lowerCamelCase_ =re_prior_cond_proj_in.sub(__snake_case , __snake_case )
# keep original key
else:
lowerCamelCase_ =original_key
lowerCamelCase_ =replace_key(__snake_case )
if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(F'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
lowerCamelCase_ =model_state_dict[F'''{key_prefix}.{key}''']
print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
lowerCamelCase_ =original_key
lowerCamelCase_ =original_key
lowerCamelCase_ =value
return new_dict
@torch.no_grad()
def a_ ( __snake_case : List[str]=None , __snake_case : Tuple=None ) -> Union[str, Any]:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/' )[-1]}''' ):
lowerCamelCase_ =requests.get(F'''{PREFIX}{file}''' , allow_redirects=__snake_case )
os.makedirs(F'''{pytorch_dump_folder_path}/''' , exist_ok=__snake_case )
open(F'''{pytorch_dump_folder_path}/{file.split('/' )[-1]}''' , '''wb''' ).write(r.content )
lowerCamelCase_ =MODEL_MAPPING[model_name.split('''/''' )[-1]]
lowerCamelCase_ =JukeboxConfig.from_pretrained(__snake_case )
lowerCamelCase_ =JukeboxModel(__snake_case )
lowerCamelCase_ =[]
lowerCamelCase_ ={}
for i, dict_name in enumerate(__snake_case ):
lowerCamelCase_ =torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}''' )['''model''']
lowerCamelCase_ ={}
for k in old_dic.keys():
if k.endswith('''.b''' ):
lowerCamelCase_ =old_dic[k]
elif k.endswith('''.w''' ):
lowerCamelCase_ =old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
lowerCamelCase_ =old_dic[k]
else:
lowerCamelCase_ =old_dic[k]
lowerCamelCase_ ='''vqvae''' if i == 0 else F'''priors.{3 - i}'''
lowerCamelCase_ =fix_jukebox_keys(__snake_case , model.state_dict() , __snake_case , __snake_case )
weight_dict.append(__snake_case )
lowerCamelCase_ =weight_dict.pop(0 )
model.vqvae.load_state_dict(__snake_case )
for i in range(len(__snake_case ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
with open(F'''{pytorch_dump_folder_path}/mapping.json''' , '''w''' ) as txtfile:
json.dump(__snake_case , __snake_case )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__snake_case )
return weight_dict
if __name__ == "__main__":
a_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
a_ : Optional[int] = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 6 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : int = filter(lambda UpperCamelCase__ : p.requires_grad , model.parameters() )
_a : Dict = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_snake_case = logging.getLogger(__name__)
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if metric == "rouge2":
_a : Tuple = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
_a : List[str] = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
_a : str = '''{val_avg_em:.4f}-{step_count}'''
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
""" function.""" )
_a : List[str] = ModelCheckpoint(
dirpath=UpperCamelCase__ , filename=UpperCamelCase__ , monitor=F"""val_{metric}""" , mode="""max""" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="""min""" if """loss""" in metric else """max""" , patience=UpperCamelCase__ , verbose=UpperCamelCase__ , )
class UpperCamelCase ( pl.Callback ):
def _lowercase ( self : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Dict ) -> List[Any]:
_a : Union[str, Any] = {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCAmelCase_ )
@rank_zero_only
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : pl.Trainer , UpperCAmelCase__ : pl.LightningModule , UpperCAmelCase__ : str , UpperCAmelCase__ : Any=True ) -> List[str]:
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
_a : Tuple = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
_a : Tuple = Path(pl_module.hparams.output_dir )
if type_path == "test":
_a : List[str] = od / '''test_results.txt'''
_a : Optional[int] = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_a : Any = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
_a : Dict = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
generations_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
with open(lowerCAmelCase_ , """a+""" ) as writer:
for key in sorted(lowerCAmelCase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
_a : Optional[int] = metrics[key]
if isinstance(lowerCAmelCase_ , torch.Tensor ):
_a : List[str] = val.item()
_a : List[Any] = f"""{key}: {val:.6f}\n"""
writer.write(lowerCAmelCase_ )
if not save_generations:
return
if "preds" in metrics:
_a : Optional[int] = '''\n'''.join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(lowerCAmelCase_ )
@rank_zero_only
def _lowercase ( self : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] ) -> Dict:
try:
_a : Union[str, Any] = pl_module.model.model.num_parameters()
except AttributeError:
_a : Any = pl_module.model.num_parameters()
_a : Optional[int] = count_trainable_parameters(lowerCAmelCase_ )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} )
@rank_zero_only
def _lowercase ( self : List[str] , UpperCAmelCase__ : pl.Trainer , UpperCAmelCase__ : pl.LightningModule ) -> Dict:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCAmelCase_ , lowerCAmelCase_ , """test""" )
@rank_zero_only
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : pl.Trainer , UpperCAmelCase__ : Optional[Any] ) -> int:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 294 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Any = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : List[Any] = '''informer'''
__UpperCamelCase : List[str] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : str = "student_t" , lowerCAmelCase_ : str = "nll" , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : List[int] = None , lowerCAmelCase_ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : Optional[List[int]] = None , lowerCAmelCase_ : int = 6_4 , lowerCAmelCase_ : int = 3_2 , lowerCAmelCase_ : int = 3_2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : int = 2 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : str = "gelu" , lowerCAmelCase_ : float = 0.05 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : float = 0.1 , lowerCAmelCase_ : int = 1_0_0 , lowerCAmelCase_ : float = 0.02 , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : str = "prob" , lowerCAmelCase_ : int = 5 , lowerCAmelCase_ : bool = True , **lowerCAmelCase_ : str , ):
"""simple docstring"""
# time series specific configuration
_A: Optional[Any] = prediction_length
_A: Optional[Any] = context_length or prediction_length
_A: Dict = distribution_output
_A: List[str] = loss
_A: int = input_size
_A: List[str] = num_time_features
_A: Optional[Any] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
_A: str = scaling
_A: Optional[Any] = num_dynamic_real_features
_A: List[Any] = num_static_real_features
_A: Tuple = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
_A: str = cardinality
else:
_A: Union[str, Any] = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
_A: List[str] = embedding_dimension
else:
_A: Union[str, Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_A: int = num_parallel_samples
# Transformer architecture configuration
_A: Union[str, Any] = input_size * len(self.lags_sequence ) + self._number_of_features
_A: Union[str, Any] = d_model
_A: Optional[Any] = encoder_attention_heads
_A: Optional[Any] = decoder_attention_heads
_A: Optional[Any] = encoder_ffn_dim
_A: Union[str, Any] = decoder_ffn_dim
_A: Any = encoder_layers
_A: str = decoder_layers
_A: List[str] = dropout
_A: Any = attention_dropout
_A: Optional[int] = activation_dropout
_A: List[Any] = encoder_layerdrop
_A: str = decoder_layerdrop
_A: int = activation_function
_A: Tuple = init_std
_A: Union[str, Any] = use_cache
# Informer
_A: Union[str, Any] = attention_type
_A: str = sampling_factor
_A: List[str] = distil
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 121 | 0 |
'''simple docstring'''
UpperCamelCase : Any = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCamelCase : Tuple = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCamelCase : Optional[int] = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 367 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class UpperCamelCase :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase_ : Tuple):
"""simple docstring"""
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
a : Dict = deepcopy(UpperCAmelCase_)
elif os.path.exists(UpperCAmelCase_):
with io.open(UpperCAmelCase_ , 'r' , encoding='utf-8') as f:
a : Union[str, Any] = json.load(UpperCAmelCase_)
else:
try:
a : Union[str, Any] = baseaa.urlsafe_baadecode(UpperCAmelCase_).decode('utf-8')
a : List[str] = json.loads(UpperCAmelCase_)
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""")
a : Optional[int] = config
self.set_stage_and_offload()
def SCREAMING_SNAKE_CASE_ ( self : List[Any]):
"""simple docstring"""
a : str = self.get_value('zero_optimization.stage' , -1)
# offload
a : Any = False
if self.is_zeroa() or self.is_zeroa():
a : Tuple = set(['cpu', 'nvme'])
a : int = set(
[
self.get_value('zero_optimization.offload_optimizer.device'),
self.get_value('zero_optimization.offload_param.device'),
])
if len(offload_devices & offload_devices_valid) > 0:
a : List[str] = True
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCAmelCase_ : Dict):
"""simple docstring"""
a : List[str] = self.config
# find the config node of interest if it exists
a : int = ds_key_long.split('.')
a : Union[str, Any] = nodes.pop()
for node in nodes:
a : Union[str, Any] = config.get(UpperCAmelCase_)
if config is None:
return None, ds_key
return config, ds_key
def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int=None):
"""simple docstring"""
a , a : int = self.find_config_node(UpperCAmelCase_)
if config is None:
return default
return config.get(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any=False):
"""simple docstring"""
a : Any = self.config
# find the config node of interest if it exists
a : Optional[Any] = ds_key_long.split('.')
for node in nodes:
a : List[str] = config
a : int = config.get(UpperCAmelCase_)
if config is None:
if must_exist:
raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""")
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCAmelCase_ : str):
"""simple docstring"""
a : List[str] = self.get_value(UpperCAmelCase_)
return False if value is None else bool(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCAmelCase_ : Union[str, Any]):
"""simple docstring"""
a : List[Any] = self.get_value(UpperCAmelCase_)
return False if value is None else not bool(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
return self._stage == 2
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
return self._stage == 3
def SCREAMING_SNAKE_CASE_ ( self : Dict):
"""simple docstring"""
return self._offload
class UpperCamelCase :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase_ : int):
"""simple docstring"""
a : Union[str, Any] = engine
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any]):
"""simple docstring"""
self.engine.backward(UpperCAmelCase_ , **UpperCAmelCase_)
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class UpperCamelCase ( a_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any):
"""simple docstring"""
super().__init__(UpperCAmelCase_ , device_placement=UpperCAmelCase_ , scaler=UpperCAmelCase_)
a : List[str] = hasattr(self.optimizer , 'overflow')
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCAmelCase_ : Dict=None):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def SCREAMING_SNAKE_CASE_ ( self : Tuple):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict):
"""simple docstring"""
if self.__has_overflow__:
return self.optimizer.overflow
return False
class UpperCamelCase ( a_ ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any]):
"""simple docstring"""
super().__init__(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int]=0.0_01 , UpperCAmelCase_ : List[Any]=0 , **UpperCAmelCase_ : Union[str, Any]):
"""simple docstring"""
a : int = params
a : str = lr
a : Tuple = weight_decay
a : Dict = kwargs
class UpperCamelCase :
"""simple docstring"""
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Union[str, Any]=0 , **UpperCAmelCase_ : List[Any]):
"""simple docstring"""
a : str = optimizer
a : Tuple = total_num_steps
a : Optional[Any] = warmup_num_steps
a : List[str] = kwargs
| 345 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"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
a : int = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 114 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCamelCase ( lowerCAmelCase ):
def __init__( self :int , lowerCamelCase :AutoencoderKL , lowerCamelCase :CLIPTextModel , lowerCamelCase :CLIPTokenizer , lowerCamelCase :UNetaDConditionModel , lowerCamelCase :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowerCamelCase :StableDiffusionSafetyChecker , lowerCamelCase :CLIPImageProcessor , ) -> Optional[int]:
super().__init__()
self.register_modules(
vae=lowerCamelCase , text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , safety_checker=lowerCamelCase , feature_extractor=lowerCamelCase , )
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :Optional[Union[str, int]] = "auto" ) -> int:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[int] ) -> Union[str, Any]:
self.enable_attention_slicing(lowerCamelCase )
@torch.no_grad()
def __call__( self :int , lowerCamelCase :Union[str, List[str]] , lowerCamelCase :int = 512 , lowerCamelCase :int = 512 , lowerCamelCase :int = 50 , lowerCamelCase :float = 7.5 , lowerCamelCase :Optional[Union[str, List[str]]] = None , lowerCamelCase :Optional[int] = 1 , lowerCamelCase :float = 0.0 , lowerCamelCase :Optional[torch.Generator] = None , lowerCamelCase :Optional[torch.FloatTensor] = None , lowerCamelCase :Optional[str] = "pil" , lowerCamelCase :bool = True , lowerCamelCase :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase :int = 1 , lowerCamelCase :Optional[torch.FloatTensor] = None , **lowerCamelCase :List[str] , ) -> str:
if isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = 1
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCamelCase , lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(lowerCamelCase )}.''' )
# get prompt text embeddings
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
UpperCAmelCase__ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase__ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase__ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
UpperCAmelCase__ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = text_embeddings.shape
UpperCAmelCase__ = text_embeddings.repeat(1 , lowerCamelCase , 1 )
UpperCAmelCase__ = text_embeddings.view(bs_embed * num_images_per_prompt , lowerCamelCase , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ = 42
if negative_prompt is None:
UpperCAmelCase__ = [""]
elif type(lowerCamelCase ) is not type(lowerCamelCase ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase )} !='''
f''' {type(lowerCamelCase )}.''' )
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = [negative_prompt]
elif batch_size != len(lowerCamelCase ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
UpperCAmelCase__ = negative_prompt
UpperCAmelCase__ = text_input_ids.shape[-1]
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , truncation=lowerCamelCase , return_tensors="pt" , )
UpperCAmelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ = uncond_embeddings.shape[1]
UpperCAmelCase__ = uncond_embeddings.repeat(lowerCamelCase , lowerCamelCase , 1 )
UpperCAmelCase__ = uncond_embeddings.view(batch_size * num_images_per_prompt , lowerCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
UpperCAmelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(self.device )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(
self.device )
else:
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
UpperCAmelCase__ = latents_reference.to(self.device )
UpperCAmelCase__ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
UpperCAmelCase__ = (latents_shape[3] - latents_shape_reference[3]) // 2
UpperCAmelCase__ = (latents_shape[2] - latents_shape_reference[2]) // 2
UpperCAmelCase__ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
UpperCAmelCase__ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
UpperCAmelCase__ = 0 if dx < 0 else dx
UpperCAmelCase__ = 0 if dy < 0 else dy
UpperCAmelCase__ = max(-dx , 0 )
UpperCAmelCase__ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
UpperCAmelCase__ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowerCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
UpperCAmelCase__ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase__ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase__ = {}
if accepts_eta:
UpperCAmelCase__ = eta
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ = self.scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
# predict the noise residual
UpperCAmelCase__ = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).sample
# perform guidance
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.chunk(2 )
UpperCAmelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = 1 / 0.1_82_15 * latents
UpperCAmelCase__ = self.vae.decode(lowerCamelCase ).sample
UpperCAmelCase__ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
UpperCAmelCase__ = self.feature_extractor(self.numpy_to_pil(lowerCamelCase ) , return_tensors="pt" ).to(
self.device )
UpperCAmelCase__ , UpperCAmelCase__ = self.safety_checker(
images=lowerCamelCase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
UpperCAmelCase__ = None
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowerCamelCase , nsfw_content_detected=lowerCamelCase )
| 169 | 0 |
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : list[float] ) -> float:
'''simple docstring'''
lowerCAmelCase_ : Tuple = 0.00
lowerCAmelCase_ : List[Any] = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase_ : str = f'Resistor at index {index} has a negative or zero value!'
raise ValueError(lowercase__ )
first_sum += 1 / float(lowercase__ )
index += 1
return 1 / first_sum
def __UpperCamelCase ( lowercase__ : list[float] ) -> float:
'''simple docstring'''
lowerCAmelCase_ : int = 0.00
lowerCAmelCase_ : Dict = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase_ : Tuple = f'Resistor at index {index} has a negative value!'
raise ValueError(lowercase__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
from math import factorial, pi
def __UpperCamelCase ( lowercase__ : float , lowercase__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowercase__ , (int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )
if not isinstance(lowercase__ , lowercase__ ) or accuracy <= 0:
raise ValueError("""maclaurin_sin() requires a positive int for accuracy""" )
lowerCAmelCase_ : Optional[int] = float(lowercase__ )
lowerCAmelCase_ : Union[str, Any] = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(lowercase__ ) )
def __UpperCamelCase ( lowercase__ : float , lowercase__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowercase__ , (int, float) ):
raise ValueError("""maclaurin_cos() requires either an int or float for theta""" )
if not isinstance(lowercase__ , lowercase__ ) or accuracy <= 0:
raise ValueError("""maclaurin_cos() requires a positive int for accuracy""" )
lowerCAmelCase_ : int = float(lowercase__ )
lowerCAmelCase_ : Optional[int] = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(lowercase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 28 | 0 |
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")
__A = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
snake_case_ = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
snake_case_ = field(
default=10_24 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
snake_case_ = field(
default=__a , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
snake_case_ = field(
default=__a , 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.'''
)
} , )
snake_case_ = field(
default=__a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
snake_case_ = field(
default=__a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
snake_case_ = field(
default=__a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
snake_case_ = field(
default=__a , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
snake_case_ = field(
default=__a , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
snake_case_ = field(default=__a , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def lowercase_ ( self ) -> Dict:
'''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:
__lowerCamelCase = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCamelCase = 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 __lowerCAmelCase :
"""simple docstring"""
snake_case_ = field(
default=__a , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
snake_case_ = field(
default=__a , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
snake_case_ = field(
default=__a , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
snake_case_ = field(
default=__a , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
snake_case_ = field(
default=__a , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
snake_case_ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
snake_case_ = field(
default=__a , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
__lowerCamelCase = 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.
__lowerCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCamelCase = 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 )] , )
__lowerCamelCase = training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase_ )
datasets.utils.logging.set_verbosity(lowerCAmelCase_ )
transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
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.
__lowerCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCamelCase = 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.
__lowerCamelCase = 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.
__lowerCamelCase = {"""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:
__lowerCamelCase = data_args.train_file.split('.' )[-1]
__lowerCamelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCamelCase = 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
__lowerCamelCase = load_dataset('csv' , data_files=lowerCAmelCase_ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCamelCase = load_dataset('json' , data_files=lowerCAmelCase_ , 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
__lowerCamelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCamelCase = len(lowerCAmelCase_ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCAmelCase_ , 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
__lowerCamelCase = 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=lowerCAmelCase_ , )
__lowerCamelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowerCAmelCase_ , 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:
__lowerCamelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCamelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCamelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCamelCase = {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}.""" )
__lowerCamelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(UpperCamelCase__ : int ):
# Tokenize the texts
def _convert_table_text_to_pandas(UpperCamelCase__ : List[Any] ):
__lowerCamelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
__lowerCamelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCamelCase = examples["""statement"""]
__lowerCamelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
__lowerCamelCase = tokenizer(lowerCAmelCase_ , lowerCAmelCase_ , padding=lowerCAmelCase_ , max_length=lowerCAmelCase_ , truncation=lowerCAmelCase_ )
__lowerCamelCase = examples["""label"""]
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
__lowerCamelCase = raw_datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , 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' )
__lowerCamelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCamelCase = 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' )
__lowerCamelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCamelCase = 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' )
__lowerCamelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCamelCase = 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(lowerCAmelCase_ ) ) , 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(UpperCamelCase__ : EvalPrediction ):
__lowerCamelCase = p.predictions[0] if isinstance(p.predictions , lowerCAmelCase_ ) else p.predictions
__lowerCamelCase = np.argmax(lowerCAmelCase_ , 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:
__lowerCamelCase = default_data_collator
elif training_args.fpaa:
__lowerCamelCase = DataCollatorWithPadding(lowerCAmelCase_ , pad_to_multiple_of=8 )
else:
__lowerCamelCase = None
# Initialize our Trainer
__lowerCamelCase = Trainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , data_collator=lowerCAmelCase_ , )
# Training
if training_args.do_train:
__lowerCamelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCamelCase = last_checkpoint
__lowerCamelCase = trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
__lowerCamelCase = train_result.metrics
__lowerCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase_ )
)
__lowerCamelCase = min(lowerCAmelCase_ , len(lowerCAmelCase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , lowerCAmelCase_ )
trainer.save_metrics('train' , lowerCAmelCase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__lowerCamelCase = trainer.evaluate(eval_dataset=lowerCAmelCase_ )
__lowerCamelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase_ )
__lowerCamelCase = min(lowerCAmelCase_ , len(lowerCAmelCase_ ) )
trainer.log_metrics('eval' , lowerCAmelCase_ )
trainer.save_metrics('eval' , lowerCAmelCase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCamelCase = predict_dataset.remove_columns('label' )
__lowerCamelCase = trainer.predict(lowerCAmelCase_ , metric_key_prefix='predict' ).predictions
__lowerCamelCase = np.argmax(lowerCAmelCase_ , axis=1 )
__lowerCamelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(lowerCAmelCase_ , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(lowerCAmelCase_ ):
__lowerCamelCase = label_list[item]
writer.write(F"""{index}\t{item}\n""" )
__lowerCamelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase_ )
else:
trainer.create_model_card(**lowerCAmelCase_ )
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> Any:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 90 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def snake_case_ ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Tuple=None , ):
if attention_mask is None:
__lowercase : Tuple = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__lowercase : Any = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__lowercase : Any = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=lowerCAmelCase_ )
if decoder_head_mask is None:
__lowercase : List[Any] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCAmelCase_ )
if cross_attn_head_mask is None:
__lowercase : List[Any] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowerCAmelCase_ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , __a : str , __a : Tuple=13 , __a : List[Any]=7 , __a : Any=True , __a : List[str]=False , __a : Optional[Any]=99 , __a : Tuple=16 , __a : int=2 , __a : Optional[Any]=4 , __a : int=4 , __a : Any="relu" , __a : Optional[int]=0.1 , __a : List[str]=0.1 , __a : Dict=0.0 , __a : List[str]=0.0 , __a : Union[str, Any]=20 , __a : str=2 , __a : str=1 , __a : Optional[int]=0 , ) -> Optional[int]:
"""simple docstring"""
__lowercase : Any = parent
__lowercase : Tuple = batch_size
__lowercase : Any = seq_length
__lowercase : Tuple = is_training
__lowercase : Optional[Any] = use_labels
__lowercase : Dict = vocab_size
__lowercase : Optional[Any] = hidden_size
__lowercase : Dict = num_hidden_layers
__lowercase : Dict = num_attention_heads
__lowercase : Dict = intermediate_size
__lowercase : Union[str, Any] = hidden_act
__lowercase : Union[str, Any] = hidden_dropout_prob
__lowercase : Tuple = attention_probs_dropout_prob
__lowercase : Tuple = encoder_layerdrop
__lowercase : List[str] = decoder_layerdrop
__lowercase : Any = max_position_embeddings
__lowercase : Any = eos_token_id
__lowercase : Dict = pad_token_id
__lowercase : List[str] = bos_token_id
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase : str = self.eos_token_id # Eos Token
__lowercase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__lowercase : Tuple = input_ids.clamp(self.pad_token_id + 1 )
__lowercase : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
__lowercase : List[str] = self.get_config()
__lowercase : str = prepare_mam_aaa_inputs_dict(__a , __a , __a )
return config, inputs_dict
def lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCAmelCase ( self : Dict ) -> str:
"""simple docstring"""
__lowercase , __lowercase : int = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase ( self : int , __a : str , __a : str ) -> int:
"""simple docstring"""
__lowercase : Optional[Any] = MaMaaaModel(config=__a ).get_decoder().to(__a ).eval()
__lowercase : List[str] = inputs_dict["""input_ids"""]
__lowercase : Dict = inputs_dict["""attention_mask"""]
__lowercase : List[Any] = inputs_dict["""head_mask"""]
# first forward pass
__lowercase : List[str] = model(__a , attention_mask=__a , head_mask=__a , use_cache=__a )
__lowercase , __lowercase : str = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
__lowercase : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase : str = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
__lowercase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowercase : Optional[Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
__lowercase : Optional[int] = model(__a , attention_mask=__a )["""last_hidden_state"""]
__lowercase : Union[str, Any] = model(__a , attention_mask=__a , past_key_values=__a )[
"""last_hidden_state"""
]
# select random slice
__lowercase : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowercase : int = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowercase : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__a , __a , atol=1E-2 ) )
def lowerCAmelCase ( self : List[str] , __a : Tuple , __a : List[str] ) -> str:
"""simple docstring"""
__lowercase : Dict = MaMaaaModel(config=__a ).to(__a ).eval()
__lowercase : Any = model(**__a )
__lowercase : str = outputs.encoder_last_hidden_state
__lowercase : Optional[Any] = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase : str = model.get_encoder()
encoder.save_pretrained(__a )
__lowercase : List[Any] = MaMaaaEncoder.from_pretrained(__a ).to(__a )
__lowercase : Tuple = encoder(inputs_dict["""input_ids"""] , attention_mask=inputs_dict["""attention_mask"""] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase : Tuple = model.get_decoder()
decoder.save_pretrained(__a )
__lowercase : Tuple = MaMaaaDecoder.from_pretrained(__a ).to(__a )
__lowercase : Tuple = decoder(
input_ids=inputs_dict["""decoder_input_ids"""] , attention_mask=inputs_dict["""decoder_attention_mask"""] , encoder_hidden_states=__a , encoder_attention_mask=inputs_dict["""attention_mask"""] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class lowerCAmelCase ( __a , __a , __a , unittest.TestCase ):
'''simple docstring'''
_A : int = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_A : int = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_A : Union[str, Any] = (
{
'''conversational''': MaMaaaForConditionalGeneration,
'''feature-extraction''': MaMaaaModel,
'''summarization''': MaMaaaForConditionalGeneration,
'''text2text-generation''': MaMaaaForConditionalGeneration,
'''translation''': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_A : Optional[int] = True
_A : Union[str, Any] = True
_A : Any = False
_A : int = False
def lowerCAmelCase ( self : str , __a : Union[str, Any] , __a : Optional[int] , __a : List[Any] , __a : Tuple , __a : Optional[int] ) -> Any:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowercase : int = MaMaaaModelTester(self )
__lowercase : Union[str, Any] = ConfigTester(self , config_class=__a )
def lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
__lowercase , __lowercase : Any = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__lowercase : List[str] = model_class(__a )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a )
__lowercase , __lowercase : str = model_class.from_pretrained(__a , output_loading_info=__a )
self.assertEqual(info["""missing_keys"""] , [] )
def lowerCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a )
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__a )
def lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
__lowercase , __lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
__lowercase : Union[str, Any] = model_class(__a )
model.to(__a )
model.eval()
__lowercase : Any = copy.deepcopy(self._prepare_for_class(__a , __a ) )
if not self.is_encoder_decoder:
__lowercase : int = inputs["""input_ids"""]
del inputs["input_ids"]
else:
__lowercase : Optional[int] = inputs["""input_ids"""]
__lowercase : Optional[Any] = inputs.get("""decoder_input_ids""" , __a )
del inputs["input_ids"]
inputs.pop("""decoder_input_ids""" , __a )
__lowercase : Union[str, Any] = model.get_input_embeddings()
if not self.is_encoder_decoder:
__lowercase : Dict = wte(__a )
else:
__lowercase : str = wte(__a )
__lowercase : Union[str, Any] = wte(__a )
with torch.no_grad():
model(**__a )[0]
def lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase : str = self.model_tester.prepare_config_and_inputs()
__lowercase : List[str] = input_dict["""input_ids"""]
__lowercase : Optional[int] = input_ids.ne(1 ).to(__a )
__lowercase : List[Any] = MaMaaaForConditionalGeneration(__a ).eval().to(__a )
if torch_device == "cuda":
model.half()
model.generate(__a , attention_mask=__a )
model.generate(num_beams=4 , do_sample=__a , early_stopping=__a , num_return_sequences=3 )
def snake_case_ ( lowerCAmelCase_ : Optional[Any] ):
return torch.tensor(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_ )
lowerCamelCase : Dict = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" )
def lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase : List[str] = MaMaaaModel.from_pretrained("""facebook/m2m100_418M""" ).to(__a )
__lowercase : Union[str, Any] = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
__lowercase : int = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
__lowercase : int = prepare_mam_aaa_inputs_dict(model.config , __a , __a )
with torch.no_grad():
__lowercase : int = model(**__a )[0]
__lowercase : int = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , __a )
# change to expected output here
__lowercase : Dict = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=__a )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=__a ) )
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Tuple = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(__a )
# change to intended input
__lowercase : Any = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
__lowercase : Union[str, Any] = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
__lowercase : Tuple = prepare_mam_aaa_inputs_dict(model.config , __a , __a )
with torch.no_grad():
__lowercase : Optional[Any] = model(**__a )[0]
__lowercase : Tuple = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , __a )
# change to expected output here
__lowercase : Union[str, Any] = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=__a )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=__a ) )
def lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
__lowercase : List[str] = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(__a )
__lowercase : Tuple = MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" , src_lang="""fr""" , tgt_lang="""en""" )
__lowercase : Dict = [
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
""" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
""" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
__lowercase : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors="""pt""" )
__lowercase : Dict = model.generate(
input_ids=dct["""input_ids"""].to(__a ) , attention_mask=dct["""attention_mask"""].to(__a ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("""en""" ) , )
__lowercase : Any = [
"""The NSA case highlights the total absence of intelligence debate""",
"""I think there are two levels of response from the French government.""",
"""When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
""" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
""" communications in France.""",
]
__lowercase : Dict = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=__a , skip_special_tokens=__a )
assert generated == expected_en
| 233 | 0 |
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
_A : List[str] = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_A : str = 25_00_04
_A : str = 25_00_20
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ,unittest.TestCase ):
snake_case : List[str] = MBartTokenizer
snake_case : List[Any] = MBartTokenizerFast
snake_case : Any = True
snake_case : Optional[Any] = True
def __lowerCamelCase ( self : Dict ) ->List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ : Optional[Any] = MBartTokenizer(_UpperCAmelCase , keep_accents=_UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self : List[Any] ) ->Union[str, Any]:
lowerCamelCase__ : Dict = MBartTokenizer(_UpperCAmelCase , keep_accents=_UpperCAmelCase )
lowerCamelCase__ : List[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCamelCase__ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_UpperCAmelCase , [
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''',
'''.''',
] , )
lowerCamelCase__ : int = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
self.assertListEqual(
_UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase__ : Optional[Any] = tokenizer.convert_ids_to_tokens(_UpperCAmelCase )
self.assertListEqual(
_UpperCAmelCase , [
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 __lowerCamelCase ( self : Optional[int] ) ->int:
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
lowerCamelCase__ : Tuple = (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})" ):
lowerCamelCase__ : List[str] = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
lowerCamelCase__ : Any = self.tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
lowerCamelCase__ : List[str] = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = tokenizer_r.save_pretrained(_UpperCAmelCase )
lowerCamelCase__ : List[str] = tokenizer_p.save_pretrained(_UpperCAmelCase )
# 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 ) )
lowerCamelCase__ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(_UpperCAmelCase , _UpperCAmelCase )
# Checks everything loads correctly in the same way
lowerCamelCase__ : int = tokenizer_r.from_pretrained(_UpperCAmelCase )
lowerCamelCase__ : Any = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase , _UpperCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(_UpperCAmelCase )
# Save tokenizer rust, legacy_format=True
lowerCamelCase__ : Optional[int] = tempfile.mkdtemp()
lowerCamelCase__ : Dict = tokenizer_r.save_pretrained(_UpperCAmelCase , legacy_format=_UpperCAmelCase )
lowerCamelCase__ : str = tokenizer_p.save_pretrained(_UpperCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(_UpperCAmelCase , _UpperCAmelCase )
# Checks everything loads correctly in the same way
lowerCamelCase__ : List[Any] = tokenizer_r.from_pretrained(_UpperCAmelCase )
lowerCamelCase__ : Any = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase , _UpperCAmelCase ) )
shutil.rmtree(_UpperCAmelCase )
# Save tokenizer rust, legacy_format=False
lowerCamelCase__ : str = tempfile.mkdtemp()
lowerCamelCase__ : str = tokenizer_r.save_pretrained(_UpperCAmelCase , legacy_format=_UpperCAmelCase )
lowerCamelCase__ : int = tokenizer_p.save_pretrained(_UpperCAmelCase )
# 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
lowerCamelCase__ : Optional[Any] = tokenizer_r.from_pretrained(_UpperCAmelCase )
lowerCamelCase__ : Any = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase , _UpperCAmelCase ) )
shutil.rmtree(_UpperCAmelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case : str = 'facebook/mbart-large-en-ro'
snake_case : int = [
' 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.',
]
snake_case : Tuple = [
'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.',
]
snake_case : Union[str, Any] = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def __lowerCamelCase ( cls : List[Any] ) ->Union[str, Any]:
lowerCamelCase__ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCamelCase__ : List[str] = 1
return cls
def __lowerCamelCase ( self : str ) ->str:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def __lowerCamelCase ( self : Any ) ->Tuple:
lowerCamelCase__ : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _UpperCAmelCase )
def __lowerCamelCase ( self : Dict ) ->Any:
self.assertIn(_UpperCAmelCase , self.tokenizer.all_special_ids )
lowerCamelCase__ : Optional[Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCamelCase__ : Tuple = self.tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
lowerCamelCase__ : List[str] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertNotIn(self.tokenizer.eos_token , _UpperCAmelCase )
def __lowerCamelCase ( self : Optional[int] ) ->Optional[Any]:
lowerCamelCase__ : str = ['this is gunna be a long sentence ' * 2_0]
assert isinstance(src_text[0] , _UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = 1_0
lowerCamelCase__ : Union[str, Any] = self.tokenizer(_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , _UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
def __lowerCamelCase ( self : List[str] ) ->Union[str, Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def __lowerCamelCase ( self : Dict ) ->List[Any]:
lowerCamelCase__ : Optional[Any] = tempfile.mkdtemp()
lowerCamelCase__ : Optional[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = MBartTokenizer.from_pretrained(_UpperCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , _UpperCAmelCase )
@require_torch
def __lowerCamelCase ( self : List[str] ) ->List[Any]:
lowerCamelCase__ : Optional[int] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_UpperCAmelCase , return_tensors='''pt''' )
lowerCamelCase__ : 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 __lowerCamelCase ( self : Dict ) ->Optional[int]:
lowerCamelCase__ : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCamelCase__ : Dict = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCamelCase__ : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , _UpperCAmelCase )
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 __lowerCamelCase ( self : List[Any] ) ->Optional[Any]:
lowerCamelCase__ : Tuple = self.tokenizer(self.src_text , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=3 , return_tensors='''pt''' )
lowerCamelCase__ : str = self.tokenizer(
text_target=self.tgt_text , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=1_0 , return_tensors='''pt''' )
lowerCamelCase__ : Any = targets['input_ids']
lowerCamelCase__ : List[Any] = shift_tokens_right(_UpperCAmelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def __lowerCamelCase ( self : Optional[int] ) ->Union[str, Any]:
lowerCamelCase__ : str = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , )
| 369 |
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def _a ( UpperCAmelCase = 10000 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Tuple = []
for num in range(1 , UpperCAmelCase ):
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Union[str, Any] = num
while iterations < 50:
lowerCamelCase__ : Dict = sum_reverse(UpperCAmelCase )
iterations += 1
if is_palindrome(UpperCAmelCase ):
break
else:
lychrel_nums.append(UpperCAmelCase )
return len(UpperCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 265 | 0 |
UpperCAmelCase : List[str] ={
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
UpperCAmelCase : Any ={
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ = from_type.lower().strip("s")
UpperCamelCase_ = to_type.lower().strip("s")
UpperCamelCase_ = UNIT_SYMBOL.get(_lowerCAmelCase , _lowerCAmelCase)
UpperCamelCase_ = UNIT_SYMBOL.get(_lowerCAmelCase , _lowerCAmelCase)
if from_sanitized not in METRIC_CONVERSION:
UpperCamelCase_ = (
f"""Invalid 'from_type' value: {from_type!r}.\n"""
f"""Conversion abbreviations are: {', '.join(_lowerCAmelCase)}"""
)
raise ValueError(_lowerCAmelCase)
if to_sanitized not in METRIC_CONVERSION:
UpperCamelCase_ = (
f"""Invalid 'to_type' value: {to_type!r}.\n"""
f"""Conversion abbreviations are: {', '.join(_lowerCAmelCase)}"""
)
raise ValueError(_lowerCAmelCase)
UpperCamelCase_ = METRIC_CONVERSION[from_sanitized]
UpperCamelCase_ = METRIC_CONVERSION[to_sanitized]
UpperCamelCase_ = 1
if from_exponent > to_exponent:
UpperCamelCase_ = from_exponent - to_exponent
else:
UpperCamelCase_ = -(to_exponent - from_exponent)
return value * pow(10 , _lowerCAmelCase)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 128 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase : Dict =TypeVar("""T""")
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = data
UpperCamelCase_ = None
def __str__( self ):
'''simple docstring'''
return F"""{self.data}"""
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
UpperCamelCase_ = None
def __iter__( self ):
'''simple docstring'''
UpperCamelCase_ = self.top
while node:
yield node.data
UpperCamelCase_ = node.next
def __str__( self ):
'''simple docstring'''
return "->".join([str(snake_case__ ) for item in self] )
def __len__( self ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def _lowerCamelCase ( self ):
'''simple docstring'''
return self.top is None
def _lowerCamelCase ( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = Node(snake_case__ )
if not self.is_empty():
UpperCamelCase_ = self.top
UpperCamelCase_ = node
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top , snake_case__ )
UpperCamelCase_ = self.top
UpperCamelCase_ = self.top.next
return pop_node.data
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 128 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 359 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
_lowerCamelCase : List[str] = {
'''b0''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1_408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1_536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1_792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2_048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2_304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2_560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def a_ ( __lowercase : int ) -> Optional[int]:
_snake_case = EfficientNetConfig()
_snake_case = CONFIG_MAP[model_name]['hidden_dim']
_snake_case = CONFIG_MAP[model_name]['width_coef']
_snake_case = CONFIG_MAP[model_name]['depth_coef']
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = CONFIG_MAP[model_name]['dropout_rate']
_snake_case = CONFIG_MAP[model_name]['dw_padding']
_snake_case = 'huggingface/label-files'
_snake_case = 'imagenet-1k-id2label.json'
_snake_case = 1_000
_snake_case = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='dataset' ) , 'r' ) )
_snake_case = {int(__lowercase ): v for k, v in idalabel.items()}
_snake_case = idalabel
_snake_case = {v: k for k, v in idalabel.items()}
return config
def a_ ( ) -> Any:
_snake_case = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_snake_case = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return im
def a_ ( __lowercase : Union[str, Any] ) -> Tuple:
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=__lowercase , )
return preprocessor
def a_ ( __lowercase : str ) -> List[Any]:
_snake_case = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
_snake_case = sorted(set(__lowercase ) )
_snake_case = len(__lowercase )
_snake_case = {b: str(__lowercase ) for b, i in zip(__lowercase , range(__lowercase ) )}
_snake_case = []
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
_snake_case = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
_snake_case = {}
for item in rename_keys:
if item[0] in original_param_names:
_snake_case = 'efficientnet.' + item[1]
_snake_case = 'classifier.weight'
_snake_case = 'classifier.bias'
return key_mapping
def a_ ( __lowercase : Any , __lowercase : Any , __lowercase : Any ) -> Optional[Any]:
for key, value in tf_params.items():
if "normalization" in key:
continue
_snake_case = key_mapping[key]
if "_conv" in key and "kernel" in key:
_snake_case = torch.from_numpy(__lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
_snake_case = torch.from_numpy(__lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
_snake_case = torch.from_numpy(np.transpose(__lowercase ) )
else:
_snake_case = torch.from_numpy(__lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__lowercase )
@torch.no_grad()
def a_ ( __lowercase : List[Any] , __lowercase : Any , __lowercase : int , __lowercase : str ) -> Dict:
_snake_case = model_classes[model_name](
include_top=__lowercase , weights='imagenet' , input_tensor=__lowercase , input_shape=__lowercase , pooling=__lowercase , classes=1_000 , classifier_activation='softmax' , )
_snake_case = original_model.trainable_variables
_snake_case = original_model.non_trainable_variables
_snake_case = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_snake_case = param.numpy()
_snake_case = list(tf_params.keys() )
# Load HuggingFace model
_snake_case = get_efficientnet_config(__lowercase )
_snake_case = EfficientNetForImageClassification(__lowercase ).eval()
_snake_case = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
_snake_case = rename_keys(__lowercase )
replace_params(__lowercase , __lowercase , __lowercase )
# Initialize preprocessor and preprocess input image
_snake_case = convert_image_processor(__lowercase )
_snake_case = preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
_snake_case = hf_model(**__lowercase )
_snake_case = outputs.logits.detach().numpy()
# Original model inference
_snake_case = False
_snake_case = CONFIG_MAP[model_name]['image_size']
_snake_case = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
_snake_case = image.img_to_array(__lowercase )
_snake_case = np.expand_dims(__lowercase , axis=0 )
_snake_case = original_model.predict(__lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__lowercase , __lowercase , atol=1E-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(__lowercase ):
os.mkdir(__lowercase )
# Save converted model and image processor
hf_model.save_pretrained(__lowercase )
preprocessor.save_pretrained(__lowercase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
_snake_case = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(__lowercase )
hf_model.push_to_hub(__lowercase )
if __name__ == "__main__":
_lowerCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
_lowerCamelCase : List[str] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 130 | 0 |
"""simple docstring"""
class _lowerCAmelCase :
def __init__( self ) -> None:
'''simple docstring'''
snake_case : dict[str, TrieNode] = {} # Mapping from char to TrieNode
snake_case : Tuple = False
def lowerCamelCase ( self , UpperCamelCase__ ) -> None:
'''simple docstring'''
for word in words:
self.insert(UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ ) -> None:
'''simple docstring'''
snake_case : str = self
for char in word:
if char not in curr.nodes:
snake_case : Tuple = TrieNode()
snake_case : Tuple = curr.nodes[char]
snake_case : Union[str, Any] = True
def lowerCamelCase ( self , UpperCamelCase__ ) -> bool:
'''simple docstring'''
snake_case : str = self
for char in word:
if char not in curr.nodes:
return False
snake_case : Dict = curr.nodes[char]
return curr.is_leaf
def lowerCamelCase ( self , UpperCamelCase__ ) -> None:
'''simple docstring'''
def _delete(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
if index == len(UpperCamelCase__ ):
# If word does not exist
if not curr.is_leaf:
return False
snake_case : int = False
return len(curr.nodes ) == 0
snake_case : Union[str, Any] = word[index]
snake_case : List[str] = curr.nodes.get(UpperCamelCase__ )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
snake_case : Union[str, Any] = _delete(UpperCamelCase__ , UpperCamelCase__ , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , UpperCamelCase__ , 0 )
def __lowerCAmelCase ( lowercase : TrieNode , lowercase : str ) -> None:
"""simple docstring"""
if node.is_leaf:
print(lowercase , end=" " )
for key, value in node.nodes.items():
print_words(lowercase , word + key )
def __lowerCAmelCase ( ) -> bool:
"""simple docstring"""
snake_case : Any = "banana bananas bandana band apple all beast".split()
snake_case : Any = TrieNode()
root.insert_many(lowercase )
# print_words(root, "")
assert all(root.find(lowercase ) for word in words )
assert root.find("banana" )
assert not root.find("bandanas" )
assert not root.find("apps" )
assert root.find("apple" )
assert root.find("all" )
root.delete("all" )
assert not root.find("all" )
root.delete("banana" )
assert not root.find("banana" )
assert root.find("bananas" )
return True
def __lowerCAmelCase ( lowercase : str , lowercase : bool ) -> None:
"""simple docstring"""
print(str(lowercase ) , "works!" if passes else "doesn't work :(" )
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
assert test_trie()
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
print_results("Testing trie functionality" , test_trie() )
if __name__ == "__main__":
main()
| 203 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__snake_case = """hf-internal-testing/tiny-random-bert"""
__snake_case = os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__snake_case = """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""
class _lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : int = cached_file(UpperCamelCase__ , UpperCamelCase__ )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(UpperCamelCase__ ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) ) )
with open(os.path.join(UpperCamelCase__ , "refs" , "main" ) ) as f:
snake_case : Dict = f.read()
self.assertEqual(UpperCamelCase__ , os.path.join(UpperCamelCase__ , "snapshots" , UpperCamelCase__ , UpperCamelCase__ ) )
self.assertTrue(os.path.isfile(UpperCamelCase__ ) )
# File is cached at the same place the second time.
snake_case : List[str] = cached_file(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# Using a specific revision to test the full commit hash.
snake_case : Any = cached_file(UpperCamelCase__ , UpperCamelCase__ , revision="9b8c223" )
self.assertEqual(UpperCamelCase__ , os.path.join(UpperCamelCase__ , "snapshots" , UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid model identifier" ):
snake_case : Optional[Any] = cached_file("tiny-random-bert" , UpperCamelCase__ )
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid git identifier" ):
snake_case : Optional[Any] = cached_file(UpperCamelCase__ , UpperCamelCase__ , revision="aaaa" )
with self.assertRaisesRegex(UpperCamelCase__ , "does not appear to have a file named" ):
snake_case : List[Any] = cached_file(UpperCamelCase__ , "conf" )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
with self.assertRaisesRegex(UpperCamelCase__ , "does not appear to have a file named" ):
snake_case : Tuple = cached_file(UpperCamelCase__ , "conf" )
with open(os.path.join(UpperCamelCase__ , "refs" , "main" ) ) as f:
snake_case : Any = f.read()
self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ , ".no_exist" , UpperCamelCase__ , "conf" ) ) )
snake_case : Optional[Any] = cached_file(UpperCamelCase__ , "conf" , _raise_exceptions_for_missing_entries=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
snake_case : Any = cached_file(UpperCamelCase__ , "conf" , local_files_only=UpperCamelCase__ , _raise_exceptions_for_missing_entries=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
snake_case : Any = mock.Mock()
snake_case : List[Any] = 500
snake_case : int = {}
snake_case : Optional[int] = HTTPError
snake_case : Tuple = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=UpperCamelCase__ ) as mock_head:
snake_case : Tuple = cached_file(UpperCamelCase__ , "conf" , _raise_exceptions_for_connection_errors=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , UpperCamelCase__ )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , UpperCamelCase__ , revision="ahaha" )
snake_case : int = get_file_from_repo("bert-base-cased" , UpperCamelCase__ )
# The name is the cached name which is not very easy to test, so instead we load the content.
snake_case : str = json.loads(open(UpperCamelCase__ , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case : int = Path(UpperCamelCase__ ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(UpperCamelCase__ , "a.txt" ) , str(UpperCamelCase__ ) )
self.assertIsNone(get_file_from_repo(UpperCamelCase__ , "b.txt" ) )
| 203 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('''foo.json''',)] )
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Dict ) -> str:
'''simple docstring'''
A: Dict = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
A: List[Any] = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Tuple ) -> Dict:
'''simple docstring'''
A: Optional[Any] = AutoConfig.from_pretrained('''gpt2''' )
A: Optional[Any] = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
A: Any = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _snake_case ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
A: Tuple = GenerationConfig()
A: Any = {
'''max_new_tokens''': 10_24,
'''foo''': '''bar''',
}
A: List[str] = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
A: List[Any] = generation_config.update(**SCREAMING_SNAKE_CASE_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 10_24 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(SCREAMING_SNAKE_CASE_ , {'''foo''': '''bar'''} )
def _snake_case ( self : int ) -> Any:
'''simple docstring'''
A: List[Any] = GenerationConfig()
A: Tuple = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(SCREAMING_SNAKE_CASE_ )
A: Optional[int] = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
A: Union[str, Any] = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_ )
assert not hasattr(SCREAMING_SNAKE_CASE_ , '''foo''' ) # no new kwargs should be initialized if from config
def _snake_case ( self : str ) -> Any:
'''simple docstring'''
A: int = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(default_config.num_beams , 1 )
A: int = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ )
A: Any = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , SCREAMING_SNAKE_CASE_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _snake_case ( cls : Any ) -> Optional[int]:
'''simple docstring'''
A: Tuple = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE_ )
@classmethod
def _snake_case ( cls : Optional[Any] ) -> Dict:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def _snake_case ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
A: Tuple = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
A: List[Any] = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='''test-generation-config''' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
A: Optional[Any] = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def _snake_case ( self : Any ) -> int:
'''simple docstring'''
A: List[Any] = GenerationConfig(
do_sample=SCREAMING_SNAKE_CASE_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
A: Optional[Any] = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
A: List[str] = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
| 363 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : torch.FloatTensor
class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self : str , SCREAMING_SNAKE_CASE_ : int = 6_55_36 , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 0 , SCREAMING_SNAKE_CASE_ : str = "fourier" , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , SCREAMING_SNAKE_CASE_ : Tuple[str] = "UNetMidBlock1D" , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : Tuple[int] = (32, 32, 64) , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : int = 8 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : bool = False , ) -> Tuple:
'''simple docstring'''
super().__init__()
A: Optional[Any] = sample_size
# time
if time_embedding_type == "fourier":
A: Tuple = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=SCREAMING_SNAKE_CASE_ , log=SCREAMING_SNAKE_CASE_ , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ )
A: List[str] = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
A: str = Timesteps(
block_out_channels[0] , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ , downscale_freq_shift=SCREAMING_SNAKE_CASE_ )
A: Any = block_out_channels[0]
if use_timestep_embedding:
A: Optional[Any] = block_out_channels[0] * 4
A: List[Any] = TimestepEmbedding(
in_channels=SCREAMING_SNAKE_CASE_ , time_embed_dim=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , out_dim=block_out_channels[0] , )
A: Optional[Any] = nn.ModuleList([] )
A: str = None
A: str = nn.ModuleList([] )
A: Tuple = None
# down
A: Any = in_channels
for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
A: Optional[int] = output_channel
A: List[Any] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
A: List[Any] = i == len(SCREAMING_SNAKE_CASE_ ) - 1
A: Optional[int] = get_down_block(
SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(SCREAMING_SNAKE_CASE_ )
# mid
A: Union[str, Any] = get_mid_block(
SCREAMING_SNAKE_CASE_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=SCREAMING_SNAKE_CASE_ , add_downsample=SCREAMING_SNAKE_CASE_ , )
# up
A: Optional[Any] = list(reversed(SCREAMING_SNAKE_CASE_ ) )
A: List[str] = reversed_block_out_channels[0]
if out_block_type is None:
A: int = out_channels
else:
A: Union[str, Any] = block_out_channels[0]
for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
A: List[Any] = output_channel
A: int = (
reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_ ) - 1 else final_upsample_channels
)
A: Optional[int] = i == len(SCREAMING_SNAKE_CASE_ ) - 1
A: Optional[Any] = get_up_block(
SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(SCREAMING_SNAKE_CASE_ )
A: Any = output_channel
# out
A: List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
A: Optional[int] = get_out_block(
out_block_type=SCREAMING_SNAKE_CASE_ , num_groups_out=SCREAMING_SNAKE_CASE_ , embed_dim=block_out_channels[0] , out_channels=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , fc_dim=block_out_channels[-1] // 4 , )
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, float, int] , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[UNetaDOutput, Tuple]:
'''simple docstring'''
A: Any = timestep
if not torch.is_tensor(SCREAMING_SNAKE_CASE_ ):
A: Union[str, Any] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(SCREAMING_SNAKE_CASE_ ) and len(timesteps.shape ) == 0:
A: List[str] = timesteps[None].to(sample.device )
A: int = self.time_proj(SCREAMING_SNAKE_CASE_ )
if self.config.use_timestep_embedding:
A: List[Any] = self.time_mlp(SCREAMING_SNAKE_CASE_ )
else:
A: str = timestep_embed[..., None]
A: Union[str, Any] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
A: Tuple = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
A: List[str] = ()
for downsample_block in self.down_blocks:
A , A: Optional[int] = downsample_block(hidden_states=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
A: Dict = self.mid_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
A: List[Any] = down_block_res_samples[-1:]
A: List[str] = down_block_res_samples[:-1]
A: Optional[int] = upsample_block(SCREAMING_SNAKE_CASE_ , res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ )
# 5. post-process
if self.out_block:
A: Any = self.out_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )
| 334 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
'''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
'''junnyu/roformer_chinese_char_small''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
),
'''junnyu/roformer_chinese_char_base''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
),
'''junnyu/roformer_small_discriminator''': (
'''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
),
'''junnyu/roformer_small_generator''': (
'''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _a ( _lowercase):
_a : int = 'roformer'
def __init__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : Union[str, Any]=5_0000 , _SCREAMING_SNAKE_CASE : Dict=None , _SCREAMING_SNAKE_CASE : int=768 , _SCREAMING_SNAKE_CASE : int=12 , _SCREAMING_SNAKE_CASE : str=12 , _SCREAMING_SNAKE_CASE : Dict=3072 , _SCREAMING_SNAKE_CASE : Union[str, Any]="gelu" , _SCREAMING_SNAKE_CASE : List[Any]=0.1 , _SCREAMING_SNAKE_CASE : Optional[int]=0.1 , _SCREAMING_SNAKE_CASE : Union[str, Any]=1536 , _SCREAMING_SNAKE_CASE : List[Any]=2 , _SCREAMING_SNAKE_CASE : Tuple=0.02 , _SCREAMING_SNAKE_CASE : str=1E-12 , _SCREAMING_SNAKE_CASE : Optional[Any]=0 , _SCREAMING_SNAKE_CASE : Optional[Any]=False , _SCREAMING_SNAKE_CASE : str=True , **_SCREAMING_SNAKE_CASE : int , )-> Union[str, Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = vocab_size
lowerCAmelCase__ : List[Any] = hidden_size if embedding_size is None else embedding_size
lowerCAmelCase__ : str = hidden_size
lowerCAmelCase__ : Dict = num_hidden_layers
lowerCAmelCase__ : Union[str, Any] = num_attention_heads
lowerCAmelCase__ : Optional[int] = hidden_act
lowerCAmelCase__ : Tuple = intermediate_size
lowerCAmelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCAmelCase__ : Dict = max_position_embeddings
lowerCAmelCase__ : Optional[int] = type_vocab_size
lowerCAmelCase__ : str = initializer_range
lowerCAmelCase__ : List[Any] = layer_norm_eps
lowerCAmelCase__ : Any = rotary_value
lowerCAmelCase__ : Optional[int] = use_cache
class _a ( _lowercase):
@property
def UpperCAmelCase__( self : Optional[Any] )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase__ : Union[str, Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase__ : Dict = {0: '''batch''', 1: '''sequence'''}
lowerCAmelCase__ : List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 131 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = """https://openaipublic.azureedge.net/jukebox/models/"""
__snake_case = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] ):
if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
UpperCamelCase :int = key.replace('''.model.1.bias''' , '''.conv1d_1.bias''' )
elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
UpperCamelCase :Union[str, Any] = key.replace('''.model.1.weight''' , '''.conv1d_1.weight''' )
elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
UpperCamelCase :Optional[Any] = key.replace('''.model.3.bias''' , '''.conv1d_2.bias''' )
elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
UpperCamelCase :Optional[int] = key.replace('''.model.3.weight''' , '''.conv1d_2.weight''' )
if "conditioner_blocks.0." in key:
UpperCamelCase :Any = key.replace('''conditioner_blocks.0''' , '''conditioner_blocks''' )
if "prime_prior" in key:
UpperCamelCase :int = key.replace('''prime_prior''' , '''encoder''' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
UpperCamelCase :Any = key.replace('''.emb.''' , '''.''' )
if key.endswith('''k''' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('''.k''' , '''.codebook''' )
if "y_emb." in key:
return key.replace('''y_emb.''' , '''metadata_embedding.''' )
if "x_emb.emb." in key:
UpperCamelCase :str = key.replace('''0.x_emb.emb''' , '''embed_tokens''' )
if "prime_state_ln" in key:
return key.replace('''prime_state_ln''' , '''encoder.final_layer_norm''' )
if ".ln" in key:
return key.replace('''.ln''' , '''.layer_norm''' )
if "_ln" in key:
return key.replace('''_ln''' , '''_layer_norm''' )
if "prime_state_proj" in key:
return key.replace('''prime_state_proj''' , '''encoder.proj_in''' )
if "prime_x_out" in key:
return key.replace('''prime_x_out''' , '''encoder.lm_head''' )
if "prior.x_out" in key:
return key.replace('''x_out''' , '''fc_proj_out''' )
if "x_emb" in key:
return key.replace('''x_emb''' , '''embed_tokens''' )
return key
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ):
UpperCamelCase :Optional[int] = {}
import re
UpperCamelCase :int = re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
UpperCamelCase :str = re.compile(
R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
UpperCamelCase :int = re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
UpperCamelCase :Tuple = re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
UpperCamelCase :int = re.compile(
R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
UpperCamelCase :Optional[int] = re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
UpperCamelCase :Optional[Any] = re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
UpperCamelCase :int = re.compile(
R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
UpperCamelCase :Tuple = re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :int = re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[str] = regex_match.groups()
UpperCamelCase :List[str] = int(groups[2] ) * 2 + int(groups[3] )
UpperCamelCase :List[Any] = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
UpperCamelCase :int = re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[Any] = re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Optional[Any] = regex_match.groups()
UpperCamelCase :Any = int(groups[2] ) * 2 + int(groups[3] )
UpperCamelCase :Any = {'''1''': 1, '''3''': 2}[groups[-2]]
UpperCamelCase :str = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
UpperCamelCase :List[str] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
UpperCamelCase :Union[str, Any] = prefix + resnet_block
UpperCamelCase :str = re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[int] = re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :int = regex_match.groups()
UpperCamelCase :int = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
UpperCamelCase :str = re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[Any] = re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Optional[int] = regex_match.groups()
UpperCamelCase :str = int(groups[2] ) * 2 + int(groups[3] ) - 2
UpperCamelCase :List[Any] = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
UpperCamelCase :Union[str, Any] = re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[Any] = re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Any = regex_match.groups()
UpperCamelCase :List[str] = int(groups[2] ) * 2 + int(groups[3] ) - 2
UpperCamelCase :Optional[int] = {'''1''': 1, '''3''': 2}[groups[-2]]
UpperCamelCase :Any = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
UpperCamelCase :Optional[int] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
UpperCamelCase :Any = prefix + resnet_block
UpperCamelCase :Optional[int] = re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[int] = re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[Any] = regex_match.groups()
UpperCamelCase :List[Any] = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
UpperCamelCase :Any = re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[Any] = re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Optional[int] = regex_match.groups()
UpperCamelCase :str = int(groups[1] ) * 2 + int(groups[2] ) - 2
UpperCamelCase :Tuple = F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
UpperCamelCase :int = re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :int = re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Any = regex_match.groups()
UpperCamelCase :Optional[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
UpperCamelCase :int = {'''1''': 1, '''3''': 2}[groups[-2]]
UpperCamelCase :Tuple = F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
UpperCamelCase :List[Any] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
UpperCamelCase :Any = prefix + resnet_block
UpperCamelCase :Dict = re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :List[str] = re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[str] = regex_match.groups()
UpperCamelCase :Dict = F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
UpperCamelCase :Any = re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# keep original key
else:
UpperCamelCase :List[str] = original_key
UpperCamelCase :Any = replace_key(SCREAMING_SNAKE_CASE__ )
if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(F'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
UpperCamelCase :Union[str, Any] = model_state_dict[F'''{key_prefix}.{key}''']
print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
UpperCamelCase :List[Any] = original_key
UpperCamelCase :Any = original_key
UpperCamelCase :Optional[int] = value
return new_dict
@torch.no_grad()
def _A ( SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Dict=None ):
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' ):
UpperCamelCase :Dict = requests.get(F'''{PREFIX}{file}''' , allow_redirects=SCREAMING_SNAKE_CASE__ )
os.makedirs(F'''{pytorch_dump_folder_path}/''' , exist_ok=SCREAMING_SNAKE_CASE__ )
open(F'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' , '''wb''' ).write(r.content )
UpperCamelCase :Optional[int] = MODEL_MAPPING[model_name.split('''/''' )[-1]]
UpperCamelCase :Any = JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :List[str] = JukeboxModel(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Dict = []
UpperCamelCase :List[Any] = {}
for i, dict_name in enumerate(SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :int = torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}''' )['''model''']
UpperCamelCase :Tuple = {}
for k in old_dic.keys():
if k.endswith('''.b''' ):
UpperCamelCase :Optional[int] = old_dic[k]
elif k.endswith('''.w''' ):
UpperCamelCase :Optional[Any] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
UpperCamelCase :Optional[Any] = old_dic[k]
else:
UpperCamelCase :Any = old_dic[k]
UpperCamelCase :Any = '''vqvae''' if i == 0 else F'''priors.{3 - i}'''
UpperCamelCase :Dict = fix_jukebox_keys(SCREAMING_SNAKE_CASE__ , model.state_dict() , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
weight_dict.append(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Optional[int] = weight_dict.pop(0 )
model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE__ )
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
with open(F'''{pytorch_dump_folder_path}/mapping.json''' , '''w''' ) as txtfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
return weight_dict
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
__snake_case = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 259 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongT5EncoderModel''',
'''LongT5ForConditionalGeneration''',
'''LongT5Model''',
'''LongT5PreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : str = [
'''FlaxLongT5ForConditionalGeneration''',
'''FlaxLongT5Model''',
'''FlaxLongT5PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 359 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : bool , snake_case_ : bool ) -> Tuple:
"""simple docstring"""
def run_func(snake_case_ : Union[str, Any] ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : Optional[int] , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : Dict , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int , snake_case_ : int ) -> ["tf.Tensor"]:
"""simple docstring"""
_lowerCAmelCase = random.Random()
_lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = "TensorFlow"
@property
def A__ (self ):
'''simple docstring'''
return tf.__version__
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase , decoder_input_ids=lowerCamelCase , training=lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase , training=lowerCamelCase )
_lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_lowerCAmelCase = model(lowerCamelCase , decoder_input_ids=lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
_lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_lowerCAmelCase = timeit.repeat(
lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
_lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
_lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
_lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase )
_lowerCAmelCase = meminfo.used
_lowerCAmelCase = Memory(lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
_lowerCAmelCase = None
else:
_lowerCAmelCase = measure_peak_memory_cpu(lowerCamelCase )
_lowerCAmelCase = Memory(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_lowerCAmelCase = stop_memory_tracing(lowerCamelCase )
if memory is None:
_lowerCAmelCase = summary.total
else:
_lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 317 | 0 |
import enum
import shutil
import sys
__A, __A : Union[str, Any] = shutil.get_terminal_size()
__A : List[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class _SCREAMING_SNAKE_CASE ( enum.Enum):
_UpperCamelCase:Dict = 0
_UpperCamelCase:str = 1
def __UpperCamelCase ( _A : int , _A : List[str]="" ) ->Optional[int]:
"""simple docstring"""
sys.stdout.write(str(_A ) + end )
sys.stdout.flush()
def __UpperCamelCase ( _A : Optional[int] , _A : List[str] , _A : List[Any]="" ) ->int:
"""simple docstring"""
forceWrite(f'\u001b[{color}m{content}\u001b[0m' , _A )
def __UpperCamelCase ( ) ->Union[str, Any]:
"""simple docstring"""
forceWrite("""\r""" )
def __UpperCamelCase ( _A : int , _A : str ) ->Any:
"""simple docstring"""
forceWrite(f'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}' )
def __UpperCamelCase ( ) ->int:
"""simple docstring"""
forceWrite(""" """ * TERMINAL_WIDTH )
reset_cursor()
def __UpperCamelCase ( ) ->Optional[Any]:
"""simple docstring"""
reset_cursor()
forceWrite("""-""" * TERMINAL_WIDTH )
| 154 |
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _SCREAMING_SNAKE_CASE ( unittest.TestCase):
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=400 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , )-> Union[str, Any]:
lowerCamelCase_ =size if size is not None else {"""shortest_edge""": 20}
lowerCamelCase_ =crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =image_size
lowerCamelCase_ =min_resolution
lowerCamelCase_ =max_resolution
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_flip_channel_order
def _snake_case ( self )-> List[Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ , unittest.TestCase):
_UpperCamelCase:Tuple = MobileViTImageProcessor if is_vision_available() else None
def _snake_case ( self )-> List[str]:
lowerCamelCase_ =MobileViTImageProcessingTester(self )
@property
def _snake_case ( self )-> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case ( self )-> Any:
lowerCamelCase_ =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_flip_channel_order""" ) )
def _snake_case ( self )-> Optional[Any]:
lowerCamelCase_ =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
lowerCamelCase_ =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 _snake_case ( self )-> Union[str, Any]:
pass
def _snake_case ( self )-> Dict:
# Initialize image_processing
lowerCamelCase_ =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase_ =prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
lowerCamelCase_ =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
lowerCamelCase_ =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 _snake_case ( self )-> str:
# Initialize image_processing
lowerCamelCase_ =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase_ =prepare_image_inputs(self.image_processor_tester , 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
lowerCamelCase_ =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
lowerCamelCase_ =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 _snake_case ( self )-> List[Any]:
# Initialize image_processing
lowerCamelCase_ =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase_ =prepare_image_inputs(self.image_processor_tester , 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
lowerCamelCase_ =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
lowerCamelCase_ =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"""],
) , )
| 154 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[str] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 352 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowerCamelCase : int = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( snake_case_ : int , snake_case_ : Any ):
snake_case__ : List[str] = b.T
snake_case__ : Union[str, Any] = np.sum(np.square(snake_case_ ) , axis=1 )
snake_case__ : Dict = np.sum(np.square(snake_case_ ) , axis=0 )
snake_case__ : Dict = np.matmul(snake_case_ , snake_case_ )
snake_case__ : Any = aa[:, None] - 2 * ab + ba[None, :]
return d
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] , snake_case_ : Tuple ):
snake_case__ : Tuple = x.reshape(-1 , 3 )
snake_case__ : int = squared_euclidean_distance(snake_case_ , snake_case_ )
return np.argmin(snake_case_ , axis=1 )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = ["pixel_values"]
def __init__( self : str , __A : Optional[Union[List[List[int]], np.ndarray]] = None , __A : bool = True , __A : Dict[str, int] = None , __A : PILImageResampling = PILImageResampling.BILINEAR , __A : bool = True , __A : bool = True , **__A : Union[str, Any] , ):
super().__init__(**__A )
snake_case__ : Optional[int] = size if size is not None else {"height": 2_5_6, "width": 2_5_6}
snake_case__ : List[Any] = get_size_dict(__A )
snake_case__ : Any = np.array(__A ) if clusters is not None else None
snake_case__ : Optional[Any] = do_resize
snake_case__ : Any = size
snake_case__ : List[Any] = resample
snake_case__ : List[Any] = do_normalize
snake_case__ : Dict = do_color_quantize
def _lowercase ( self : List[Any] , __A : np.ndarray , __A : Dict[str, int] , __A : PILImageResampling = PILImageResampling.BILINEAR , __A : Optional[Union[str, ChannelDimension]] = None , **__A : int , ):
snake_case__ : List[Any] = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
__A , size=(size["height"], size["width"]) , resample=__A , data_format=__A , **__A )
def _lowercase ( self : List[Any] , __A : np.ndarray , __A : Optional[Union[str, ChannelDimension]] = None , ):
snake_case__ : List[str] = rescale(image=__A , scale=1 / 1_2_7.5 , data_format=__A )
snake_case__ : List[Any] = image - 1
return image
def _lowercase ( self : Dict , __A : ImageInput , __A : bool = None , __A : Dict[str, int] = None , __A : PILImageResampling = None , __A : bool = None , __A : Optional[bool] = None , __A : Optional[Union[List[List[int]], np.ndarray]] = None , __A : Optional[Union[str, TensorType]] = None , __A : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **__A : Optional[int] , ):
snake_case__ : Any = do_resize if do_resize is not None else self.do_resize
snake_case__ : Union[str, Any] = size if size is not None else self.size
snake_case__ : Union[str, Any] = get_size_dict(__A )
snake_case__ : Optional[Any] = resample if resample is not None else self.resample
snake_case__ : Tuple = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ : Optional[Any] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ : Union[str, Any] = clusters if clusters is not None else self.clusters
snake_case__ : Union[str, Any] = np.array(__A )
snake_case__ : Any = make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ : Optional[Any] = [to_numpy_array(__A ) for image in images]
if do_resize:
snake_case__ : List[str] = [self.resize(image=__A , size=__A , resample=__A ) for image in images]
if do_normalize:
snake_case__ : Union[str, Any] = [self.normalize(image=__A ) for image in images]
if do_color_quantize:
snake_case__ : int = [to_channel_dimension_format(__A , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ : int = np.array(__A )
snake_case__ : Dict = color_quantize(__A , __A ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ : str = images.shape[0]
snake_case__ : str = images.reshape(__A , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ : Union[str, Any] = list(__A )
else:
snake_case__ : Any = [to_channel_dimension_format(__A , __A ) for image in images]
snake_case__ : Optional[int] = {"input_ids": images}
return BatchFeature(data=__A , tensor_type=__A )
| 286 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Dict = "gpt_neo"
UpperCAmelCase__ : List[str] = ["past_key_values"]
UpperCAmelCase__ : Optional[Any] = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self, SCREAMING_SNAKE_CASE_=5_0257, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=24, SCREAMING_SNAKE_CASE_=[[["global", "local"], 12]], SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_="gelu_new", SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=1e-5, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=5_0256, SCREAMING_SNAKE_CASE_=5_0256, **SCREAMING_SNAKE_CASE_, ) -> Optional[Any]:
UpperCamelCase : Tuple = vocab_size
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Optional[Any] = hidden_size
UpperCamelCase : str = num_layers
UpperCamelCase : Any = num_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Any = window_size
UpperCamelCase : Any = activation_function
UpperCamelCase : Tuple = resid_dropout
UpperCamelCase : List[str] = embed_dropout
UpperCamelCase : Dict = attention_dropout
UpperCamelCase : Optional[Any] = classifier_dropout
UpperCamelCase : Optional[int] = layer_norm_epsilon
UpperCamelCase : Optional[Any] = initializer_range
UpperCamelCase : List[Any] = use_cache
UpperCamelCase : Tuple = bos_token_id
UpperCamelCase : Tuple = eos_token_id
UpperCamelCase : Union[str, Any] = attention_types
UpperCamelCase : int = self.expand_attention_types_params(SCREAMING_SNAKE_CASE_ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.attention_layers)` == `config.num_layers` '
F"""but is `len(config.attention_layers) = {len(self.attention_layers )}`, """
F"""`config.num_layers = {self.num_layers}`. """
'`config.attention_layers` is prepared using `config.attention_types`. '
'Please verify the value of `config.attention_types` argument.' )
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCamelCase : Optional[int] = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def UpperCamelCase ( snake_case__ : Dict , snake_case__ : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : int ) -> Dict:
import torch
UpperCamelCase : Dict = input.size()
UpperCamelCase : str = len(snake_case__ )
UpperCamelCase : str = shape[dimension]
UpperCamelCase : List[Any] = torch.arange(0 , snake_case__ , snake_case__ )
UpperCamelCase : Optional[int] = torch.div(sizedim - size , snake_case__ , rounding_mode='floor' ) + 1
UpperCamelCase : Dict = torch.arange(snake_case__ ) + low_indices[:min_length][:, None]
UpperCamelCase : Any = [slice(snake_case__ )] * rank
UpperCamelCase : List[str] = indices
UpperCamelCase : str = input[s]
UpperCamelCase : Dict = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(snake_case__ )
def UpperCamelCase ( snake_case__ : Tuple , snake_case__ : Optional[int] ) -> Union[str, Any]:
import torch
UpperCamelCase : Dict = torch.arange(1 , snake_case__ )
UpperCamelCase : Any = torch.remainder(snake_case__ , snake_case__ )
UpperCamelCase : Any = remainders == 0
UpperCamelCase : int = candidates[divisor_indices]
UpperCamelCase : str = torch.max(snake_case__ )
return largest_divisor, torch.div(snake_case__ , snake_case__ , rounding_mode='floor' )
class lowerCAmelCase_ ( a__ ):
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
UpperCamelCase : int = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_, direction='inputs' )
UpperCamelCase : Tuple = {0: 'batch', 1: 'past_sequence + sequence'}
else:
UpperCamelCase : List[Any] = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def snake_case_ ( self ) -> int:
return self._config.num_heads
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = -1, SCREAMING_SNAKE_CASE_ = -1, SCREAMING_SNAKE_CASE_ = False, SCREAMING_SNAKE_CASE_ = None, ) -> Mapping[str, Any]:
UpperCamelCase : str = super(SCREAMING_SNAKE_CASE_, self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE_, batch_size=SCREAMING_SNAKE_CASE_, seq_length=SCREAMING_SNAKE_CASE_, is_pair=SCREAMING_SNAKE_CASE_, framework=SCREAMING_SNAKE_CASE_ )
# We need to order the input in the way they appears in the forward()
UpperCamelCase : List[str] = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
UpperCamelCase , UpperCamelCase : Dict = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
UpperCamelCase : Union[str, Any] = seqlen + 2
UpperCamelCase : int = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCamelCase : str = [
(torch.zeros(SCREAMING_SNAKE_CASE_ ), torch.zeros(SCREAMING_SNAKE_CASE_ )) for _ in range(self.num_layers )
]
UpperCamelCase : List[str] = common_inputs['attention_mask']
if self.use_past:
UpperCamelCase : List[Any] = ordered_inputs['attention_mask'].dtype
UpperCamelCase : Union[str, Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, dtype=SCREAMING_SNAKE_CASE_ )], dim=1 )
return ordered_inputs
@property
def snake_case_ ( self ) -> int:
return 13
| 119 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def UpperCamelCase ( ) -> tuple[list[int], int]:
UpperCamelCase : int = [randint(-1000 , 1000 ) for i in range(10 )]
UpperCamelCase : Dict = randint(-5000 , 5000 )
return (arr, r)
__UpperCAmelCase = make_dataset()
def UpperCamelCase ( snake_case__ : list[int] , snake_case__ : int ) -> tuple[int, ...]:
for triplet in permutations(snake_case__ , 3 ):
if sum(snake_case__ ) == target:
return tuple(sorted(snake_case__ ) )
return (0, 0, 0)
def UpperCamelCase ( snake_case__ : list[int] , snake_case__ : int ) -> tuple[int, int, int]:
arr.sort()
UpperCamelCase : List[str] = len(snake_case__ )
for i in range(n - 1 ):
UpperCamelCase , UpperCamelCase : Optional[Any] = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def UpperCamelCase ( ) -> tuple[float, float]:
UpperCamelCase : Any = '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
UpperCamelCase : Optional[Any] = '\ntriplet_sum1(*dataset)\n'
UpperCamelCase : Dict = '\ntriplet_sum2(*dataset)\n'
UpperCamelCase : Optional[int] = repeat(setup=snake_case__ , stmt=snake_case__ , repeat=5 , number=10000 )
UpperCamelCase : Any = repeat(setup=snake_case__ , stmt=snake_case__ , repeat=5 , number=10000 )
return (min(snake_case__ ), min(snake_case__ ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCAmelCase = solution_times()
print(F"""The time for naive implementation is {times[0]}.""")
print(F"""The time for optimized implementation is {times[1]}.""")
| 119 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : int = StableDiffusionLDMaDPipeline
UpperCAmelCase__ : List[Any] = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase__ : Optional[Any] = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase__ : str = TEXT_TO_IMAGE_IMAGE_PARAMS
def __lowercase ( self ) -> List[str]:
torch.manual_seed(0 )
_a : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
_a : Optional[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=_a , set_alpha_to_one=_a , )
torch.manual_seed(0 )
_a : str = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=6 , out_channels=6 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
_a : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
_a : List[str] = CLIPTextModel(_a )
_a : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_a : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def __lowercase ( self , _a , _a=0 ) -> Optional[Any]:
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : str = torch.Generator(device=_a ).manual_seed(_a )
_a : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self ) -> int:
_a : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_a : Union[str, Any] = self.get_dummy_components()
_a : str = StableDiffusionLDMaDPipeline(**_a )
_a : int = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Dict = self.get_dummy_inputs(_a )
_a : Any = ldmad_pipe(**_a )
_a : Union[str, Any] = output.rgb, output.depth
_a : Tuple = rgb[0, -3:, -3:, -1]
_a : int = depth[0, -3:, -1]
assert rgb.shape == (1, 6_4, 6_4, 3)
assert depth.shape == (1, 6_4, 6_4)
_a : int = np.array(
[0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262] )
_a : Any = np.array([103.4_6727, 85.81_2004, 87.84_9236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def __lowercase ( self ) -> List[str]:
_a : Optional[int] = self.get_dummy_components()
_a : Optional[Any] = StableDiffusionLDMaDPipeline(**_a )
_a : int = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = self.get_dummy_inputs(_a )
_a : Union[str, Any] = 3 * [inputs['''prompt''']]
# forward
_a : List[Any] = ldmad_pipe(**_a )
_a : str = output.rgb, output.depth
_a : str = rgb_slice_a[0, -3:, -3:, -1]
_a : Optional[Any] = depth_slice_a[0, -3:, -1]
_a : Any = self.get_dummy_inputs(_a )
_a : List[Any] = 3 * [inputs.pop('''prompt''' )]
_a : int = ldmad_pipe.tokenizer(
_a , padding='''max_length''' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=_a , return_tensors='''pt''' , )
_a : Tuple = text_inputs['''input_ids'''].to(_a )
_a : Union[str, Any] = ldmad_pipe.text_encoder(_a )[0]
_a : Optional[int] = prompt_embeds
# forward
_a : Dict = ldmad_pipe(**_a )
_a : List[str] = output.rgb, output.depth
_a : Union[str, Any] = rgb_slice_a[0, -3:, -3:, -1]
_a : int = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def __lowercase ( self ) -> Dict:
_a : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_a : List[str] = self.get_dummy_components()
_a : Any = PNDMScheduler(skip_prk_steps=_a )
_a : Any = StableDiffusionLDMaDPipeline(**_a )
_a : Any = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Tuple = self.get_dummy_inputs(_a )
_a : int = '''french fries'''
_a : Tuple = ldmad_pipe(**_a , negative_prompt=_a )
_a : Optional[Any] = output.rgb, output.depth
_a : List[Any] = rgb[0, -3:, -3:, -1]
_a : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 6_4, 6_4, 3)
assert depth.shape == (1, 6_4, 6_4)
_a : Any = np.array(
[0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217] )
_a : int = np.array([107.8_4738, 84.6_2802, 89.96_2135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self , _a , _a="cpu" , _a=torch.floataa , _a=0 ) -> Tuple:
_a : int = torch.Generator(device=_a ).manual_seed(_a )
_a : Optional[int] = np.random.RandomState(_a ).standard_normal((1, 4, 6_4, 6_4) )
_a : Dict = torch.from_numpy(_a ).to(device=_a , dtype=_a )
_a : int = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Tuple = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
_a : Optional[Any] = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : int = self.get_inputs(_a )
_a : Optional[int] = ldmad_pipe(**_a )
_a : Any = output.rgb, output.depth
_a : Tuple = rgb[0, -3:, -3:, -1].flatten()
_a : int = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
assert depth.shape == (1, 5_1_2, 5_1_2)
_a : int = np.array(
[0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706] )
_a : Dict = np.array(
[0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self , _a , _a="cpu" , _a=torch.floataa , _a=0 ) -> Optional[int]:
_a : Optional[Any] = torch.Generator(device=_a ).manual_seed(_a )
_a : Dict = np.random.RandomState(_a ).standard_normal((1, 4, 6_4, 6_4) )
_a : Optional[Any] = torch.from_numpy(_a ).to(device=_a , dtype=_a )
_a : str = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 5_0,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self ) -> Tuple:
_a : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Any = self.get_inputs(_a )
_a : List[str] = ldmad_pipe(**_a )
_a : Any = output.rgb, output.depth
_a : str = 0.49_5586
_a : Union[str, Any] = 0.3379_5515
_a : Any = 112.4_8518
_a : List[Any] = 98.48_9746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def __lowercase ( self ) -> int:
_a : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : List[str] = self.get_inputs(_a )
_a : List[Any] = ldmad_pipe(**_a )
_a : Union[str, Any] = output.rgb, output.depth
_a : int = 0.419_4127
_a : str = 0.3537_5586
_a : str = 0.563_8502
_a : Tuple = 0.3468_6103
assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
assert depth.shape == (1, 5_1_2, 5_1_2, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 365 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a__ = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: "Table of Contents"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Dataset Description"
allow_empty: false
allow_empty_text: false
subsections:
- name: "Dataset Summary"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Supported Tasks and Leaderboards"
allow_empty: true
allow_empty_text: true
subsections: null
- name: Languages
allow_empty: false
allow_empty_text: true
subsections: null
'''
)
a__ = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Extra Ignored Subsection''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
}
],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
a__ = '''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = (
'''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)
a__ = '''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = (
'''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)
a__ = '''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'''
a__ = '''\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'''
a__ = ''''''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'''
@pytest.mark.parametrize(
'''readme_md, expected_dict''' ,[
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] ,)
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : List[str] ) -> Optional[int]:
"""simple docstring"""
assert ReadMe.from_string(__a ,__a ).to_dict() == expected_dict
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] ,)
def __UpperCAmelCase ( __a : List[str] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with pytest.raises(__a ,match=re.escape(expected_error.format(path='''root''' ) ) ):
_a : List[Any] = ReadMe.from_string(__a ,__a )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Dict ,__a : Dict ) -> Tuple:
"""simple docstring"""
with pytest.raises(__a ,match=re.escape(expected_error.format(path='''root''' ) ) ):
ReadMe.from_string(__a ,__a )
@pytest.mark.parametrize(
'''readme_md,''' ,[
(README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
ReadMe.from_string(__a ,__a ,suppress_parsing_errors=__a )
@pytest.mark.parametrize(
'''readme_md, expected_dict''' ,[
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] ,)
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Any ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : Tuple = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : Optional[Any] = ReadMe.from_readme(__a ,__a ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] ,)
def __UpperCAmelCase ( __a : List[Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : int = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : Optional[int] = expected_error.format(path=__a )
with pytest.raises(__a ,match=re.escape(__a ) ):
_a : Any = ReadMe.from_readme(__a ,__a )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : str ,__a : Union[str, Any] ) -> Dict:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : Optional[Any] = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : str = expected_error.format(path=__a )
with pytest.raises(__a ,match=re.escape(__a ) ):
ReadMe.from_readme(__a ,__a )
@pytest.mark.parametrize(
'''readme_md,''' ,[
(README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Optional[Any] ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : int = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
ReadMe.from_readme(__a ,__a ,suppress_parsing_errors=__a )
| 15 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
lowerCAmelCase = len(SCREAMING_SNAKE_CASE ) + 1
lowerCAmelCase = len(SCREAMING_SNAKE_CASE ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowerCAmelCase = [[0 for i in range(SCREAMING_SNAKE_CASE )] for j in range(SCREAMING_SNAKE_CASE )]
# since string of zero length match pattern of zero length
lowerCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , SCREAMING_SNAKE_CASE ):
for j in range(1 , SCREAMING_SNAKE_CASE ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowerCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowerCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowerCAmelCase = dp[i - 1][j]
else:
lowerCAmelCase = 0
else:
lowerCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
SCREAMING_SNAKE_CASE__ = "aab"
SCREAMING_SNAKE_CASE__ = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 46 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
UpperCAmelCase__ = {
"""b0""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 2_2_4,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 2_4_0,
"""dropout_rate""": 0.2,
"""dw_padding""": [1_6],
},
"""b2""": {
"""hidden_dim""": 1_4_0_8,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 2_6_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 1_6],
},
"""b3""": {
"""hidden_dim""": 1_5_3_6,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 3_0_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 1_8],
},
"""b4""": {
"""hidden_dim""": 1_7_9_2,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 3_8_0,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_0_4_8,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 4_5_6,
"""dropout_rate""": 0.4,
"""dw_padding""": [1_3, 2_7],
},
"""b6""": {
"""hidden_dim""": 2_3_0_4,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 5_2_8,
"""dropout_rate""": 0.5,
"""dw_padding""": [3_1],
},
"""b7""": {
"""hidden_dim""": 2_5_6_0,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 6_0_0,
"""dropout_rate""": 0.5,
"""dw_padding""": [1_8],
},
}
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = EfficientNetConfig()
_UpperCAmelCase = CONFIG_MAP[model_name]["""hidden_dim"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""width_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""depth_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dropout_rate"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dw_padding"""]
_UpperCAmelCase = """huggingface/label-files"""
_UpperCAmelCase = """imagenet-1k-id2label.json"""
_UpperCAmelCase = 10_00
_UpperCAmelCase = json.load(open(hf_hub_download(lowercase ,lowercase ,repo_type="""dataset""" ) ,"""r""" ) )
_UpperCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase = Image.open(requests.get(lowercase ,stream=lowercase ).raw )
return im
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} ,image_mean=[0.4_85, 0.4_56, 0.4_06] ,image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] ,do_center_crop=lowercase ,)
return preprocessor
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
_UpperCAmelCase = sorted(set(lowercase ) )
_UpperCAmelCase = len(lowercase )
_UpperCAmelCase = {b: str(lowercase ) for b, i in zip(lowercase ,range(lowercase ) )}
_UpperCAmelCase = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
_UpperCAmelCase = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
_UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
_UpperCAmelCase = """efficientnet.""" + item[1]
_UpperCAmelCase = """classifier.weight"""
_UpperCAmelCase = """classifier.bias"""
return key_mapping
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
_UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
_UpperCAmelCase = torch.from_numpy(lowercase ).permute(3 ,2 ,0 ,1 )
elif "depthwise_kernel" in key:
_UpperCAmelCase = torch.from_numpy(lowercase ).permute(2 ,3 ,0 ,1 )
elif "kernel" in key:
_UpperCAmelCase = torch.from_numpy(np.transpose(lowercase ) )
else:
_UpperCAmelCase = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = model_classes[model_name](
include_top=lowercase ,weights="""imagenet""" ,input_tensor=lowercase ,input_shape=lowercase ,pooling=lowercase ,classes=10_00 ,classifier_activation="""softmax""" ,)
_UpperCAmelCase = original_model.trainable_variables
_UpperCAmelCase = original_model.non_trainable_variables
_UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_UpperCAmelCase = param.numpy()
_UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
_UpperCAmelCase = get_efficientnet_config(lowercase )
_UpperCAmelCase = EfficientNetForImageClassification(lowercase ).eval()
_UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
_UpperCAmelCase = rename_keys(lowercase )
replace_params(lowercase ,lowercase ,lowercase )
# Initialize preprocessor and preprocess input image
_UpperCAmelCase = convert_image_processor(lowercase )
_UpperCAmelCase = preprocessor(images=prepare_img() ,return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
_UpperCAmelCase = hf_model(**lowercase )
_UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
_UpperCAmelCase = False
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = prepare_img().resize((image_size, image_size) ,resample=PIL.Image.NEAREST )
_UpperCAmelCase = image.img_to_array(lowercase )
_UpperCAmelCase = np.expand_dims(lowercase ,axis=0 )
_UpperCAmelCase = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase ,lowercase ,atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
_UpperCAmelCase = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
UpperCAmelCase__ = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 289 | 0 |
import numpy
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] , __a : numpy.ndarray , __a : numpy.ndarray ) -> None:
"""simple docstring"""
__lowercase : Any = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
__lowercase : Dict = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
__lowercase : int = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
__lowercase : List[Any] = numpy.random.rand(3 , 1 )
# Real output values provided.
__lowercase : Tuple = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
__lowercase : List[str] = numpy.zeros(output_array.shape )
def lowerCAmelCase ( self : Optional[Any] ) -> numpy.ndarray:
"""simple docstring"""
__lowercase : Dict = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
__lowercase : Optional[Any] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
__lowercase : Optional[int] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase ( self : int ) -> None:
"""simple docstring"""
__lowercase : Any = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
__lowercase : Any = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
__lowercase : str = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase ( self : Dict , __a : numpy.ndarray , __a : int , __a : bool ) -> None:
"""simple docstring"""
for iteration in range(1 , iterations + 1 ):
__lowercase : Union[str, Any] = self.feedforward()
self.back_propagation()
if give_loss:
__lowercase : List[str] = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F"Iteration {iteration} Loss: {loss}" )
def lowerCAmelCase ( self : Optional[Any] , __a : numpy.ndarray ) -> int:
"""simple docstring"""
__lowercase : Dict = input_arr
__lowercase : str = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
__lowercase : int = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
__lowercase : List[Any] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def snake_case_ ( lowerCAmelCase_ : Any ):
return 1 / (1 + numpy.exp(-value ))
def snake_case_ ( lowerCAmelCase_ : Tuple ):
return (value) * (1 - (value))
def snake_case_ ( ):
__lowercase : Dict = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
__lowercase : Optional[Any] = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
__lowercase : List[Any] = TwoHiddenLayerNeuralNetwork(
input_array=a__ , output_array=a__ )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=a__ , iterations=10 , give_loss=a__ )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 369 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : Optional[Any] = (DPMSolverSDEScheduler,)
_A : Dict = 10
def lowerCAmelCase ( self : Optional[int] , **__a : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase : Any = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**__a )
return config
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a )
def lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Optional[int] = self.scheduler_classes[0]
__lowercase : List[str] = self.get_scheduler_config()
__lowercase : Any = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[Any] = self.dummy_model()
__lowercase : str = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Optional[Any] = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Union[str, Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Optional[Any] = scheduler.step(__a , __a , __a )
__lowercase : str = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Union[str, Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config(prediction_type="""v_prediction""" )
__lowercase : int = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[int] = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Dict = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Dict = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[int] = model(__a , __a )
__lowercase : Optional[int] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : List[str] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config()
__lowercase : Optional[int] = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : int = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowercase : int = scheduler.scale_model_input(__a , __a )
__lowercase : List[str] = model(__a , __a )
__lowercase : List[str] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : List[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase : str = self.scheduler_classes[0]
__lowercase : List[Any] = self.get_scheduler_config()
__lowercase : Tuple = scheduler_class(**__a , use_karras_sigmas=__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : List[str] = self.dummy_model()
__lowercase : Optional[int] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
__lowercase : str = sample.to(__a )
for t in scheduler.timesteps:
__lowercase : List[Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Any = scheduler.step(__a , __a , __a )
__lowercase : Optional[Any] = output.prev_sample
__lowercase : Any = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 306 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCamelCase = 2048
_UpperCamelCase = 4096
_UpperCamelCase = 42
_UpperCamelCase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_UpperCamelCase = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def lowerCAmelCase__( lowercase : List[Any] ) -> Optional[Any]:
def choose_first(lowercase : Any , lowercase : Tuple=False ):
assert isinstance(lowercase , lowercase )
if len(lowercase ) == 1:
__snake_case : str = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__snake_case : List[Any] = {k: [a[k]] for k in a}
if len(a["start_token"] ) > 0:
break
return a
__snake_case : Union[str, Any] = {'''id''': example['''id''']}
__snake_case : Optional[Any] = example['''annotations''']
__snake_case : Tuple = annotation['''yes_no_answer''']
if 0 in yes_no_answer or 1 in yes_no_answer:
__snake_case : List[Any] = ['''yes'''] if 1 in yes_no_answer else ['''no''']
__snake_case : Tuple = []
__snake_case : List[Any] = []
__snake_case : Tuple = ['''<cls>''']
else:
__snake_case : Optional[Any] = ['''short''']
__snake_case : Union[str, Any] = choose_first(annotation["short_answers"] )
if len(out["start_token"] ) == 0:
# answer will be long if short is not available
__snake_case : int = ['''long''']
__snake_case : List[Any] = choose_first(annotation["long_answer"] , is_long_answer=lowercase )
__snake_case : str = []
answer.update(lowercase )
# disregard some samples
if len(answer["start_token"] ) > 1 or answer["start_token"] == answer["end_token"]:
__snake_case : Any = True
else:
__snake_case : str = False
__snake_case : Tuple = ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
if not all(isinstance(answer[k] , lowercase ) for k in cols ):
raise ValueError("Issue in ID" , example["id"] )
return answer
def lowerCAmelCase__( lowercase : str , lowercase : List[str]=False ) -> str:
__snake_case : Tuple = _get_single_answer(lowercase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__snake_case : str = example['''document''']['''tokens''']
__snake_case : Any = []
for i in range(len(doc["token"] ) ):
if not doc["is_html"][i]:
context.append(doc["token"][i] )
return {
"context": " ".join(lowercase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__snake_case : Optional[int] = ['''start_token''', '''end_token''']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__snake_case : Optional[Any] = example['''document''']['''tokens''']
__snake_case : List[Any] = answer['''start_token''']
__snake_case : Dict = answer['''end_token''']
__snake_case : Tuple = []
for i in range(len(doc["token"] ) ):
if not doc["is_html"][i]:
context.append(doc["token"][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__snake_case : Optional[int] = ''' '''.join(context[start_token:end_token] )
# checking above code
if assertion:
__snake_case : str = doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
__snake_case : Union[str, Any] = doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
__snake_case : Dict = ''' '''.join([old[i] for i in range(len(lowercase ) ) if not is_html[i]] )
if new != old:
print("ID:" , example["id"] )
print("New:" , lowercase , end="\n" )
print("Old:" , lowercase , end="\n\n" )
return {
"context": " ".join(lowercase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase__( lowercase : Optional[int] , lowercase : Union[str, Any] , lowercase : int=2048 , lowercase : int=4096 , lowercase : Tuple=True ) -> Dict:
__snake_case : Optional[Any] = get_context_and_ans(lowercase , assertion=lowercase )
__snake_case : List[str] = out['''answer''']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__snake_case : Optional[int] = tokenizer(example["question"]["text"] , out["context"] ).input_ids
__snake_case : Optional[int] = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__snake_case : int = []
__snake_case : Any = []
__snake_case : Tuple = input_ids[:q_len]
__snake_case : Any = range(lowercase , len(lowercase ) , max_length - doc_stride )
for i in doc_start_indices:
__snake_case : Tuple = i + max_length - q_len
__snake_case : Any = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["category"][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(lowercase ),
"end_token": [-100] * len(lowercase ),
"category": category,
},
}
__snake_case : Tuple = out['''context'''].split()
__snake_case : Optional[Any] = splitted_context[answer['''end_token''']]
__snake_case : List[str] = len(
tokenizer(
" ".join(splitted_context[: answer["start_token"]] ) , add_special_tokens=lowercase , ).input_ids )
__snake_case : Dict = len(
tokenizer(" ".join(splitted_context[: answer["end_token"]] ) , add_special_tokens=lowercase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__snake_case : Any = len(tokenizer(lowercase , add_special_tokens=lowercase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__snake_case : Optional[Any] = input_ids[answer['''start_token'''] : answer['''end_token'''] + 1] # right & left are inclusive
__snake_case : List[Any] = answer['''start_token''']
__snake_case : Optional[Any] = answer['''end_token''']
if assertion:
__snake_case : Optional[int] = tokenizer.decode(lowercase )
if answer["span"] != new:
print("ISSUE IN TOKENIZATION" )
print("OLD:" , answer["span"] )
print("NEW:" , lowercase , end="\n\n" )
if len(lowercase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__snake_case : List[str] = input_ids[:q_len]
__snake_case : Optional[Any] = range(lowercase , len(lowercase ) , max_length - doc_stride )
__snake_case : str = []
__snake_case : List[str] = []
__snake_case : str = []
__snake_case : List[Any] = [] # null, yes, no, long, short
for i in doc_start_indices:
__snake_case : str = i + max_length - q_len
__snake_case : str = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__snake_case : Tuple = start_token - i + q_len
__snake_case : List[str] = end_token - i + q_len
answers_category.append(answer["category"][0] ) # ["short"] -> "short"
else:
__snake_case : Any = -100
__snake_case : Union[str, Any] = -100
answers_category.append("null" )
__snake_case : str = inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowercase )
answers_end_token.append(lowercase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("ISSUE in strided for ID:" , example["id"] )
print("New:" , tokenizer.decode(lowercase ) )
print("Old:" , tokenizer.decode(lowercase ) , end="\n\n" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase__( lowercase : Dict , lowercase : Dict , lowercase : int=2048 , lowercase : Dict=4096 , lowercase : Any=False ) -> Optional[Any]:
__snake_case : Any = get_strided_contexts_and_ans(
lowercase , lowercase , doc_stride=lowercase , max_length=lowercase , assertion=lowercase , )
return example
def lowerCAmelCase__( lowercase : Any , lowercase : List[Any] ) -> Tuple:
with jsonlines.open(lowercase , "a" ) as writer:
for example in tqdm(lowercase , total=len(lowercase ) , desc="Saving samples ... " ):
__snake_case : Union[str, Any] = example['''labels''']
for ids, start, end, cat in zip(
example["input_ids"] , labels["start_token"] , labels["end_token"] , labels["category"] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"input_ids": ids,
"start_token": start,
"end_token": end,
"category": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
_UpperCamelCase = load_dataset('''natural_questions''')
_UpperCamelCase = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
_UpperCamelCase = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
_UpperCamelCase = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
_UpperCamelCase = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
_UpperCamelCase = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
_UpperCamelCase = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 326 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : int = StableUnCLIPImgaImgPipeline
a__ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
a__ : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a__ : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
a__ : int = frozenset([] )
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :Tuple = 32
__UpperCamelCase :Optional[int] = embedder_hidden_size
# image encoding components
__UpperCamelCase :Union[str, Any] = CLIPImageProcessor(crop_size=32 , size=32)
torch.manual_seed(0)
__UpperCamelCase :Union[str, Any] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__lowercase , projection_dim=__lowercase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ))
# regular denoising components
torch.manual_seed(0)
__UpperCamelCase :str = StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
__UpperCamelCase :Optional[int] = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')
torch.manual_seed(0)
__UpperCamelCase :Union[str, Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
torch.manual_seed(0)
__UpperCamelCase :Dict = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__lowercase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ))
torch.manual_seed(0)
__UpperCamelCase :List[Any] = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__lowercase , layers_per_block=1 , upcast_attention=__lowercase , use_linear_projection=__lowercase , )
torch.manual_seed(0)
__UpperCamelCase :Tuple = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type='''v_prediction''' , set_alpha_to_one=__lowercase , steps_offset=1 , )
torch.manual_seed(0)
__UpperCamelCase :List[str] = AutoencoderKL()
__UpperCamelCase :Tuple = {
# image encoding components
'''feature_extractor''': feature_extractor,
'''image_encoder''': image_encoder.eval(),
# image noising components
'''image_normalizer''': image_normalizer.eval(),
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder.eval(),
'''unet''': unet.eval(),
'''scheduler''': scheduler,
'''vae''': vae.eval(),
}
return components
def UpperCamelCase__ ( self , __lowercase , __lowercase=0 , __lowercase=True) -> str:
if str(__lowercase).startswith('''mps'''):
__UpperCamelCase :Union[str, Any] = torch.manual_seed(__lowercase)
else:
__UpperCamelCase :int = torch.Generator(device=__lowercase).manual_seed(__lowercase)
__UpperCamelCase :int = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)
if pil_image:
__UpperCamelCase :List[Any] = input_image * 0.5 + 0.5
__UpperCamelCase :Optional[Any] = input_image.clamp(0 , 1)
__UpperCamelCase :int = input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
__UpperCamelCase :Optional[Any] = DiffusionPipeline.numpy_to_pil(__lowercase)[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase :Tuple = self.get_dummy_components()
__UpperCamelCase :Any = StableUnCLIPImgaImgPipeline(**__lowercase)
__UpperCamelCase :Optional[Any] = sd_pipe.to(__lowercase)
sd_pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :List[Any] = self.get_dummy_inputs(__lowercase)
inputs.update({'''image_embeds''': None})
__UpperCamelCase :Any = sd_pipe(**__lowercase).images
__UpperCamelCase :List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__UpperCamelCase :List[Any] = np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Optional[Any] = torch_device in ['''cpu''', '''mps''']
self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Optional[Any] = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=__lowercase)
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def UpperCamelCase__ ( self) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')
__UpperCamelCase :Any = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')
__UpperCamelCase :List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa)
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__UpperCamelCase :int = torch.Generator(device='''cpu''').manual_seed(0)
__UpperCamelCase :Dict = pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')
__UpperCamelCase :Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')
__UpperCamelCase :Any = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')
__UpperCamelCase :Any = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__UpperCamelCase :int = torch.Generator(device='''cpu''').manual_seed(0)
__UpperCamelCase :Optional[int] = pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')
__UpperCamelCase :List[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__UpperCamelCase :List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
__UpperCamelCase :Union[str, Any] = pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__UpperCamelCase :Optional[Any] = pipe(
__lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )
__UpperCamelCase :int = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 43 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str]=5 ) -> Dict:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('''<mask>''' ) == 1
UpperCAmelCase_ = torch.tensor(tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) ).unsqueeze(0 ) # Batch size 1
UpperCAmelCase_ = model(__UpperCamelCase )[0] # The last hidden-state is the first element of the output tuple
UpperCAmelCase_ = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
UpperCAmelCase_ = logits[0, masked_index, :]
UpperCAmelCase_ = logits.softmax(dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ = prob.topk(k=__UpperCamelCase , dim=0 )
UpperCAmelCase_ = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__UpperCamelCase ) )] )
UpperCAmelCase_ = tokenizer.mask_token
UpperCAmelCase_ = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
UpperCAmelCase_ = predicted_token_bpe.replace('''\u2581''' , ''' ''' )
if " {0}".format(__UpperCamelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(__UpperCamelCase ) , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(__UpperCamelCase , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
_lowerCamelCase = CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase = CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()
_lowerCamelCase = 'Le camembert est <mask> :)'
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 177 |
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class a ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , __snake_case : int , __snake_case : List[Any]=7 , __snake_case : Any=3 , __snake_case : Any=18 , __snake_case : str=30 , __snake_case : Any=4_00 , __snake_case : Optional[int]=True , __snake_case : str=None , __snake_case : Any=True , __snake_case : List[Any]=None , ):
UpperCAmelCase_ = size if size is not None else {'''shortest_edge''': 20}
UpperCAmelCase_ = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
def lowerCamelCase_ ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class a ( _A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = MobileNetVaImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self : Any ):
UpperCAmelCase_ = MobileNetVaImageProcessingTester(self )
@property
def lowerCamelCase_ ( self : List[str] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : Optional[int] ):
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__snake_case , '''do_resize''' ) )
self.assertTrue(hasattr(__snake_case , '''size''' ) )
self.assertTrue(hasattr(__snake_case , '''do_center_crop''' ) )
self.assertTrue(hasattr(__snake_case , '''crop_size''' ) )
def lowerCamelCase_ ( self : Dict ):
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
UpperCAmelCase_ = 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 lowerCamelCase_ ( self : Optional[int] ):
pass
def lowerCamelCase_ ( self : Tuple ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , Image.Image )
# Test not batched input
UpperCAmelCase_ = 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
UpperCAmelCase_ = image_processing(__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 lowerCamelCase_ ( self : str ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , numpify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , np.ndarray )
# Test not batched input
UpperCAmelCase_ = 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
UpperCAmelCase_ = image_processing(__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 lowerCamelCase_ ( self : int ):
# Initialize image_processing
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__snake_case , torchify=__snake_case )
for image in image_inputs:
self.assertIsInstance(__snake_case , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = 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
UpperCAmelCase_ = image_processing(__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'''],
) , )
| 177 | 1 |
"""simple docstring"""
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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Dict , A : List[Any] , A : Tuple=7 , A : Dict=3 , A : Optional[Any]=18 , A : Union[str, Any]=30 , A : List[str]=4_00 , A : List[Any]=True , A : Union[str, Any]=32 , A : Any=True , ) -> List[Any]:
lowercase_ : Optional[Any] = parent
lowercase_ : Optional[int] = batch_size
lowercase_ : Any = num_channels
lowercase_ : List[str] = image_size
lowercase_ : Optional[int] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : str = do_resize
lowercase_ : Optional[Any] = size_divisor
lowercase_ : List[Any] = do_rescale
def A ( self : Dict ) -> Optional[int]:
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class _UpperCAmelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = GLPNImageProcessor if is_vision_available() else None
def A ( self : Union[str, Any] ) -> List[str]:
lowercase_ : Any = GLPNImageProcessingTester(self )
@property
def A ( self : int ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[str] ) -> int:
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size_divisor''' ) )
self.assertTrue(hasattr(A , '''resample''' ) )
self.assertTrue(hasattr(A , '''do_rescale''' ) )
def A ( self : Any ) -> str:
pass
def A ( self : Tuple ) -> Tuple:
# Initialize image_processing
lowercase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def A ( self : List[Any] ) -> Tuple:
# Initialize image_processing
lowercase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def A ( self : Optional[int] ) -> List[Any]:
# Initialize image_processing
lowercase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 33 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperCamelCase ):
A = False
return [i for i in range(2 , UpperCamelCase ) if is_prime[i]]
def A__ ( UpperCamelCase = 800_800 , UpperCamelCase = 800_800 ):
A = degree * loga(UpperCamelCase )
A = int(UpperCamelCase )
A = calculate_prime_numbers(UpperCamelCase )
A = 0
A = 0
A = len(UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 292 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Any:
# Load checkpoint
__lowerCAmelCase: Tuple = torch.load(__SCREAMING_SNAKE_CASE , map_location="cpu" )
__lowerCAmelCase: int = chkpt["model"]
# We have the base model one level deeper than the original XLM repository
__lowerCAmelCase: Optional[int] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
__lowerCAmelCase: Any = v
else:
__lowerCAmelCase: List[str] = v
__lowerCAmelCase: Optional[Any] = chkpt["params"]
__lowerCAmelCase: Union[str, Any] = {n: v for n, v in config.items() if not isinstance(__SCREAMING_SNAKE_CASE , (torch.FloatTensor, numpy.ndarray) )}
__lowerCAmelCase: Optional[Any] = chkpt["dico_word2id"]
__lowerCAmelCase: Optional[int] = {s + "</w>" if s.find("@@" ) == -1 and i > 1_3 else s.replace("@@" , "" ): i for s, i in vocab.items()}
# Save pytorch-model
__lowerCAmelCase: Union[str, Any] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
__lowerCAmelCase: List[Any] = pytorch_dump_folder_path + "/" + CONFIG_NAME
__lowerCAmelCase: Dict = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(__SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__SCREAMING_SNAKE_CASE , indent=2 ) + "\n" )
print(F"Save vocab file to {pytorch_config_dump_path}" )
with open(__SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__SCREAMING_SNAKE_CASE , indent=2 ) + "\n" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__A = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 367 |
"""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 a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Any:
# Load configuration defined in the metadata file
with open(__SCREAMING_SNAKE_CASE ) as metadata_file:
__lowerCAmelCase: List[Any] = json.load(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
__lowerCAmelCase: Optional[Any] = torch.load(__SCREAMING_SNAKE_CASE , map_location="cpu" )["module"]
# Load the entity vocab file
__lowerCAmelCase: List[Any] = load_original_entity_vocab(__SCREAMING_SNAKE_CASE )
# add an entry for [MASK2]
__lowerCAmelCase: Any = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
__lowerCAmelCase: Union[str, Any] = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
__lowerCAmelCase: str = AddedToken("<ent>" , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = AddedToken("<ent2>" , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE )
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(__SCREAMING_SNAKE_CASE )
with open(os.path.join(__SCREAMING_SNAKE_CASE , "tokenizer_config.json" ) , "r" ) as f:
__lowerCAmelCase: Optional[int] = json.load(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = "MLukeTokenizer"
with open(os.path.join(__SCREAMING_SNAKE_CASE , "tokenizer_config.json" ) , "w" ) as f:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with open(os.path.join(__SCREAMING_SNAKE_CASE , MLukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# Initialize the embeddings of the special tokens
__lowerCAmelCase: Union[str, Any] = tokenizer.convert_tokens_to_ids(["@"] )[0]
__lowerCAmelCase: Optional[int] = tokenizer.convert_tokens_to_ids(["#"] )[0]
__lowerCAmelCase: Dict = state_dict["embeddings.word_embeddings.weight"]
__lowerCAmelCase: Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
__lowerCAmelCase: int = word_emb[enta_init_index].unsqueeze(0 )
__lowerCAmelCase: 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"]:
__lowerCAmelCase: Dict = state_dict[bias_name]
__lowerCAmelCase: Union[str, Any] = decoder_bias[ent_init_index].unsqueeze(0 )
__lowerCAmelCase: Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
__lowerCAmelCase: Optional[int] = 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"]:
__lowerCAmelCase: Optional[int] = F"encoder.layer.{layer_index}.attention.self."
__lowerCAmelCase: Tuple = state_dict[prefix + matrix_name]
__lowerCAmelCase: Dict = state_dict[prefix + matrix_name]
__lowerCAmelCase: Optional[Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__lowerCAmelCase: int = state_dict["entity_embeddings.entity_embeddings.weight"]
__lowerCAmelCase: Dict = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
__lowerCAmelCase: str = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
__lowerCAmelCase: List[str] = state_dict["entity_predictions.bias"]
__lowerCAmelCase: Dict = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
__lowerCAmelCase: str = torch.cat([entity_prediction_bias, entity_mask_bias] )
__lowerCAmelCase: Optional[int] = LukeForMaskedLM(config=__SCREAMING_SNAKE_CASE ).eval()
state_dict.pop("entity_predictions.decoder.weight" )
state_dict.pop("lm_head.decoder.weight" )
state_dict.pop("lm_head.decoder.bias" )
__lowerCAmelCase: Tuple = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
__lowerCAmelCase: Any = state_dict[key]
else:
__lowerCAmelCase: Tuple = state_dict[key]
__lowerCAmelCase , __lowerCAmelCase: Tuple = model.load_state_dict(__SCREAMING_SNAKE_CASE , strict=__SCREAMING_SNAKE_CASE )
if set(__SCREAMING_SNAKE_CASE ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}" )
if set(__SCREAMING_SNAKE_CASE ) != {
"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
__lowerCAmelCase: Tuple = MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE , task="entity_classification" )
__lowerCAmelCase: Tuple = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
__lowerCAmelCase: Optional[Any] = (0, 9)
__lowerCAmelCase: Optional[int] = tokenizer(__SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors="pt" )
__lowerCAmelCase: int = model(**__SCREAMING_SNAKE_CASE )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCAmelCase: Dict = torch.Size((1, 3_3, 7_6_8) )
__lowerCAmelCase: Optional[int] = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
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] , __SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCAmelCase: Union[str, Any] = torch.Size((1, 1, 7_6_8) )
__lowerCAmelCase: Any = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
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] , __SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
__lowerCAmelCase: Tuple = MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = "Tokyo is the capital of <mask>."
__lowerCAmelCase: List[str] = (2_4, 3_0)
__lowerCAmelCase: int = tokenizer(__SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors="pt" )
__lowerCAmelCase: Union[str, Any] = model(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = encoding["input_ids"][0].tolist()
__lowerCAmelCase: int = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
__lowerCAmelCase: Optional[Any] = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = outputs.entity_logits[0][0].argmax().item()
__lowerCAmelCase: Union[str, 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(__SCREAMING_SNAKE_CASE ) )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
def a__ ( __SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase: Tuple = ["[MASK]", "[PAD]", "[UNK]"]
__lowerCAmelCase: Optional[Any] = [json.loads(__SCREAMING_SNAKE_CASE ) for line in open(__SCREAMING_SNAKE_CASE )]
__lowerCAmelCase: str = {}
for entry in data:
__lowerCAmelCase: Tuple = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
__lowerCAmelCase: Optional[int] = entity_id
break
__lowerCAmelCase: Optional[Any] = F"{language}:{entity_name}"
__lowerCAmelCase: Optional[int] = entity_id
return new_mapping
if __name__ == "__main__":
__A = 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."
)
__A = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 108 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
UpperCAmelCase__ = pytest.mark.integration
@require_faiss
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Tuple ) ->Any:
"""simple docstring"""
a = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__UpperCAmelCase ) for x in np.arange(30 ).tolist()]} )
return dset
def __lowerCAmelCase ( self : Tuple ) ->Any:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
a = dset.map(
lambda __UpperCAmelCase , __UpperCAmelCase : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__UpperCAmelCase , keep_in_memory=__UpperCAmelCase )
a = dset.add_faiss_index('''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
a , a = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
dset.drop_index('''vecs''' )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
a , a = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase ) as tmp_file:
dset.save_faiss_index('''vecs''' , tmp_file.name )
dset.load_faiss_index('''vecs2''' , tmp_file.name )
os.unlink(tmp_file.name )
a , a = dset.get_nearest_examples('''vecs2''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(__UpperCAmelCase , partial(dset.get_nearest_examples , '''vecs2''' , np.ones(5 , dtype=np.floataa ) ) )
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
from elasticsearch import Elasticsearch
a = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 30 )
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
a = Elasticsearch()
dset.add_elasticsearch_index('''filename''' , es_client=__UpperCAmelCase )
a , a = dset.get_nearest_examples('''filename''' , '''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
@require_faiss
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) ->Any:
"""simple docstring"""
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(__UpperCAmelCase )
self.assertRaises(__UpperCAmelCase , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
a = np.eye(5 , dtype=np.floataa )[::-1]
a , a = index.search_batch(__UpperCAmelCase )
self.assertRaises(__UpperCAmelCase , index.search_batch , queries[0] )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __UpperCAmelCase )
def __lowerCAmelCase ( self : Any ) ->List[Any]:
"""simple docstring"""
import faiss
a = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
a = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__UpperCAmelCase ):
a = FaissIndex(string_factory='''Flat''' , custom_index=faiss.IndexFlat(5 ) )
def __lowerCAmelCase ( self : int ) ->Optional[Any]:
"""simple docstring"""
import faiss
a = faiss.IndexFlat(5 )
a = FaissIndex(custom_index=__UpperCAmelCase )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase ) as tmp_file:
index.save(tmp_file.name )
a = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(__UpperCAmelCase )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _a ( a :Dict ) -> Any:
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
a = '''index.faiss'''
a = F"""mock://{index_name}"""
index.save(a , storage_options=mockfs.storage_options )
a = FaissIndex.load(a , storage_options=mockfs.storage_options )
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(a )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a = Elasticsearch()
a = {'''acknowledged''': True}
a = ElasticSearchIndex(es_client=__UpperCAmelCase )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
a = '''foo'''
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a = index.search(__UpperCAmelCase )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
a = '''foo'''
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a = index.search(__UpperCAmelCase , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
a = ['''foo''', '''bar''', '''foobar''']
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a = index.search_batch(__UpperCAmelCase )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __UpperCAmelCase )
# batched queries with timeout
a = ['''foo''', '''bar''', '''foobar''']
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a = index.search_batch(__UpperCAmelCase , request_timeout=30 )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __UpperCAmelCase )
| 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a :str = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n"
a :List[Any] = "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"
a :int = r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n >>> metric = datasets.load_metric(\"competition_math\")\n >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n >>> print(results)\n {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __a (datasets.Metric):
'''simple docstring'''
def _a ( self ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/hendrycks/math""" , codebase_urls=["""https://github.com/hendrycks/math"""] , )
def _a ( self , _a , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.0
for i, j in zip(_a , _a ):
n_correct += 1.0 if math_equivalence.is_equiv(_a , _a ) else 0.0
SCREAMING_SNAKE_CASE__ : List[str] = n_correct / len(_a )
return {
"accuracy": accuracy,
}
| 132 | 0 |
"""simple docstring"""
import re
def _lowerCamelCase( lowercase__ ) -> list:
'''simple docstring'''
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def _lowerCamelCase( lowercase__ ) -> str:
'''simple docstring'''
__lowercase= split_input(str_ )
return "".join(
[''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
try:
__lowercase= split_input(lowercase__ )
if upper:
__lowercase= ''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__lowercase= ''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def _lowerCamelCase( lowercase__ ) -> str:
'''simple docstring'''
return to_simple_case(lowercase__ )
def _lowerCamelCase( lowercase__ ) -> str:
'''simple docstring'''
try:
__lowercase= to_simple_case(lowercase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def _lowerCamelCase( lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
return to_complex_case(lowercase__ , lowercase__ , '_' )
def _lowerCamelCase( lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
return to_complex_case(lowercase__ , lowercase__ , '-' )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 367 |
from __future__ import annotations
import numpy as np
def _lowerCamelCase( lowercase__ ) -> str:
'''simple docstring'''
return np.maximum(0 , lowercase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 304 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
),
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'roberta-prelayernorm'
def __init__( self , lowercase=50265 , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3072 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = initializer_range
A__ = layer_norm_eps
A__ = position_embedding_type
A__ = use_cache
A__ = classifier_dropout
class a__ ( snake_case ):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
A__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 68 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 1 |
def A ( lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase = _modexpt(lowercase , exponent // 2 , lowercase ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(lowercase , exponent - 1 , lowercase )) % modulo_value
def A ( lowercase = 1_777 , lowercase = 1_855 , lowercase = 8 ) -> int:
'''simple docstring'''
UpperCamelCase = base
for _ in range(1 , lowercase ):
UpperCamelCase = _modexpt(lowercase , lowercase , 10**digits )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 110 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class lowercase ( unittest.TestCase ):
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = tempfile.mkdtemp()
# fmt: off
UpperCamelCase = ['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
UpperCamelCase = dict(zip(A_ , range(len(A_ ) ) ) )
UpperCamelCase = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
UpperCamelCase = {'unk_token': '<unk>'}
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCamelCase = 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_ ) )
UpperCamelCase = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
UpperCamelCase = os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(A_ , A_ )
def __UpperCamelCase ( self , **A_ ) -> Union[str, Any]:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token='!' , **A_ )
def __UpperCamelCase ( self , **A_ ) -> Tuple:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token='!' , **A_ )
def __UpperCamelCase ( self , **A_ ) -> Union[str, Any]:
"""simple docstring"""
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = self.get_image_processor()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
UpperCamelCase = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
UpperCamelCase = OwlViTProcessor.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 , A_ )
self.assertIsInstance(processor_fast.tokenizer , A_ )
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 , A_ )
self.assertIsInstance(processor_fast.image_processor , A_ )
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
UpperCamelCase = self.get_image_processor(do_normalize=A_ )
UpperCamelCase = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=A_ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(A_ , return_tensors='np' )
UpperCamelCase = processor(images=A_ , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
UpperCamelCase = 'lower newer'
UpperCamelCase = processor(text=A_ , return_tensors='np' )
UpperCamelCase = tokenizer(A_ , return_tensors='np' )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
UpperCamelCase = 'lower newer'
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = 'google/owlvit-base-patch32'
UpperCamelCase = OwlViTProcessor.from_pretrained(A_ )
UpperCamelCase = ['cat', 'nasa badge']
UpperCamelCase = processor(text=A_ )
UpperCamelCase = 16
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask'] )
self.assertEqual(inputs['input_ids'].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = 'google/owlvit-base-patch32'
UpperCamelCase = OwlViTProcessor.from_pretrained(A_ )
UpperCamelCase = [['cat', 'nasa badge'], ['person']]
UpperCamelCase = processor(text=A_ )
UpperCamelCase = 16
UpperCamelCase = len(A_ )
UpperCamelCase = max([len(A_ ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask'] )
self.assertEqual(inputs['input_ids'].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = 'google/owlvit-base-patch32'
UpperCamelCase = OwlViTProcessor.from_pretrained(A_ )
UpperCamelCase = ['cat', 'nasa badge']
UpperCamelCase = processor(text=A_ )
UpperCamelCase = 16
UpperCamelCase = inputs['input_ids']
UpperCamelCase = [
[49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask'] )
self.assertEqual(inputs['input_ids'].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(images=A_ , query_images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['query_pixel_values', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = OwlViTProcessor(tokenizer=A_ , image_processor=A_ )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(A_ )
UpperCamelCase = tokenizer.batch_decode(A_ )
self.assertListEqual(A_ , A_ )
| 110 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
_snake_case = "2020.9.26"
_snake_case = "xcodz-dot, cclaus, dhruvmanila"
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if not all(isinstance(_UpperCamelCase , (float, int) ) for val in locals().values() ):
_a : Optional[Any] = F"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(_UpperCamelCase )
_a : Union[str, Any] = ((x * distance) / (z + distance)) * scale
_a : List[Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("""Axis must be a str""" )
_a : Tuple = locals()
del input_variables["axis"]
if not all(isinstance(_UpperCamelCase , (float, int) ) for val in input_variables.values() ):
_a : Tuple = (
"""Input values except axis must either be float or int: """
F"""{list(input_variables.values() )}"""
)
raise TypeError(_UpperCamelCase )
_a : Optional[int] = (angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
if axis == "z":
_a : int = x * math.cos(_UpperCamelCase ) - y * math.sin(_UpperCamelCase )
_a : Union[str, Any] = y * math.cos(_UpperCamelCase ) + x * math.sin(_UpperCamelCase )
_a : str = z
elif axis == "x":
_a : Optional[int] = y * math.cos(_UpperCamelCase ) - z * math.sin(_UpperCamelCase )
_a : Union[str, Any] = z * math.cos(_UpperCamelCase ) + y * math.sin(_UpperCamelCase )
_a : Optional[int] = x
elif axis == "y":
_a : Union[str, Any] = x * math.cos(_UpperCamelCase ) - z * math.sin(_UpperCamelCase )
_a : Tuple = z * math.cos(_UpperCamelCase ) + x * math.sin(_UpperCamelCase )
_a : int = y
else:
raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'""" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(F'''{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }''')
| 294 |
"""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()
A : Tuple = logging.get_logger(__name__)
A : Tuple = [
("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"),
]
A : Optional[Any] = [
"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 ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location="cpu" )
return sd
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=rename_keys_prefix ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict()
__lowerCAmelCase = 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
__lowerCAmelCase = key
for name_pair in rename_keys_prefix:
__lowerCAmelCase = new_key.replace(name_pair[0] , name_pair[1] )
__lowerCAmelCase = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__lowerCAmelCase = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''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:
__lowerCAmelCase = "pretraining"
if "vcr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(f"No implementation found for `{checkpoint_path}`." )
else:
if "vcr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 512}
__lowerCAmelCase = "multichoice"
elif "vqa_advanced" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
__lowerCAmelCase = "vqa_advanced"
elif "vqa" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048, "num_labels": 3129}
__lowerCAmelCase = "vqa"
elif "nlvr" in checkpoint_path:
__lowerCAmelCase = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
__lowerCAmelCase = "nlvr"
__lowerCAmelCase = VisualBertConfig(**_UpperCamelCase )
# Load State Dict
__lowerCAmelCase = load_state_dict(_UpperCamelCase )
__lowerCAmelCase = get_new_dict(_UpperCamelCase , _UpperCamelCase )
if model_type == "pretraining":
__lowerCAmelCase = VisualBertForPreTraining(_UpperCamelCase )
elif model_type == "vqa":
__lowerCAmelCase = VisualBertForQuestionAnswering(_UpperCamelCase )
elif model_type == "nlvr":
__lowerCAmelCase = VisualBertForVisualReasoning(_UpperCamelCase )
elif model_type == "multichoice":
__lowerCAmelCase = VisualBertForMultipleChoice(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
# Save Checkpoints
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
A : Union[str, Any] = 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.")
A : Optional[int] = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 57 | 0 |
'''simple docstring'''
class A :
def __init__( self : List[Any] , lowerCAmelCase_ : list[int] ) -> None:
"""simple docstring"""
_a = len(lowerCAmelCase_ )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , lowerCAmelCase_ ):
_a = self.prefix_sum[i - 1] + array[i]
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : int ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : int ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(lowerCAmelCase_ )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 179 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class A ( _a ):
def __init__( self : str , *lowerCAmelCase_ : int , **lowerCAmelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
super().__init__(*lowerCAmelCase_ , **lowerCAmelCase_ )
_a = {}
def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Tuple , *lowerCAmelCase_ : Any , **lowerCAmelCase_ : Tuple ) -> str:
"""simple docstring"""
_a = super().add_tokens(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
''' `placeholder_token` that is not already in the tokenizer.''' )
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict , *lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple=1 , **lowerCAmelCase_ : int ) -> Any:
"""simple docstring"""
_a = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ )
output.append(lowerCAmelCase_ )
else:
_a = []
for i in range(lowerCAmelCase_ ):
_a = placeholder_token + F'_{i}'
self.try_adding_tokens(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ )
output.append(lowerCAmelCase_ )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
_a = output
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Optional[int]=1.0 ) -> Tuple:
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_a = []
for i in range(len(lowerCAmelCase_ ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowerCAmelCase_ ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
_a = self.token_map[placeholder_token]
_a = tokens[: 1 + int(len(lowerCAmelCase_ ) * prop_tokens_to_load )]
if vector_shuffle:
_a = copy.copy(lowerCAmelCase_ )
random.shuffle(lowerCAmelCase_ )
_a = text.replace(lowerCAmelCase_ , ''' '''.join(lowerCAmelCase_ ) )
return text
def __call__( self : List[str] , lowerCAmelCase_ : str , *lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int]=False , lowerCAmelCase_ : Union[str, Any]=1.0 , **lowerCAmelCase_ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowerCAmelCase_ , vector_shuffle=lowerCAmelCase_ , prop_tokens_to_load=lowerCAmelCase_ ) , *lowerCAmelCase_ , **lowerCAmelCase_ , )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : int , *lowerCAmelCase_ : str , lowerCAmelCase_ : Any=False , lowerCAmelCase_ : List[Any]=1.0 , **lowerCAmelCase_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return super().encode(
self.replace_placeholder_tokens_in_text(
lowerCAmelCase_ , vector_shuffle=lowerCAmelCase_ , prop_tokens_to_load=lowerCAmelCase_ ) , *lowerCAmelCase_ , **lowerCAmelCase_ , )
| 179 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'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
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase ( a_ , a_ ) -> tuple:
"""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()
| 344 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''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''',
'''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
'''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''',
'''label_embs_concat''': '''label_embeddings_concat''',
'''mask_emb''': '''masked_spec_embed''',
'''spk_proj''': '''speaker_proj''',
}
__lowerCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> Dict:
for attribute in key.split('.' ):
lowercase__: Tuple = getattr(snake_case , snake_case )
if weight_type is not None:
lowercase__: int = getattr(snake_case , snake_case ).shape
else:
lowercase__: int = 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":
lowercase__: int = value
elif weight_type == "weight_g":
lowercase__: List[Any] = value
elif weight_type == "weight_v":
lowercase__: List[Any] = value
elif weight_type == "bias":
lowercase__: Optional[int] = value
else:
lowercase__: Optional[Any] = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def snake_case_ ( snake_case , snake_case ) -> Tuple:
lowercase__: str = []
lowercase__: Union[str, Any] = fairseq_model.state_dict()
lowercase__: Optional[Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__: Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
snake_case , snake_case , snake_case , snake_case , hf_model.config.feat_extract_norm == 'group' , )
lowercase__: Dict = True
else:
for key, mapped_key in MAPPING.items():
lowercase__: List[str] = 'unispeech_sat.' + 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]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__: int = True
if "*" in mapped_key:
lowercase__: List[Any] = name.split(snake_case )[0].split('.' )[-2]
lowercase__: List[str] = mapped_key.replace('*' , snake_case )
if "weight_g" in name:
lowercase__: Optional[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__: Optional[int] = 'weight_v'
elif "bias" in name:
lowercase__: Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__: List[str] = 'weight'
else:
lowercase__: List[Any] = None
set_recursively(snake_case , snake_case , snake_case , snake_case , snake_case )
continue
if not is_used:
unused_weights.append(snake_case )
logger.warning(f'Unused weights: {unused_weights}' )
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> Union[str, Any]:
lowercase__: Union[str, Any] = full_name.split('conv_layers.' )[-1]
lowercase__: List[str] = name.split('.' )
lowercase__: int = int(items[0] )
lowercase__: str = 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.' )
lowercase__: List[str] = 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.' )
lowercase__: 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[layer_id].layer_norm.bias.data.shape} was found.' )
lowercase__: Tuple = 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[layer_id].layer_norm.weight.data.shape} was found.' )
lowercase__: List[str] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case )
@torch.no_grad()
def snake_case_ ( snake_case , snake_case , snake_case=None , snake_case=None , snake_case=True ) -> List[Any]:
if config_path is not None:
lowercase__: List[Any] = UniSpeechSatConfig.from_pretrained(snake_case )
else:
lowercase__: Dict = UniSpeechSatConfig()
lowercase__: Any = ''
if is_finetuned:
lowercase__: Tuple = UniSpeechSatForCTC(snake_case )
else:
lowercase__: Optional[int] = UniSpeechSatForPreTraining(snake_case )
lowercase__ , lowercase__ , lowercase__: str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__: List[Any] = model[0].eval()
recursively_load_weights(snake_case , snake_case )
hf_wavavec.save_pretrained(snake_case )
if __name__ == "__main__":
__lowerCAmelCase = 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'''
)
__lowerCAmelCase = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 288 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCAmelCase = logging.getLogger(__name__)
def snake_case_ ( snake_case , snake_case ) -> Optional[int]:
lowercase__: Optional[int] = np.argmax(snake_case , axis=1 )
return np.sum(outputs == labels )
def snake_case_ ( snake_case ) -> Dict:
with open(snake_case , encoding='utf_8' ) as f:
lowercase__: str = csv.reader(snake_case )
lowercase__: int = []
next(snake_case ) # skip the first line
for line in tqdm(snake_case ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
lowercase__: List[Any] = []
for dataset in encoded_datasets:
lowercase__: Dict = len(snake_case )
lowercase__: int = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowercase__: int = np.zeros((n_batch, 2) , dtype=np.intaa )
lowercase__: Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-1_00 , dtype=np.intaa )
lowercase__: Optional[Any] = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(snake_case ):
lowercase__: List[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowercase__: List[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowercase__: Union[str, Any] = with_conta
lowercase__: List[Any] = with_conta
lowercase__: Any = len(snake_case ) - 1
lowercase__: Dict = len(snake_case ) - 1
lowercase__: Optional[Any] = with_conta
lowercase__: Tuple = with_conta
lowercase__: int = mc_label
lowercase__: Any = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(snake_case ) for t in all_inputs ) )
return tensor_datasets
def snake_case_ ( ) -> Union[str, Any]:
lowercase__: Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=snake_case , default='openai-gpt' , help='pretrained model name' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir' , default=snake_case , type=snake_case , required=snake_case , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=snake_case , default='' )
parser.add_argument('--eval_dataset' , type=snake_case , default='' )
parser.add_argument('--seed' , type=snake_case , default=42 )
parser.add_argument('--num_train_epochs' , type=snake_case , default=3 )
parser.add_argument('--train_batch_size' , type=snake_case , default=8 )
parser.add_argument('--eval_batch_size' , type=snake_case , default=16 )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , type=snake_case , default=1 )
parser.add_argument(
'--max_steps' , default=-1 , type=snake_case , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=snake_case , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=snake_case , default=6.25e-5 )
parser.add_argument('--warmup_steps' , default=0 , type=snake_case , help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule' , type=snake_case , default='warmup_linear' )
parser.add_argument('--weight_decay' , type=snake_case , default=0.0_1 )
parser.add_argument('--lm_coef' , type=snake_case , default=0.9 )
parser.add_argument('--n_valid' , type=snake_case , default=3_74 )
parser.add_argument('--server_ip' , type=snake_case , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=snake_case , default='' , help='Can be used for distant debugging.' )
lowercase__: List[str] = parser.parse_args()
print(snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowercase__: Any = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
lowercase__: Tuple = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(snake_case , snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowercase__: Any = ['_start_', '_delimiter_', '_classify_']
lowercase__: Any = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(snake_case )
lowercase__: int = tokenizer.convert_tokens_to_ids(snake_case )
lowercase__: int = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(snake_case ) )
model.to(snake_case )
# Load and encode the datasets
def tokenize_and_encode(snake_case ):
if isinstance(snake_case , snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case ) )
elif isinstance(snake_case , snake_case ):
return obj
return [tokenize_and_encode(snake_case ) for o in obj]
logger.info('Encoding dataset...' )
lowercase__: Dict = load_rocstories_dataset(args.train_dataset )
lowercase__: Dict = load_rocstories_dataset(args.eval_dataset )
lowercase__: str = (train_dataset, eval_dataset)
lowercase__: Any = tokenize_and_encode(snake_case )
# Compute the max input length for the Transformer
lowercase__: Optional[Any] = model.config.n_positions // 2 - 2
lowercase__: Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowercase__: List[str] = min(snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowercase__: str = pre_process_datasets(snake_case , snake_case , snake_case , *snake_case )
lowercase__ , lowercase__: Optional[Any] = tensor_datasets[0], tensor_datasets[1]
lowercase__: List[str] = TensorDataset(*snake_case )
lowercase__: Dict = RandomSampler(snake_case )
lowercase__: Optional[int] = DataLoader(snake_case , sampler=snake_case , batch_size=args.train_batch_size )
lowercase__: str = TensorDataset(*snake_case )
lowercase__: str = SequentialSampler(snake_case )
lowercase__: Optional[Any] = DataLoader(snake_case , sampler=snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowercase__: Union[str, Any] = args.max_steps
lowercase__: Tuple = args.max_steps // (len(snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowercase__: Optional[Any] = len(snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowercase__: str = list(model.named_parameters() )
lowercase__: Any = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
lowercase__: str = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
lowercase__: Tuple = AdamW(snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowercase__: Tuple = get_linear_schedule_with_warmup(
snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=snake_case )
if args.do_train:
lowercase__ , lowercase__ , lowercase__: int = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='Epoch' ):
lowercase__: str = 0
lowercase__: Optional[Any] = 0
lowercase__: List[Any] = tqdm(snake_case , desc='Training' )
for step, batch in enumerate(snake_case ):
lowercase__: Union[str, Any] = tuple(t.to(snake_case ) for t in batch )
lowercase__ , lowercase__ , lowercase__ , lowercase__: List[Any] = batch
lowercase__: List[str] = model(snake_case , mc_token_ids=snake_case , lm_labels=snake_case , mc_labels=snake_case )
lowercase__: Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowercase__: Union[str, Any] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowercase__: Tuple = 'Training loss: {:.2e} lr: {:.2e}'.format(snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowercase__: Any = model.module if hasattr(snake_case , 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowercase__: Tuple = os.path.join(args.output_dir , snake_case )
lowercase__: List[str] = os.path.join(args.output_dir , snake_case )
torch.save(model_to_save.state_dict() , snake_case )
model_to_save.config.to_json_file(snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowercase__: Optional[Any] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowercase__: Any = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(snake_case )
if args.do_eval:
model.eval()
lowercase__ , lowercase__: Optional[Any] = 0, 0
lowercase__ , lowercase__: List[Any] = 0, 0
for batch in tqdm(snake_case , desc='Evaluating' ):
lowercase__: str = tuple(t.to(snake_case ) for t in batch )
lowercase__ , lowercase__ , lowercase__ , lowercase__: Union[str, Any] = batch
with torch.no_grad():
lowercase__ , lowercase__ , lowercase__ , lowercase__: Any = model(
snake_case , mc_token_ids=snake_case , lm_labels=snake_case , mc_labels=snake_case )
lowercase__: Dict = mc_logits.detach().cpu().numpy()
lowercase__: Tuple = mc_labels.to('cpu' ).numpy()
lowercase__: Dict = accuracy(snake_case , snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowercase__: Optional[int] = eval_loss / nb_eval_steps
lowercase__: Optional[int] = eval_accuracy / nb_eval_examples
lowercase__: int = tr_loss / nb_tr_steps if args.do_train else None
lowercase__: Optional[Any] = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
lowercase__: Dict = os.path.join(args.output_dir , 'eval_results.txt' )
with open(snake_case , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , snake_case , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 288 | 1 |
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 ..auto import CONFIG_MAPPING
__snake_case :Optional[int] = logging.get_logger(__name__)
__snake_case :Optional[Any] = {
'''microsoft/table-transformer-detection''': (
'''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
),
}
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = '''table-transformer'''
UpperCamelCase__ : Tuple = ['''past_key_values''']
UpperCamelCase__ : List[str] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : Tuple=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=100 , __SCREAMING_SNAKE_CASE : Tuple=6 , __SCREAMING_SNAKE_CASE : Optional[int]=2_048 , __SCREAMING_SNAKE_CASE : Any=8 , __SCREAMING_SNAKE_CASE : Union[str, Any]=6 , __SCREAMING_SNAKE_CASE : str=2_048 , __SCREAMING_SNAKE_CASE : Tuple=8 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=0.0 , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Dict="relu" , __SCREAMING_SNAKE_CASE : int=256 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.02 , __SCREAMING_SNAKE_CASE : Any=1.0 , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : Dict="sine" , __SCREAMING_SNAKE_CASE : str="resnet50" , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : Dict=5 , __SCREAMING_SNAKE_CASE : str=2 , __SCREAMING_SNAKE_CASE : List[Any]=1 , __SCREAMING_SNAKE_CASE : List[Any]=1 , __SCREAMING_SNAKE_CASE : Optional[int]=5 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.1 , **__SCREAMING_SNAKE_CASE : str , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
__a = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = backbone_config.get('''model_type''')
__a = CONFIG_MAPPING[backbone_model_type]
__a = config_class.from_dict(__SCREAMING_SNAKE_CASE)
# set timm attributes to None
__a , __a , __a = None, None, None
__a = use_timm_backbone
__a = backbone_config
__a = num_channels
__a = num_queries
__a = d_model
__a = encoder_ffn_dim
__a = encoder_layers
__a = encoder_attention_heads
__a = decoder_ffn_dim
__a = decoder_layers
__a = decoder_attention_heads
__a = dropout
__a = attention_dropout
__a = activation_dropout
__a = activation_function
__a = init_std
__a = init_xavier_std
__a = encoder_layerdrop
__a = decoder_layerdrop
__a = encoder_layers
__a = auxiliary_loss
__a = position_embedding_type
__a = backbone
__a = use_pretrained_backbone
__a = dilation
# Hungarian matcher
__a = class_cost
__a = bbox_cost
__a = giou_cost
# Loss coefficients
__a = mask_loss_coefficient
__a = dice_loss_coefficient
__a = bbox_loss_coefficient
__a = giou_loss_coefficient
__a = eos_coefficient
super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
@property
def _lowerCamelCase ( self : int):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _lowerCamelCase ( self : int):
'''simple docstring'''
return self.d_model
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Union[str, Any] = version.parse('''1.11''' )
@property
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
return 1E-5
@property
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return 12
| 49 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : str = R'''\w+[.]\d+'''
UpperCAmelCase__ : List[Any] = re.findall(lowerCAmelCase__ , lowerCAmelCase__ )
for pat in pats:
UpperCAmelCase__ : Union[str, Any] = key.replace(lowerCAmelCase__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCAmelCase__ : Optional[int] = pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCAmelCase__ : str = pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCAmelCase__ : List[Any] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase__ : int = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
UpperCAmelCase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase__ : Optional[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=42 ) -> Tuple:
# Step 1: Convert pytorch tensor to numpy
UpperCAmelCase__ : int = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCAmelCase__ : Tuple = flax_model.init_weights(PRNGKey(lowerCAmelCase__ ) )
UpperCAmelCase__ : Optional[Any] = flatten_dict(lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase__ : Optional[int] = rename_key(lowerCAmelCase__ )
UpperCAmelCase__ : str = tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = rename_key_and_reshape_tensor(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
UpperCAmelCase__ : List[str] = jnp.asarray(lowerCAmelCase__ )
return unflatten_dict(lowerCAmelCase__ )
| 181 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = model.generate(__lowerCAmelCase ,max_new_tokens=10 ,do_sample=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
SCREAMING_SNAKE_CASE = TextStreamer(__lowerCAmelCase )
model.generate(__lowerCAmelCase ,max_new_tokens=10 ,do_sample=__lowerCAmelCase ,streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
SCREAMING_SNAKE_CASE = cs.out[:-1]
self.assertEqual(__lowerCAmelCase ,__lowerCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = model.generate(__lowerCAmelCase ,max_new_tokens=10 ,do_sample=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(greedy_ids[0] )
SCREAMING_SNAKE_CASE = TextIteratorStreamer(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
SCREAMING_SNAKE_CASE = Thread(target=model.generate ,kwargs=__lowerCAmelCase )
thread.start()
SCREAMING_SNAKE_CASE = """"""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(__lowerCAmelCase ,__lowerCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = model.generate(__lowerCAmelCase ,max_new_tokens=10 ,do_sample=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = greedy_ids[:, input_ids.shape[1] :]
SCREAMING_SNAKE_CASE = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
SCREAMING_SNAKE_CASE = TextStreamer(__lowerCAmelCase ,skip_prompt=__lowerCAmelCase )
model.generate(__lowerCAmelCase ,max_new_tokens=10 ,do_sample=__lowerCAmelCase ,streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
SCREAMING_SNAKE_CASE = cs.out[:-1]
self.assertEqual(__lowerCAmelCase ,__lowerCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""distilgpt2""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained("""distilgpt2""" ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = torch.ones((1, 5) ,device=__lowerCAmelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
SCREAMING_SNAKE_CASE = TextStreamer(__lowerCAmelCase ,skip_special_tokens=__lowerCAmelCase )
model.generate(__lowerCAmelCase ,max_new_tokens=1 ,do_sample=__lowerCAmelCase ,streamer=__lowerCAmelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
SCREAMING_SNAKE_CASE = cs.out[:-1] # Remove the final "\n"
SCREAMING_SNAKE_CASE = tokenizer(__lowerCAmelCase ,return_tensors="""pt""" )
self.assertEqual(streamer_text_tokenized.input_ids.shape ,(1, 1) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = TextIteratorStreamer(__lowerCAmelCase ,timeout=0.001 )
SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
SCREAMING_SNAKE_CASE = Thread(target=model.generate ,kwargs=__lowerCAmelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE = """"""
for new_text in streamer:
streamer_text += new_text
| 371 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = ComputeEnvironment.AMAZON_SAGEMAKER
__snake_case : List[Any] = True
__snake_case : Optional[int] = "ml.p3.2xlarge"
__snake_case : List[str] = "accelerate_sagemaker_execution_role"
__snake_case : Tuple = "hf-sm"
__snake_case : Any = "us-east-1"
__snake_case : Union[str, Any] = 1
__snake_case : Dict = "accelerate-sagemaker-1"
__snake_case : Tuple = "1.6"
__snake_case : List[str] = "4.4"
__snake_case : str = "train.py"
__snake_case : List[str] = [
"--model_name_or_path",
"bert",
"--do_train",
"False",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
__snake_case : Optional[int] = [
"--model_name_or_path",
"bert",
"--do_train",
"--do_test",
"False",
"--do_predict",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args["""model_name_or_path"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""do_train"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""epochs"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""learning_rate"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""max_steps"""] ,lowerCamelCase__ )
with pytest.raises(lowerCamelCase__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 193 | 0 |
from heapq import heappop, heappush
import numpy as np
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : bool , ):
__UpperCamelCase , __UpperCamelCase =grid.shape
__UpperCamelCase =[-1, 1, 0, 0]
__UpperCamelCase =[0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__UpperCamelCase , __UpperCamelCase =[(0, source)], set()
__UpperCamelCase =np.full((rows, cols) , np.inf )
__UpperCamelCase =0
__UpperCamelCase =np.empty((rows, cols) , dtype=SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =None
while queue:
((__UpperCamelCase) , (__UpperCamelCase)) =heappop(SCREAMING_SNAKE_CASE__ )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__UpperCamelCase =[]
while (x, y) != source:
path.append((x, y) )
__UpperCamelCase , __UpperCamelCase =predecessors[x, y]
path.append(SCREAMING_SNAKE_CASE__ ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
__UpperCamelCase , __UpperCamelCase =x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__UpperCamelCase =grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(SCREAMING_SNAKE_CASE__ , (dist + 1, (nx, ny)) )
__UpperCamelCase =dist + 1
__UpperCamelCase =(x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
_A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : dict[int, list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[bool] ):
__UpperCamelCase =True
__UpperCamelCase =[]
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
order.append(SCREAMING_SNAKE_CASE__ )
return order
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : dict[int, list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[bool] ):
__UpperCamelCase =True
__UpperCamelCase =[vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return component
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : dict[int, list[int]] ):
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ ) * [False]
__UpperCamelCase ={vert: [] for vert in range(len(SCREAMING_SNAKE_CASE__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[]
for i, was_visited in enumerate(SCREAMING_SNAKE_CASE__ ):
if not was_visited:
order += topology_sort(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[]
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ ) * [False]
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
__UpperCamelCase =order[len(SCREAMING_SNAKE_CASE__ ) - i - 1]
if not visited[vert]:
__UpperCamelCase =find_components(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
components_list.append(SCREAMING_SNAKE_CASE__ )
return components_list
| 62 | 1 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : str = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class __UpperCAmelCase ( __lowerCamelCase ):
'''simple docstring'''
__lowercase : int = 'autoformer'
__lowercase : int = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "student_t" , _SCREAMING_SNAKE_CASE = "nll" , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = [1, 2, 3, 4, 5, 6, 7] , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 64 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 32 , _SCREAMING_SNAKE_CASE = 32 , _SCREAMING_SNAKE_CASE = "gelu" , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = 0.02 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE = 10 , _SCREAMING_SNAKE_CASE = 25 , _SCREAMING_SNAKE_CASE = 3 , **_SCREAMING_SNAKE_CASE , ) -> Dict:
# time series specific configuration
A_ = prediction_length
A_ = context_length if context_length is not None else prediction_length
A_ = distribution_output
A_ = loss
A_ = input_size
A_ = num_time_features
A_ = lags_sequence
A_ = scaling
A_ = num_dynamic_real_features
A_ = num_static_real_features
A_ = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(UpperCamelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
A_ = cardinality
else:
A_ = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(UpperCamelCase_ ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
A_ = embedding_dimension
else:
A_ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
A_ = num_parallel_samples
# Transformer architecture configuration
A_ = input_size * len(self.lags_sequence ) + self._number_of_features
A_ = d_model
A_ = encoder_attention_heads
A_ = decoder_attention_heads
A_ = encoder_ffn_dim
A_ = decoder_ffn_dim
A_ = encoder_layers
A_ = decoder_layers
A_ = dropout
A_ = attention_dropout
A_ = activation_dropout
A_ = encoder_layerdrop
A_ = decoder_layerdrop
A_ = activation_function
A_ = init_std
A_ = use_cache
# Autoformer
A_ = label_length
A_ = moving_average
A_ = autocorrelation_factor
super().__init__(is_encoder_decoder=UpperCamelCase_ , **UpperCamelCase_ )
@property
def __A ( self ) -> Any:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 356 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : Any = logging.get_logger(__name__)
def _UpperCAmelCase ( _UpperCamelCase : Dict ) -> List[str]:
A_ = torch.load(_UpperCamelCase, map_location='''cpu''' )
if "model" in sd.keys():
A_ = torch.load(_UpperCamelCase, map_location='''cpu''' )['''model''']
# pop unnecessary weights
A_ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(_UpperCamelCase )
A_ = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
A_ = sd.pop(_UpperCamelCase )
A_ = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
A_ = sd[key]
# We split QKV in separate Q,K,V
A_ = key.replace('''.qkv_proj.''', '''.q_proj.''' )
A_ = key.replace('''.qkv_proj.''', '''.k_proj.''' )
A_ = key.replace('''.qkv_proj.''', '''.v_proj.''' )
A_ = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
A_ ,A_ ,A_ = torch.split(_UpperCamelCase, depth // 3, dim=0 )
A_ = q
A_ = k
A_ = v
del sd[key]
return sd
@torch.no_grad()
def _UpperCAmelCase ( _UpperCamelCase : Optional[int], _UpperCamelCase : Optional[Any], _UpperCamelCase : List[str]=None ) -> Dict:
A_ = load_checkpoint(_UpperCamelCase )
if config is not None:
A_ = OPTConfig.from_pretrained(_UpperCamelCase )
else:
A_ = OPTConfig()
A_ = OPTModel(_UpperCamelCase ).half().eval()
model.load_state_dict(_UpperCamelCase )
# Check results
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__snake_case : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--fairseq_path',
type=str,
help=(
'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
' https://huggingface.co/models?other=opt_metasq'
),
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
__snake_case : Optional[Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 18 | 0 |
'''simple docstring'''
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A = 1
@register_to_config
def __init__(self , _UpperCAmelCase=2_0_0_0 , _UpperCAmelCase=0.1 , _UpperCAmelCase=2_0 , _UpperCAmelCase=1E-3 ) -> List[Any]:
__UpperCamelCase : List[Any] = None
__UpperCamelCase : int = None
__UpperCamelCase : Optional[int] = None
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = None ) -> Tuple:
__UpperCamelCase : List[Any] = torch.linspace(1 , self.config.sampling_eps , _UpperCAmelCase , device=_UpperCAmelCase )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None ) -> str:
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__UpperCamelCase : Any = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__UpperCamelCase : Union[str, Any] = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__UpperCamelCase : List[Any] = std.flatten()
while len(std.shape ) < len(score.shape ):
__UpperCamelCase : Tuple = std.unsqueeze(-1 )
__UpperCamelCase : List[str] = -score / std
# compute
__UpperCamelCase : Tuple = -1.0 / len(self.timesteps )
__UpperCamelCase : Dict = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__UpperCamelCase : Union[str, Any] = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__UpperCamelCase : Dict = beta_t.unsqueeze(-1 )
__UpperCamelCase : List[str] = -0.5 * beta_t * x
__UpperCamelCase : Dict = torch.sqrt(_UpperCAmelCase )
__UpperCamelCase : List[str] = drift - diffusion**2 * score
__UpperCamelCase : List[str] = x + drift * dt
# add noise
__UpperCamelCase : Tuple = randn_tensor(x.shape , layout=x.layout , generator=_UpperCAmelCase , device=x.device , dtype=x.dtype )
__UpperCamelCase : int = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__(self ) -> Dict:
return self.config.num_train_timesteps
| 298 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 255 | 0 |
'''simple docstring'''
from collections.abc import Callable
def _A ( snake_case , snake_case , snake_case ) -> str:
_lowercase : float = a
_lowercase : float = b
if function(_a ) == 0: # one of the a or b is a root for the function
return a
elif function(_a ) == 0:
return b
elif (
function(_a ) * function(_a ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("could not find root in given interval." )
else:
_lowercase : float = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_a ) == 0:
return mid
elif function(_a ) * function(_a ) < 0:
_lowercase : List[str] = mid
else:
_lowercase : int = mid
_lowercase : Dict = start + (end - start) / 2.0
return mid
def _A ( snake_case ) -> int:
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 365 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Any = 'roc_bert'
def __init__( self , _UpperCamelCase=30522 , _UpperCamelCase=768 , _UpperCamelCase=12 , _UpperCamelCase=12 , _UpperCamelCase=3072 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=2 , _UpperCamelCase=0.0_2 , _UpperCamelCase=1E-1_2 , _UpperCamelCase=True , _UpperCamelCase=0 , _UpperCamelCase="absolute" , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=768 , _UpperCamelCase=910 , _UpperCamelCase=512 , _UpperCamelCase=24858 , _UpperCamelCase=True , **_UpperCamelCase , ):
"""simple docstring"""
_lowercase : str = vocab_size
_lowercase : List[str] = max_position_embeddings
_lowercase : List[Any] = hidden_size
_lowercase : Dict = num_hidden_layers
_lowercase : str = num_attention_heads
_lowercase : int = intermediate_size
_lowercase : Optional[Any] = hidden_act
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Dict = attention_probs_dropout_prob
_lowercase : Dict = initializer_range
_lowercase : List[Any] = type_vocab_size
_lowercase : Tuple = layer_norm_eps
_lowercase : Optional[int] = use_cache
_lowercase : Tuple = enable_pronunciation
_lowercase : Optional[int] = enable_shape
_lowercase : int = pronunciation_embed_dim
_lowercase : List[str] = pronunciation_vocab_size
_lowercase : int = shape_embed_dim
_lowercase : str = shape_vocab_size
_lowercase : str = concat_input
_lowercase : Dict = position_embedding_type
_lowercase : Optional[Any] = classifier_dropout
super().__init__(pad_token_id=_UpperCamelCase , **_UpperCamelCase )
| 199 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
SCREAMING_SNAKE_CASE : int = 16
SCREAMING_SNAKE_CASE : str = 32
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ = 16 ) -> Dict:
_lowercase : Optional[int] = AutoTokenizer.from_pretrained('bert-base-cased' )
_lowercase : str = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCamelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_lowercase : Union[str, Any] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowerCamelCase_ , max_length=lowerCamelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowercase : str = datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowercase : str = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCamelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowercase : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowercase : Union[str, Any] = 16
elif accelerator.mixed_precision != "no":
_lowercase : List[str] = 8
else:
_lowercase : List[Any] = None
return tokenizer.pad(
lowerCamelCase_ , padding='longest' , max_length=lowerCamelCase_ , pad_to_multiple_of=lowerCamelCase_ , return_tensors='pt' , )
# Instantiate dataloaders.
_lowercase : Any = DataLoader(
tokenized_datasets['train'] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
_lowercase : Optional[Any] = DataLoader(
tokenized_datasets['validation'] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE : Tuple = mocked_dataloaders # noqa: F811
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , lowerCamelCase_ ) == "1":
_lowercase : int = 2
# Initialize accelerator
_lowercase : Dict = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowercase : str = config['lr']
_lowercase : Any = int(config['num_epochs'] )
_lowercase : int = int(config['seed'] )
_lowercase : Dict = int(config['batch_size'] )
_lowercase : int = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCamelCase_ )
def inner_training_loop(lowerCamelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCamelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowercase : List[str] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowerCamelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowercase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_lowercase : Dict = AdamW(params=model.parameters() , lr=lowerCamelCase_ )
_lowercase , _lowercase : Tuple = get_dataloaders(lowerCamelCase_ , lowerCamelCase_ )
# Instantiate scheduler
_lowercase : int = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase : List[str] = accelerator.prepare(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Now we train the model
for epoch in range(lowerCamelCase_ ):
model.train()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowercase : str = model(**lowerCamelCase_ )
_lowercase : List[str] = outputs.loss
accelerator.backward(lowerCamelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowercase : List[str] = model(**lowerCamelCase_ )
_lowercase : List[Any] = outputs.logits.argmax(dim=-1 )
_lowercase , _lowercase : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCamelCase_ , references=lowerCamelCase_ , )
_lowercase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCamelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def UpperCamelCase_( ) -> List[Any]:
_lowercase : Tuple = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowerCamelCase_ , default=lowerCamelCase_ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
_lowercase : Union[str, Any] = parser.parse_args()
_lowercase : Optional[int] = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
main()
| 21 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class _lowercase :
'''simple docstring'''
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=5 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=50 , snake_case__=0.02 , snake_case__=True , snake_case__=None , ):
'''simple docstring'''
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_input_mask
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = initializer_range
UpperCamelCase_ = use_labels
UpperCamelCase_ = scope
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = None
if self.use_input_mask:
UpperCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = self.get_config()
return config, input_ids, input_mask, token_labels
def _lowerCamelCase ( self ):
'''simple docstring'''
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=snake_case__ , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self ):
'''simple docstring'''
(
(
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) ,
) = self.prepare_config_and_inputs()
UpperCamelCase_ = True
UpperCamelCase_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationEncoder(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ )
UpperCamelCase_ = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = True
UpperCamelCase_ = BertGenerationEncoder(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , )
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = True
UpperCamelCase_ = True
UpperCamelCase_ = BertGenerationDecoder(config=snake_case__ ).to(snake_case__ ).eval()
# first forward pass
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , use_cache=snake_case__ , )
UpperCamelCase_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase_ = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , output_hidden_states=snake_case__ , )["hidden_states"][0]
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , past_key_values=snake_case__ , output_hidden_states=snake_case__ , )["hidden_states"][0]
# select random slice
UpperCamelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-3 ) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , *snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationDecoder(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = self.prepare_config_and_inputs()
UpperCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase (a_ , a_ , a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
lowercase__ = (BertGenerationDecoder,) if is_torch_available() else ()
lowercase__ = (
{"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
if is_torch_available()
else {}
)
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationEncoderTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
UpperCamelCase_ = "bert"
self.model_tester.create_and_check_model(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
(
(
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase_ = None
self.model_tester.create_and_check_model_as_decoder(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*snake_case__ )
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(snake_case__ )
@require_torch
class _lowercase (unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase_ = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
UpperCamelCase_ = model(snake_case__ )[0]
UpperCamelCase_ = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , snake_case__ )
UpperCamelCase_ = torch.tensor(
[[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case__ , atol=1e-4 ) )
@require_torch
class _lowercase (unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase_ = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
UpperCamelCase_ = model(snake_case__ )[0]
UpperCamelCase_ = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , snake_case__ )
UpperCamelCase_ = torch.tensor(
[[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case__ , atol=1e-4 ) )
| 128 | 0 |
from __future__ import annotations
a_ = list[tuple[int, int]]
a_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
a_ = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class _lowercase :
def __init__( self : Any , snake_case : int , snake_case : int , snake_case : int , snake_case : int , snake_case : float , snake_case : Node | None , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : str = pos_x
UpperCamelCase_ : Optional[Any] = pos_y
UpperCamelCase_ : Dict = (pos_y, pos_x)
UpperCamelCase_ : Tuple = goal_x
UpperCamelCase_ : str = goal_y
UpperCamelCase_ : Tuple = g_cost
UpperCamelCase_ : Optional[int] = parent
UpperCamelCase_ : Optional[Any] = self.calculate_heuristic()
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> float:
"""simple docstring"""
UpperCamelCase_ : Dict = abs(self.pos_x - self.goal_x )
UpperCamelCase_ : Dict = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self : int , snake_case : Union[str, Any] ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class _lowercase :
def __init__( self : Optional[int] , snake_case : tuple[int, int] , snake_case : tuple[int, int] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , snake_case )
UpperCamelCase_ : Optional[int] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , snake_case )
UpperCamelCase_ : Optional[Any] = [self.start]
UpperCamelCase_ : list[Node] = []
UpperCamelCase_ : Optional[Any] = False
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Path | None:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
UpperCamelCase_ : Optional[int] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
UpperCamelCase_ : str = True
return self.retrace_path(snake_case )
self.closed_nodes.append(snake_case )
UpperCamelCase_ : List[Any] = self.get_successors(snake_case )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(snake_case )
else:
# retrieve the best current path
UpperCamelCase_ : Any = self.open_nodes.pop(self.open_nodes.index(snake_case ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(snake_case )
else:
self.open_nodes.append(snake_case )
if not self.reached:
return [self.start.pos]
return None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : Node ) -> list[Node]:
"""simple docstring"""
UpperCamelCase_ : Any = []
for action in delta:
UpperCamelCase_ : Tuple = parent.pos_x + action[1]
UpperCamelCase_ : Tuple = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
snake_case , snake_case , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , snake_case , ) )
return successors
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Node | None ) -> Path:
"""simple docstring"""
UpperCamelCase_ : str = node
UpperCamelCase_ : List[str] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCamelCase_ : Optional[int] = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
a_ = (0, 0)
a_ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('------')
a_ = GreedyBestFirst(init, goal)
a_ = greedy_bf.search()
if path:
for pos_x, pos_y in path:
a_ = 2
for elem in grid:
print(elem)
| 50 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self : List[Any] , snake_case : List[str] , snake_case : str=1_3 , snake_case : Any=3_0 , snake_case : Tuple=2 , snake_case : List[Any]=3 , snake_case : str=True , snake_case : List[Any]=True , snake_case : List[Any]=3_2 , snake_case : Union[str, Any]=5 , snake_case : Dict=4 , snake_case : str=3_7 , snake_case : Optional[int]="gelu" , snake_case : Any=0.1 , snake_case : Optional[Any]=0.1 , snake_case : Union[str, Any]=1_0 , snake_case : List[str]=0.02 , snake_case : Optional[int]=3 , snake_case : str=0.6 , snake_case : Any=None , ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : int = parent
UpperCamelCase_ : Optional[Any] = batch_size
UpperCamelCase_ : List[str] = image_size
UpperCamelCase_ : Optional[Any] = patch_size
UpperCamelCase_ : Optional[int] = num_channels
UpperCamelCase_ : Union[str, Any] = is_training
UpperCamelCase_ : Dict = use_labels
UpperCamelCase_ : Tuple = hidden_size
UpperCamelCase_ : str = num_hidden_layers
UpperCamelCase_ : Tuple = num_attention_heads
UpperCamelCase_ : Any = intermediate_size
UpperCamelCase_ : Dict = hidden_act
UpperCamelCase_ : Tuple = hidden_dropout_prob
UpperCamelCase_ : Dict = attention_probs_dropout_prob
UpperCamelCase_ : Any = type_sequence_label_size
UpperCamelCase_ : str = initializer_range
UpperCamelCase_ : Tuple = mask_ratio
UpperCamelCase_ : int = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCamelCase_ : List[Any] = (image_size // patch_size) ** 2
UpperCamelCase_ : Dict = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase_ : List[str] = None
if self.use_labels:
UpperCamelCase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ : List[str] = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , snake_case : Dict , snake_case : List[str] , snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ViTMAEModel(config=snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : int = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : List[str] , snake_case : Optional[int] , snake_case : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = ViTMAEForPreTraining(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : Tuple = model(snake_case )
UpperCamelCase_ : Tuple = (self.image_size // self.patch_size) ** 2
UpperCamelCase_ : Tuple = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCamelCase_ : Optional[int] = 1
UpperCamelCase_ : Dict = ViTMAEForPreTraining(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase_ : Tuple = model(snake_case )
UpperCamelCase_ : Optional[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = self.prepare_config_and_inputs()
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_ : int = config_and_inputs
UpperCamelCase_ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( snake_case_ , snake_case_ , unittest.TestCase ):
lowercase = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowercase = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Any = ViTMAEModelTester(self )
UpperCamelCase_ : Tuple = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ : Any = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase_ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ : List[str] = model_class(snake_case )
UpperCamelCase_ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase_ : Union[str, Any] = [*signature.parameters.keys()]
UpperCamelCase_ : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
"""simple docstring"""
UpperCamelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : List[str] , snake_case : Optional[int] , snake_case : Dict ) -> Dict:
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase_ : Optional[int] = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCamelCase_ : List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase_ : int = torch.from_numpy(snake_case )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCamelCase_ : Tuple = pt_noise
super().check_pt_tf_models(snake_case , snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ : Tuple = model_class(snake_case )
model.to(snake_case )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ : str = model(**self._prepare_for_class(snake_case , snake_case ) )
UpperCamelCase_ : Any = outputs[0].cpu().numpy()
UpperCamelCase_ : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case )
UpperCamelCase_ : Union[str, Any] = model_class.from_pretrained(snake_case )
model.to(snake_case )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ : Any = model(**self._prepare_for_class(snake_case , snake_case ) )
# Make sure we don't have nans
UpperCamelCase_ : Optional[Any] = after_outputs[0].cpu().numpy()
UpperCamelCase_ : Union[str, Any] = 0
UpperCamelCase_ : int = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(snake_case , 1e-5 )
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
"""simple docstring"""
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
"""simple docstring"""
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[Any]:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ : Dict = ViTMAEModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __lowercase ( ):
UpperCamelCase_ : List[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase_ : List[str] = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(snake_case )
UpperCamelCase_ : Tuple = self.default_image_processor
UpperCamelCase_ : Union[str, Any] = prepare_img()
UpperCamelCase_ : Optional[int] = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCamelCase_ : int = ViTMAEConfig()
UpperCamelCase_ : Any = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCamelCase_ : List[str] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCamelCase_ : str = model(**snake_case , noise=torch.from_numpy(snake_case ).to(device=snake_case ) )
# verify the logits
UpperCamelCase_ : Dict = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape , snake_case )
UpperCamelCase_ : Union[str, Any] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(snake_case ) , atol=1e-4 ) )
| 50 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = False ) -> int:
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
A__ = f"""Expected string as input, found {type(_UpperCAmelCase )}"""
raise ValueError(_UpperCAmelCase )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
A__ = f"""Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase )}"""
raise ValueError(_UpperCAmelCase )
A__ = input_str.split('''_''' )
A__ = 0 if use_pascal else 1
A__ = words[start_index:]
A__ = [word[0].upper() + word[1:] for word in words_to_capitalize]
A__ = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 14 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowercase_ : List[str] = CustomTokenizer
pass
| 286 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 363 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig
__lowercase = logging.get_logger(__name__)
# General docstring
__lowercase = '''MobileNetV1Config'''
# Base docstring
__lowercase = '''google/mobilenet_v1_1.0_224'''
__lowercase = [1, 1024, 7, 7]
# Image classification docstring
__lowercase = '''google/mobilenet_v1_1.0_224'''
__lowercase = '''tabby, tabby cat'''
__lowercase = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__UpperCamelCase :Tuple = {}
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Dict = model.mobilenet_va
else:
__UpperCamelCase :str = model
__UpperCamelCase :int = '''MobilenetV1/Conv2d_0/'''
__UpperCamelCase :str = backbone.conv_stem.convolution.weight
__UpperCamelCase :int = backbone.conv_stem.normalization.bias
__UpperCamelCase :Union[str, Any] = backbone.conv_stem.normalization.weight
__UpperCamelCase :Optional[int] = backbone.conv_stem.normalization.running_mean
__UpperCamelCase :Optional[int] = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__UpperCamelCase :Optional[Any] = i + 1
__UpperCamelCase :Optional[int] = i * 2
__UpperCamelCase :List[Any] = backbone.layer[pt_index]
__UpperCamelCase :Tuple = f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__UpperCamelCase :Any = pointer.convolution.weight
__UpperCamelCase :Dict = pointer.normalization.bias
__UpperCamelCase :List[str] = pointer.normalization.weight
__UpperCamelCase :Any = pointer.normalization.running_mean
__UpperCamelCase :List[str] = pointer.normalization.running_var
__UpperCamelCase :Union[str, Any] = backbone.layer[pt_index + 1]
__UpperCamelCase :List[str] = f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__UpperCamelCase :Optional[Any] = pointer.convolution.weight
__UpperCamelCase :Dict = pointer.normalization.bias
__UpperCamelCase :int = pointer.normalization.weight
__UpperCamelCase :Optional[int] = pointer.normalization.running_mean
__UpperCamelCase :Optional[int] = pointer.normalization.running_var
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Any = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
__UpperCamelCase :Union[str, Any] = model.classifier.weight
__UpperCamelCase :int = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
__UpperCamelCase :Any = tf.train.list_variables(SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = {}
for name, shape in init_vars:
logger.info(f"""Loading TF weight {name} with shape {shape}""" )
__UpperCamelCase :str = tf.train.load_variable(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = array
# Build TF to PyTorch weights loading map
__UpperCamelCase :Optional[Any] = _build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(f"""Importing {name}""" )
if name not in tf_weights:
logger.info(f"""{name} not in tf pre-trained weights, skipping""" )
continue
__UpperCamelCase :Optional[Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
__UpperCamelCase :Optional[int] = np.transpose(SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
__UpperCamelCase :Tuple = array.squeeze().transpose()
else:
__UpperCamelCase :Union[str, Any] = np.transpose(SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(f"""Initialize PyTorch weight {name} {array.shape}""" )
__UpperCamelCase :Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE )
tf_weights.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/RMSProp''' , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/RMSProp_1''' , SCREAMING_SNAKE_CASE )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , SCREAMING_SNAKE_CASE )
logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :str = features.shape[-2:]
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = conv_layer.stride
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = conv_layer.kernel_size
if in_height % stride_height == 0:
__UpperCamelCase :Optional[int] = max(kernel_height - stride_height , 0 )
else:
__UpperCamelCase :List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__UpperCamelCase :List[str] = max(kernel_width - stride_width , 0 )
else:
__UpperCamelCase :Tuple = max(kernel_width - (in_width % stride_width) , 0 )
__UpperCamelCase :Any = pad_along_width // 2
__UpperCamelCase :Tuple = pad_along_width - pad_left
__UpperCamelCase :Union[str, Any] = pad_along_height // 2
__UpperCamelCase :str = pad_along_height - pad_top
__UpperCamelCase :Optional[Any] = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''constant''' , 0.0 )
class lowerCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = 1 , __lowercase = 1 , __lowercase = False , __lowercase = True , __lowercase = True , ) -> None:
super().__init__()
__UpperCamelCase :str = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""")
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""")
__UpperCamelCase :Any = 0 if config.tf_padding else int((kernel_size - 1) / 2)
__UpperCamelCase :List[Any] = nn.Convad(
in_channels=__lowercase , out_channels=__lowercase , kernel_size=__lowercase , stride=__lowercase , padding=__lowercase , groups=__lowercase , bias=__lowercase , padding_mode='''zeros''' , )
if use_normalization:
__UpperCamelCase :str = nn.BatchNormad(
num_features=__lowercase , eps=config.layer_norm_eps , momentum=0.99_97 , affine=__lowercase , track_running_stats=__lowercase , )
else:
__UpperCamelCase :Tuple = None
if use_activation:
if isinstance(__lowercase , __lowercase):
__UpperCamelCase :Union[str, Any] = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __lowercase):
__UpperCamelCase :Dict = ACTaFN[config.hidden_act]
else:
__UpperCamelCase :List[Any] = config.hidden_act
else:
__UpperCamelCase :Optional[Any] = None
def UpperCamelCase__ ( self , __lowercase) -> torch.Tensor:
if self.config.tf_padding:
__UpperCamelCase :Any = apply_tf_padding(__lowercase , self.convolution)
__UpperCamelCase :str = self.convolution(__lowercase)
if self.normalization is not None:
__UpperCamelCase :Any = self.normalization(__lowercase)
if self.activation is not None:
__UpperCamelCase :List[str] = self.activation(__lowercase)
return features
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[str] = MobileNetVaConfig
a__ : Dict = load_tf_weights_in_mobilenet_va
a__ : Tuple = """mobilenet_v1"""
a__ : Optional[Any] = """pixel_values"""
a__ : int = False
def UpperCamelCase__ ( self , __lowercase) -> None:
if isinstance(__lowercase , (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(__lowercase , nn.BatchNormad):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
__lowercase = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__lowercase = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.""" , UpperCAmelCase_ , )
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase = True) -> Optional[Any]:
super().__init__(__lowercase)
__UpperCamelCase :List[str] = config
__UpperCamelCase :Any = 32
__UpperCamelCase :List[str] = max(int(depth * config.depth_multiplier) , config.min_depth)
__UpperCamelCase :Union[str, Any] = MobileNetVaConvLayer(
__lowercase , in_channels=config.num_channels , out_channels=__lowercase , kernel_size=3 , stride=2 , )
__UpperCamelCase :str = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__UpperCamelCase :Any = nn.ModuleList()
for i in range(13):
__UpperCamelCase :str = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__UpperCamelCase :Tuple = max(int(depth * config.depth_multiplier) , config.min_depth)
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=3 , stride=strides[i] , groups=__lowercase , ))
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=1 , ))
__UpperCamelCase :str = nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase__ ( self , __lowercase) -> Union[str, Any]:
raise NotImplementedError
@add_start_docstrings_to_model_forward(__lowercase)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCamelCase__ ( self , __lowercase = None , __lowercase = None , __lowercase = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
__UpperCamelCase :Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase :str = 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''')
__UpperCamelCase :int = self.conv_stem(__lowercase)
__UpperCamelCase :List[str] = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
__UpperCamelCase :Optional[Any] = layer_module(__lowercase)
if output_hidden_states:
__UpperCamelCase :int = all_hidden_states + (hidden_states,)
__UpperCamelCase :Any = hidden_states
if self.pooler is not None:
__UpperCamelCase :str = torch.flatten(self.pooler(__lowercase) , start_dim=1)
else:
__UpperCamelCase :Tuple = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowercase , pooler_output=__lowercase , hidden_states=__lowercase , )
@add_start_docstrings(
"""
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , UpperCAmelCase_ , )
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase) -> None:
super().__init__(__lowercase)
__UpperCamelCase :int = config.num_labels
__UpperCamelCase :Optional[int] = MobileNetVaModel(__lowercase)
__UpperCamelCase :Optional[Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__UpperCamelCase :str = nn.Dropout(config.classifier_dropout_prob , inplace=__lowercase)
__UpperCamelCase :Dict = nn.Linear(__lowercase , 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(__lowercase)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCamelCase__ ( self , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
__UpperCamelCase :List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase :Tuple = self.mobilenet_va(__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase)
__UpperCamelCase :List[str] = outputs.pooler_output if return_dict else outputs[1]
__UpperCamelCase :Union[str, Any] = self.classifier(self.dropout(__lowercase))
__UpperCamelCase :int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__UpperCamelCase :Tuple = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__UpperCamelCase :Union[str, Any] = '''single_label_classification'''
else:
__UpperCamelCase :Optional[Any] = '''multi_label_classification'''
if self.config.problem_type == "regression":
__UpperCamelCase :Any = MSELoss()
if self.num_labels == 1:
__UpperCamelCase :List[str] = loss_fct(logits.squeeze() , labels.squeeze())
else:
__UpperCamelCase :Dict = loss_fct(__lowercase , __lowercase)
elif self.config.problem_type == "single_label_classification":
__UpperCamelCase :Optional[int] = CrossEntropyLoss()
__UpperCamelCase :str = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
__UpperCamelCase :Dict = BCEWithLogitsLoss()
__UpperCamelCase :List[str] = loss_fct(__lowercase , __lowercase)
if not return_dict:
__UpperCamelCase :Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__lowercase , logits=__lowercase , hidden_states=outputs.hidden_states , )
| 105 | 0 |
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
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = "▁"
SCREAMING_SNAKE_CASE : List[str] = {"vocab_file": "sentencepiece.bpe.model"}
SCREAMING_SNAKE_CASE : Optional[Any] = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
SCREAMING_SNAKE_CASE : Tuple = {
"facebook/xglm-564M": 2048,
}
class _lowerCamelCase( _a ):
lowercase_ : Dict = VOCAB_FILES_NAMES
lowercase_ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowercase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ : Any = ["""input_ids""", """attention_mask"""]
def __init__( self, lowerCamelCase, lowerCamelCase="<s>", lowerCamelCase="</s>", lowerCamelCase="</s>", lowerCamelCase="<s>", lowerCamelCase="<unk>", lowerCamelCase="<pad>", lowerCamelCase = None, **lowerCamelCase, ) -> None:
"""simple docstring"""
_lowercase : str = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
_lowercase : List[str] = 7
_lowercase : str = [F'''<madeupword{i}>''' for i in range(self.num_madeup_words)]
_lowercase : List[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=lowerCamelCase, eos_token=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, cls_token=lowerCamelCase, pad_token=lowerCamelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCamelCase, )
_lowercase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(lowerCamelCase))
_lowercase : List[Any] = 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
_lowercase : int = 1
# Mimic fairseq token-to-id alignment for the first 4 token
_lowercase : int = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
_lowercase : Any = len(self.sp_model)
_lowercase : Optional[int] = {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
self.fairseq_tokens_to_ids.update(lowerCamelCase)
_lowercase : Tuple = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self) -> Dict:
"""simple docstring"""
_lowercase : Any = self.__dict__.copy()
_lowercase : Optional[int] = None
_lowercase : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self, lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : List[Any] = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs'):
_lowercase : str = {}
_lowercase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
_lowercase : Union[str, Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = False) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase, token_ids_a=lowerCamelCase, already_has_special_tokens=lowerCamelCase)
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase))
return [1] + ([0] * len(lowerCamelCase)) + [1, 1] + ([0] * len(lowerCamelCase))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None) -> List[int]:
"""simple docstring"""
_lowercase : Optional[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) -> Union[str, Any]:
"""simple docstring"""
return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = {self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def UpperCamelCase ( self, lowerCamelCase) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(lowerCamelCase, out_type=lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> Dict:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_lowercase : Optional[Any] = self.sp_model.PieceToId(lowerCamelCase)
# 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, lowerCamelCase) -> str:
"""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, lowerCamelCase) -> Dict:
"""simple docstring"""
_lowercase : Union[str, Any] = ''.join(lowerCamelCase).replace(lowerCamelCase, ' ').strip()
return out_string
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(lowerCamelCase):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
return
_lowercase : Optional[Any] = os.path.join(
lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, lowerCamelCase)
elif not os.path.isfile(self.vocab_file):
with open(lowerCamelCase, 'wb') as fi:
_lowercase : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase)
return (out_vocab_file,)
| 21 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
if isinstance(lowerCamelCase_ , torch.Tensor ):
return image
elif isinstance(lowerCamelCase_ , PIL.Image.Image ):
_lowercase : List[Any] = [image]
if isinstance(image[0] , PIL.Image.Image ):
_lowercase : Tuple = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
_lowercase : str = np.concatenate(lowerCamelCase_ , axis=0 )
_lowercase : Dict = np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
_lowercase : Optional[int] = image.transpose(0 , 3 , 1 , 2 )
_lowercase : str = 2.0 * image - 1.0
_lowercase : Tuple = torch.from_numpy(lowerCamelCase_ )
elif isinstance(image[0] , torch.Tensor ):
_lowercase : Any = torch.cat(lowerCamelCase_ , dim=0 )
return image
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=0.99_95 ) -> Tuple:
if not isinstance(lowerCamelCase_ , np.ndarray ):
_lowercase : List[Any] = True
_lowercase : Any = va.device
_lowercase : Union[str, Any] = va.cpu().numpy()
_lowercase : int = va.cpu().numpy()
_lowercase : int = np.sum(va * va / (np.linalg.norm(lowerCamelCase_ ) * np.linalg.norm(lowerCamelCase_ )) )
if np.abs(lowerCamelCase_ ) > DOT_THRESHOLD:
_lowercase : Any = (1 - t) * va + t * va
else:
_lowercase : Dict = np.arccos(lowerCamelCase_ )
_lowercase : str = np.sin(lowerCamelCase_ )
_lowercase : int = theta_a * t
_lowercase : Dict = np.sin(lowerCamelCase_ )
_lowercase : Any = np.sin(theta_a - theta_t ) / sin_theta_a
_lowercase : List[Any] = sin_theta_t / sin_theta_a
_lowercase : Dict = sa * va + sa * va
if inputs_are_torch:
_lowercase : Optional[Any] = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
return va
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
_lowercase : Tuple = F.normalize(lowerCamelCase_ , dim=-1 )
_lowercase : Tuple = F.normalize(lowerCamelCase_ , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
for param in model.parameters():
_lowercase : Any = value
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase=None, ) -> Tuple:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=lowerCamelCase, text_encoder=lowerCamelCase, clip_model=lowerCamelCase, tokenizer=lowerCamelCase, unet=lowerCamelCase, scheduler=lowerCamelCase, feature_extractor=lowerCamelCase, coca_model=lowerCamelCase, coca_tokenizer=lowerCamelCase, coca_transform=lowerCamelCase, )
_lowercase : Tuple = (
feature_extractor.size
if isinstance(feature_extractor.size, lowerCamelCase)
else feature_extractor.size['shortest_edge']
)
_lowercase : Union[str, Any] = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
set_requires_grad(self.text_encoder, lowerCamelCase)
set_requires_grad(self.clip_model, lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase = "auto") -> Any:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_lowercase : Optional[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
self.enable_attention_slicing(lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
set_requires_grad(self.vae, lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
set_requires_grad(self.vae, lowerCamelCase)
def UpperCamelCase ( self) -> str:
"""simple docstring"""
set_requires_grad(self.unet, lowerCamelCase)
def UpperCamelCase ( self) -> int:
"""simple docstring"""
set_requires_grad(self.unet, lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase : str = min(int(num_inference_steps * strength), lowerCamelCase)
_lowercase : List[Any] = max(num_inference_steps - init_timestep, 0)
_lowercase : int = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None) -> Optional[Any]:
"""simple docstring"""
if not isinstance(lowerCamelCase, torch.Tensor):
raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase)}''')
_lowercase : Any = image.to(device=lowerCamelCase, dtype=lowerCamelCase)
if isinstance(lowerCamelCase, lowerCamelCase):
_lowercase : Dict = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(lowerCamelCase)
]
_lowercase : int = torch.cat(lowerCamelCase, dim=0)
else:
_lowercase : int = self.vae.encode(lowerCamelCase).latent_dist.sample(lowerCamelCase)
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowercase : str = 0.1_8_2_1_5 * init_latents
_lowercase : List[str] = init_latents.repeat_interleave(lowerCamelCase, dim=0)
_lowercase : List[str] = randn_tensor(init_latents.shape, generator=lowerCamelCase, device=lowerCamelCase, dtype=lowerCamelCase)
# get latents
_lowercase : Any = self.scheduler.add_noise(lowerCamelCase, lowerCamelCase, lowerCamelCase)
_lowercase : str = init_latents
return latents
def UpperCamelCase ( self, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase : str = self.coca_transform(lowerCamelCase).unsqueeze(0)
with torch.no_grad(), torch.cuda.amp.autocast():
_lowercase : List[str] = self.coca_model.generate(transformed_image.to(device=self.device, dtype=self.coca_model.dtype))
_lowercase : int = self.coca_tokenizer.decode(generated[0].cpu().numpy())
return generated.split('<end_of_text>')[0].replace('<start_of_text>', '').rstrip(' .,')
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> List[str]:
"""simple docstring"""
_lowercase : Tuple = self.feature_extractor.preprocess(lowerCamelCase)
_lowercase : List[str] = torch.from_numpy(clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
_lowercase : int = self.clip_model.get_image_features(lowerCamelCase)
_lowercase : Dict = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=lowerCamelCase)
_lowercase : int = image_embeddings_clip.repeat_interleave(lowerCamelCase, dim=0)
return image_embeddings_clip
@torch.enable_grad()
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, ) -> List[str]:
"""simple docstring"""
_lowercase : List[Any] = latents.detach().requires_grad_()
_lowercase : Union[str, Any] = self.scheduler.scale_model_input(lowerCamelCase, lowerCamelCase)
# predict the noise residual
_lowercase : Tuple = self.unet(lowerCamelCase, lowerCamelCase, encoder_hidden_states=lowerCamelCase).sample
if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
_lowercase : Any = self.scheduler.alphas_cumprod[timestep]
_lowercase : Any = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_lowercase : List[Any] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_lowercase : List[str] = torch.sqrt(lowerCamelCase)
_lowercase : Dict = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler, lowerCamelCase):
_lowercase : Dict = self.scheduler.sigmas[index]
_lowercase : List[Any] = latents - sigma * noise_pred
else:
raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowercase : Dict = 1 / 0.1_8_2_1_5 * sample
_lowercase : Optional[Any] = self.vae.decode(lowerCamelCase).sample
_lowercase : int = (image / 2 + 0.5).clamp(0, 1)
_lowercase : Any = transforms.Resize(self.feature_extractor_size)(lowerCamelCase)
_lowercase : Optional[Any] = self.normalize(lowerCamelCase).to(latents.dtype)
_lowercase : List[str] = self.clip_model.get_image_features(lowerCamelCase)
_lowercase : List[Any] = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=lowerCamelCase)
_lowercase : Optional[Any] = spherical_dist_loss(lowerCamelCase, lowerCamelCase).mean() * clip_guidance_scale
_lowercase : str = -torch.autograd.grad(lowerCamelCase, lowerCamelCase)[0]
if isinstance(self.scheduler, lowerCamelCase):
_lowercase : Union[str, Any] = latents.detach() + grads * (sigma**2)
_lowercase : List[str] = noise_pred_original
else:
_lowercase : List[Any] = noise_pred_original - torch.sqrt(lowerCamelCase) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = 5_12, lowerCamelCase = 5_12, lowerCamelCase = 0.6, lowerCamelCase = 50, lowerCamelCase = 7.5, lowerCamelCase = 1, lowerCamelCase = 0.0, lowerCamelCase = 1_00, lowerCamelCase = None, lowerCamelCase = "pil", lowerCamelCase = True, lowerCamelCase = 0.8, lowerCamelCase = 0.1, lowerCamelCase = 0.1, ) -> int:
"""simple docstring"""
if isinstance(lowerCamelCase, lowerCamelCase) and len(lowerCamelCase) != batch_size:
raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase)} generators.''')
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')
if isinstance(lowerCamelCase, torch.Generator) and batch_size > 1:
_lowercase : Dict = [generator] + [None] * (batch_size - 1)
_lowercase : Optional[int] = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
_lowercase : Optional[int] = [x[0] for x in coca_is_none if x[1]]
_lowercase : str = ', '.join(lowerCamelCase)
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(lowerCamelCase):
raise ValueError(
F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
_lowercase : List[Any] = self.get_image_description(lowerCamelCase)
if style_prompt is None:
if len(lowerCamelCase):
raise ValueError(
F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
_lowercase : Dict = self.get_image_description(lowerCamelCase)
# get prompt text embeddings for content and style
_lowercase : Optional[int] = self.tokenizer(
lowerCamelCase, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=lowerCamelCase, return_tensors='pt', )
_lowercase : Optional[int] = self.text_encoder(content_text_input.input_ids.to(self.device))[0]
_lowercase : Union[str, Any] = self.tokenizer(
lowerCamelCase, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=lowerCamelCase, return_tensors='pt', )
_lowercase : List[Any] = self.text_encoder(style_text_input.input_ids.to(self.device))[0]
_lowercase : Any = slerp(lowerCamelCase, lowerCamelCase, lowerCamelCase)
# duplicate text embeddings for each generation per prompt
_lowercase : Dict = text_embeddings.repeat_interleave(lowerCamelCase, dim=0)
# set timesteps
_lowercase : Dict = 'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
_lowercase : Optional[Any] = {}
if accepts_offset:
_lowercase : Any = 1
self.scheduler.set_timesteps(lowerCamelCase, **lowerCamelCase)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device)
_lowercase , _lowercase : List[Any] = self.get_timesteps(lowerCamelCase, lowerCamelCase, self.device)
_lowercase : str = timesteps[:1].repeat(lowerCamelCase)
# Preprocess image
_lowercase : str = preprocess(lowerCamelCase, lowerCamelCase, lowerCamelCase)
_lowercase : List[str] = self.prepare_latents(
lowerCamelCase, lowerCamelCase, lowerCamelCase, text_embeddings.dtype, self.device, lowerCamelCase)
_lowercase : int = preprocess(lowerCamelCase, lowerCamelCase, lowerCamelCase)
_lowercase : List[str] = self.prepare_latents(
lowerCamelCase, lowerCamelCase, lowerCamelCase, text_embeddings.dtype, self.device, lowerCamelCase)
_lowercase : Optional[int] = slerp(lowerCamelCase, lowerCamelCase, lowerCamelCase)
if clip_guidance_scale > 0:
_lowercase : Optional[int] = self.get_clip_image_embeddings(lowerCamelCase, lowerCamelCase)
_lowercase : Dict = self.get_clip_image_embeddings(lowerCamelCase, lowerCamelCase)
_lowercase : Optional[int] = slerp(
lowerCamelCase, lowerCamelCase, lowerCamelCase)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_lowercase : Dict = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_lowercase : Tuple = content_text_input.input_ids.shape[-1]
_lowercase : Union[str, Any] = self.tokenizer([''], padding='max_length', max_length=lowerCamelCase, return_tensors='pt')
_lowercase : int = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt
_lowercase : Union[str, Any] = uncond_embeddings.repeat_interleave(lowerCamelCase, dim=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowercase : Optional[Any] = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_lowercase : Tuple = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_lowercase : Optional[int] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_lowercase : List[Any] = torch.randn(lowerCamelCase, generator=lowerCamelCase, device='cpu', dtype=lowerCamelCase).to(
self.device)
else:
_lowercase : Any = torch.randn(lowerCamelCase, generator=lowerCamelCase, device=self.device, dtype=lowerCamelCase)
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
_lowercase : Tuple = latents.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
_lowercase : List[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_lowercase : Dict = 'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
_lowercase : Optional[Any] = {}
if accepts_eta:
_lowercase : List[Any] = eta
# check if the scheduler accepts generator
_lowercase : Dict = 'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
_lowercase : str = generator
with self.progress_bar(total=lowerCamelCase):
for i, t in enumerate(lowerCamelCase):
# expand the latents if we are doing classifier free guidance
_lowercase : List[str] = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
_lowercase : List[Any] = self.scheduler.scale_model_input(lowerCamelCase, lowerCamelCase)
# predict the noise residual
_lowercase : Dict = self.unet(lowerCamelCase, lowerCamelCase, encoder_hidden_states=lowerCamelCase).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_lowercase , _lowercase : Optional[Any] = noise_pred.chunk(2)
_lowercase : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_lowercase : Tuple = (
text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
)
_lowercase , _lowercase : List[Any] = self.cond_fn(
lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )
# compute the previous noisy sample x_t -> x_t-1
_lowercase : Optional[Any] = self.scheduler.step(lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_lowercase : Any = 1 / 0.1_8_2_1_5 * latents
_lowercase : List[str] = self.vae.decode(lowerCamelCase).sample
_lowercase : Tuple = (image / 2 + 0.5).clamp(0, 1)
_lowercase : List[Any] = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
_lowercase : List[Any] = self.numpy_to_pil(lowerCamelCase)
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=lowerCamelCase, nsfw_content_detected=lowerCamelCase)
| 21 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __magic_name__ ( __lowerCAmelCase):
A: Optional[int] = ComputeEnvironment.AMAZON_SAGEMAKER
A: str = True
A: Tuple = "ml.p3.2xlarge"
A: int = "accelerate_sagemaker_execution_role"
A: List[Any] = "hf-sm"
A: str = "us-east-1"
A: int = 1
A: Tuple = "accelerate-sagemaker-1"
A: Optional[int] = "1.6"
A: Dict = "4.4"
A: Dict = "train.py"
A: List[Any] = [
"--model_name_or_path",
"bert",
"--do_train",
"False",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
A: Optional[Any] = [
"--model_name_or_path",
"bert",
"--do_train",
"--do_test",
"False",
"--do_predict",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : List[str] ) -> int:
'''simple docstring'''
UpperCamelCase__ : str = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , lowerCamelCase__ )
assert isinstance(converted_args['''do_train'''] , lowerCamelCase__ )
assert isinstance(converted_args['''epochs'''] , lowerCamelCase__ )
assert isinstance(converted_args['''learning_rate'''] , lowerCamelCase__ )
assert isinstance(converted_args['''max_steps'''] , lowerCamelCase__ )
with pytest.raises(lowerCamelCase__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 51 |
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
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
# See all ViT models at https://huggingface.co/models?filter=vit
}
class __magic_name__ ( __lowerCAmelCase):
A: Optional[int] = "vit"
def __init__( self : Optional[int] , lowerCamelCase__ : Union[str, Any]=768 , lowerCamelCase__ : Optional[int]=12 , lowerCamelCase__ : Optional[int]=12 , lowerCamelCase__ : Any=3072 , lowerCamelCase__ : Tuple="gelu" , lowerCamelCase__ : Any=0.0 , lowerCamelCase__ : Tuple=0.0 , lowerCamelCase__ : Tuple=0.02 , lowerCamelCase__ : str=1E-1_2 , lowerCamelCase__ : Dict=224 , lowerCamelCase__ : int=16 , lowerCamelCase__ : List[Any]=3 , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Optional[int]=16 , **lowerCamelCase__ : List[str] , ) -> List[Any]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = hidden_size
UpperCamelCase__ : str = num_hidden_layers
UpperCamelCase__ : List[str] = num_attention_heads
UpperCamelCase__ : List[str] = intermediate_size
UpperCamelCase__ : Optional[Any] = hidden_act
UpperCamelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCamelCase__ : List[Any] = attention_probs_dropout_prob
UpperCamelCase__ : Union[str, Any] = initializer_range
UpperCamelCase__ : Union[str, Any] = layer_norm_eps
UpperCamelCase__ : Optional[int] = image_size
UpperCamelCase__ : Optional[Any] = patch_size
UpperCamelCase__ : Optional[Any] = num_channels
UpperCamelCase__ : List[Any] = qkv_bias
UpperCamelCase__ : Union[str, Any] = encoder_stride
class __magic_name__ ( __lowerCAmelCase):
A: int = version.parse("1.11")
@property
def UpperCAmelCase__ ( self : int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def UpperCAmelCase__ ( self : Tuple ) -> float:
'''simple docstring'''
return 1E-4
| 51 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A , A ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = []
create_all_state(1 , A , A , [] , A )
return result
def _SCREAMING_SNAKE_CASE (A , A , A , A , A , ) -> None:
"""simple docstring"""
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def _SCREAMING_SNAKE_CASE (A ) -> None:
"""simple docstring"""
for i in total_list:
print(*A )
if __name__ == "__main__":
lowerCamelCase : Tuple = 4
lowerCamelCase : Union[str, Any] = 2
lowerCamelCase : Dict = generate_all_combinations(n, k)
print_all_state(total_list)
| 2 |
'''simple docstring'''
from ....utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def __init__(self : Optional[int] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any]=None , UpperCamelCase : int=2048 ):
'''simple docstring'''
lowercase__ = config.__dict__
lowercase__ = modal_hidden_size
if num_labels:
lowercase__ = num_labels
| 2 | 1 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
return int(input_a == input_a == 0 )
def __snake_case ( ):
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(f'| 0 | 0 | {nor_gate(0 , 0 )} |' )
print(f'| 0 | 1 | {nor_gate(0 , 1 )} |' )
print(f'| 1 | 0 | {nor_gate(1 , 0 )} |' )
print(f'| 1 | 1 | {nor_gate(1 , 1 )} |' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 131 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : str):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a = 1
__a = 3
__a = (32, 32)
__a = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
return image
@property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
torch.manual_seed(0)
__a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
return model
@property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
torch.manual_seed(0)
__a = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def _lowerCamelCase ( self : Any):
'''simple docstring'''
torch.manual_seed(0)
__a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(__SCREAMING_SNAKE_CASE)
@property
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
def extract(*__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : Dict):
class _A :
def __init__( self : int):
'''simple docstring'''
__a = torch.ones([0])
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
self.pixel_values.to(__SCREAMING_SNAKE_CASE)
return self
return Out()
return extract
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a = self.dummy_cond_unet
__a = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__SCREAMING_SNAKE_CASE , set_alpha_to_one=__SCREAMING_SNAKE_CASE , )
__a = self.dummy_vae
__a = self.dummy_text_encoder
__a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
# make sure here that pndm scheduler skips prk
__a = StableDiffusionPipeline(
unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = '''A painting of a squirrel eating a burger'''
__a = torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
__a = sd_pipe([prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''')
__a = output.images
__a = torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=__SCREAMING_SNAKE_CASE , )[0]
__a = image[0, -3:, -3:, -1]
__a = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a = np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__a = self.dummy_cond_unet
__a = PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
__a = self.dummy_vae
__a = self.dummy_text_encoder
__a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
# make sure here that pndm scheduler skips prk
__a = StableDiffusionPipeline(
unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = '''A painting of a squirrel eating a burger'''
__a = torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
__a = sd_pipe([prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''')
__a = output.images
__a = torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , return_dict=__SCREAMING_SNAKE_CASE , )[0]
__a = image[0, -3:, -3:, -1]
__a = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a = np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = StableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-lms-pipe''' , safety_checker=__SCREAMING_SNAKE_CASE)
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
assert isinstance(pipe.scheduler , __SCREAMING_SNAKE_CASE)
assert pipe.safety_checker is None
__a = pipe('''example prompt''' , num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__SCREAMING_SNAKE_CASE)
__a = StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__a = pipe('''example prompt''' , num_inference_steps=2).images[0]
assert image is not None
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''')
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = self.dummy_cond_unet
__a = PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
__a = self.dummy_vae
__a = self.dummy_text_encoder
__a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
# put models in fp16
__a = unet.half()
__a = vae.half()
__a = bert.half()
# make sure here that pndm scheduler skips prk
__a = StableDiffusionPipeline(
unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = '''A painting of a squirrel eating a burger'''
__a = sd_pipe([prompt] , num_inference_steps=2 , output_type='''np''').images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=__SCREAMING_SNAKE_CASE)
__a = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = (
'''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
''' children from bahnhof zoo, detailed '''
)
__a = 4_003_660_346
__a = 7
# without safety guidance (sld_guidance_scale = 0)
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
# without safety guidance (strong configuration)
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' , safety_checker=__SCREAMING_SNAKE_CASE)
__a = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = '''padme amidala taking a bath artwork, safe for work, no nudity'''
__a = 2_734_971_755
__a = 7
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
__a = sd_pipe.to(__SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
__a = (
'''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
''' leyendecker'''
)
__a = 1_044_355_234
__a = 12
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=0 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7
__a = torch.manual_seed(__SCREAMING_SNAKE_CASE)
__a = sd_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=50 , output_type='''np''' , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__a = output.images
__a = image[0, -3:, -3:, -1]
__a = np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
| 131 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
__SCREAMING_SNAKE_CASE : Optional[int] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__SCREAMING_SNAKE_CASE : str = BASE_URL + '''/user'''
# https://github.com/settings/tokens
__SCREAMING_SNAKE_CASE : List[Any] = os.environ.get("""USER_TOKEN""", """""")
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> dict[Any, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = {
"Authorization": F"""token {auth_token}""",
"Accept": "application/vnd.github.v3+json",
}
return requests.get(__lowercase , headers=__lowercase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(F'{key}: {value}')
else:
raise ValueError("""\'USER_TOKEN\' field cannot be empty.""")
| 31 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_UpperCAmelCase : Dict = "longformer"
def __init__( self : Optional[Any] , lowercase : Union[List[int], int] = 512 , lowercase : int = 2 , lowercase : int = 1 , lowercase : int = 0 , lowercase : int = 2 , lowercase : int = 30_522 , lowercase : int = 768 , lowercase : int = 12 , lowercase : int = 12 , lowercase : int = 3_072 , lowercase : str = "gelu" , lowercase : float = 0.1 , lowercase : float = 0.1 , lowercase : int = 512 , lowercase : int = 2 , lowercase : float = 0.02 , lowercase : float = 1E-12 , lowercase : bool = False , **lowercase : Optional[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase , **lowercase )
_snake_case = attention_window
_snake_case = sep_token_id
_snake_case = bos_token_id
_snake_case = eos_token_id
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = hidden_act
_snake_case = intermediate_size
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = onnx_export
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : int , lowercase : "PretrainedConfig" , lowercase : str = "default" , lowercase : "List[PatchingSpec]" = None ):
'''simple docstring'''
super().__init__(lowercase , lowercase , lowercase )
_snake_case = True
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
_snake_case = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_snake_case = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def A ( self : int ):
'''simple docstring'''
_snake_case = super().outputs
if self.task == "default":
_snake_case = {0: 'batch'}
return outputs
@property
def A ( self : List[Any] ):
'''simple docstring'''
return 1E-4
@property
def A ( self : List[str] ):
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def A ( self : str , lowercase : "PreTrainedTokenizerBase" , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
_snake_case = super().generate_dummy_inputs(
preprocessor=lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
_snake_case = torch.zeros_like(inputs['input_ids'] )
# make every second token global
_snake_case = 1
return inputs
| 282 | 0 |
'''simple docstring'''
import functools
from typing import Any
def lowercase_ ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict ):
"""simple docstring"""
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or len(lowerCAmelCase__ ) == 0:
raise ValueError("""the string should be not empty string""" )
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not all(
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) > 0 for item in words ):
raise ValueError("""the words should be a list of non-empty strings""" )
# Build trie
__UpperCAmelCase : int = {}
__UpperCAmelCase : Union[str, Any] = """WORD_KEEPER"""
for word in words:
__UpperCAmelCase : Optional[int] = trie
for c in word:
if c not in trie_node:
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : int = trie_node[c]
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : str = len(lowerCAmelCase__ )
# Dynamic programming method
@functools.cache
def is_breakable(lowerCAmelCase__ : str ) -> bool:
if index == len_string:
return True
__UpperCAmelCase : Dict = trie
for i in range(lowerCAmelCase__ , lowerCAmelCase__ ):
__UpperCAmelCase : List[str] = trie_node.get(string[i] , lowerCAmelCase__ )
if trie_node is None:
return False
if trie_node.get(lowerCAmelCase__ , lowerCAmelCase__ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _A :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=[1, 2, 1] , __UpperCAmelCase=[2, 2, 4] , __UpperCAmelCase=2 , __UpperCAmelCase=2.0 , __UpperCAmelCase=True , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase="gelu" , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-5 , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=10 , __UpperCAmelCase=8 , ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : Any = image_size
__UpperCAmelCase : Dict = patch_size
__UpperCAmelCase : Dict = num_channels
__UpperCAmelCase : List[Any] = embed_dim
__UpperCAmelCase : str = depths
__UpperCAmelCase : Dict = num_heads
__UpperCAmelCase : str = window_size
__UpperCAmelCase : int = mlp_ratio
__UpperCAmelCase : Union[str, Any] = qkv_bias
__UpperCAmelCase : Dict = hidden_dropout_prob
__UpperCAmelCase : str = attention_probs_dropout_prob
__UpperCAmelCase : Optional[int] = drop_path_rate
__UpperCAmelCase : List[str] = hidden_act
__UpperCAmelCase : Optional[int] = use_absolute_embeddings
__UpperCAmelCase : Any = patch_norm
__UpperCAmelCase : Union[str, Any] = layer_norm_eps
__UpperCAmelCase : Optional[int] = initializer_range
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : Any = scope
__UpperCAmelCase : Optional[Any] = use_labels
__UpperCAmelCase : Optional[int] = type_sequence_label_size
__UpperCAmelCase : int = encoder_stride
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase : Tuple = None
if self.use_labels:
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __A ( self ) -> Dict:
'''simple docstring'''
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , 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 , )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = SwinvaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Union[str, Any] = model(__UpperCAmelCase )
__UpperCAmelCase : Tuple = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__UpperCAmelCase : List[Any] = 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 __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Any = SwinvaForMaskedImageModeling(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : List[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : Dict = SwinvaForMaskedImageModeling(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__UpperCAmelCase : str = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : str = self.type_sequence_label_size
__UpperCAmelCase : str = SwinvaForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Any = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = config_and_inputs
__UpperCAmelCase : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[str] = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE : List[str] = (
{"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : Dict = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : List[str] = SwinvaModelTester(self )
__UpperCAmelCase : Any = ConfigTester(self , config_class=__UpperCAmelCase , embed_dim=37 )
def __A ( self ) -> Any:
'''simple docstring'''
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 __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def __A ( self ) -> Dict:
'''simple docstring'''
pass
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__UpperCAmelCase : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Tuple = model_class(__UpperCAmelCase )
__UpperCAmelCase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : str = [*signature.parameters.keys()]
__UpperCAmelCase : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def __A ( self ) -> int:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[Any] = True
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : int = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : List[Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : str = outputs.attentions
__UpperCAmelCase : Any = len(self.model_tester.depths )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__UpperCAmelCase : Dict = True
__UpperCAmelCase : int = config.window_size**2
__UpperCAmelCase : Any = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : int = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : Dict = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__UpperCAmelCase : Dict = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__UpperCAmelCase : Any = True
__UpperCAmelCase : Any = True
__UpperCAmelCase : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : List[str] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
if hasattr(self.model_tester , """num_hidden_states_types""" ):
__UpperCAmelCase : Any = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__UpperCAmelCase : Optional[int] = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCAmelCase ) )
__UpperCAmelCase : Tuple = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCAmelCase : List[Any] = outputs.hidden_states
__UpperCAmelCase : List[Any] = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# Swinv2 has a different seq_length
__UpperCAmelCase : List[str] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : Union[str, Any] = (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] , )
__UpperCAmelCase : int = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : str = reshaped_hidden_states[0].shape
__UpperCAmelCase : Any = (
reshaped_hidden_states[0].view(__UpperCAmelCase , __UpperCAmelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = (
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:
__UpperCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = 3
__UpperCAmelCase : 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)
)
__UpperCAmelCase : List[str] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__UpperCAmelCase : Union[str, Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__UpperCAmelCase : int = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Tuple = True
self.check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , (padded_height, padded_width) )
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Dict = SwinvaModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Tuple = _config_zero_init(__UpperCAmelCase )
for model_class in self.all_model_classes:
__UpperCAmelCase : List[Any] = model_class(config=__UpperCAmelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" 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 _A ( unittest.TestCase ):
@cached_property
def __A ( self ) -> int:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Tuple = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
__UpperCAmelCase )
__UpperCAmelCase : Tuple = self.default_image_processor
__UpperCAmelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__UpperCAmelCase : Any = image_processor(images=__UpperCAmelCase , return_tensors="""pt""" ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__UpperCAmelCase : Optional[int] = model(**__UpperCAmelCase )
# verify the logits
__UpperCAmelCase : int = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 16 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Any , snake_case__ : int , snake_case__ : Any , snake_case__ : Optional[int] , snake_case__ : List[str] , ) -> float:
'''simple docstring'''
lowerCAmelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("""Relative densities cannot be greater than one""" )
else:
lowerCAmelCase = 1 - (matter_density + radiation_density + dark_energy)
lowerCAmelCase = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
lowerCAmelCase = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
a = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 155 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
"processing_clap": ["ClapProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase =[
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapModel",
"ClapPreTrainedModel",
"ClapTextModel",
"ClapTextModelWithProjection",
"ClapAudioModel",
"ClapAudioModelWithProjection",
]
__UpperCAmelCase =["ClapFeatureExtractor"]
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
__UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 67 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
a__ : str = list(range(1_0, 0, -1))
print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")
| 54 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase_ ( UpperCamelCase , unittest.TestCase):
"""simple docstring"""
snake_case__ : Any = CLIPTokenizer
snake_case__ : Dict = CLIPTokenizerFast
snake_case__ : List[Any] = True
snake_case__ : Optional[Any] = {}
snake_case__ : Dict = False
def UpperCAmelCase_ ( self : Any ) -> Any:
super().setUp()
# fmt: off
__SCREAMING_SNAKE_CASE = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
__SCREAMING_SNAKE_CASE = dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
__SCREAMING_SNAKE_CASE = ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
__SCREAMING_SNAKE_CASE = {"unk_token": "<unk>"}
__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__SCREAMING_SNAKE_CASE = 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(UpperCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase__ ) )
def UpperCAmelCase_ ( self : List[Any] , **UpperCAmelCase__ : Tuple ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Any , **UpperCAmelCase__ : Optional[Any] ) -> List[str]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] , UpperCAmelCase__ : int ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = "lower newer"
__SCREAMING_SNAKE_CASE = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : int ) -> List[str]:
__SCREAMING_SNAKE_CASE = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__SCREAMING_SNAKE_CASE = "lower newer"
__SCREAMING_SNAKE_CASE = ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
__SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
__SCREAMING_SNAKE_CASE = [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , UpperCAmelCase__ )
@require_ftfy
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
__SCREAMING_SNAKE_CASE = tokenizer_s.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
__SCREAMING_SNAKE_CASE = "xa\u0303y" + " " + "x\xe3y"
__SCREAMING_SNAKE_CASE = tokenizer_s.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Test that the tokenization is identical on unicode of space type
__SCREAMING_SNAKE_CASE = [
"\u0009", # (horizontal tab, '\t')
"\u000B", # (vertical tab)
"\u000C", # (form feed)
"\u0020", # (space, ' ')
"\u200E", # (left-to-right mark):w
"\u200F", # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
__SCREAMING_SNAKE_CASE = tokenizer_s.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# Test that the tokenization is identical on unicode of line break type
__SCREAMING_SNAKE_CASE = [
"\u000A", # (line feed, '\n')
"\r\n", # (carriage return and line feed, '\r\n')
"\u000D", # (carriage return, '\r')
"\r", # (carriage return, '\r')
"\u000D", # (carriage return, '\r')
"\u2028", # (line separator)
"\u2029", # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
__SCREAMING_SNAKE_CASE = tokenizer_s.tokenize(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = tokenizer_r.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
__SCREAMING_SNAKE_CASE = F"""{text_of_1_token} {text_of_1_token}"""
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
UpperCAmelCase__ , use_fast=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = tokenizer_r(UpperCAmelCase__ , return_offsets_mapping=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCAmelCase__ ) + 1, len(UpperCAmelCase__ ) + 1 + len(UpperCAmelCase__ )) , )
__SCREAMING_SNAKE_CASE = F""" {text}"""
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
UpperCAmelCase__ , use_fast=UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = tokenizer_r(UpperCAmelCase__ , return_offsets_mapping=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(UpperCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCAmelCase__ ) + 1, 1 + len(UpperCAmelCase__ ) + 1 + len(UpperCAmelCase__ )) , )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
# Test related to the breaking change introduced in transformers v4.17.0
# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
with self.assertRaises(UpperCAmelCase__ ) as context:
self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer" )
self.assertTrue(
context.exception.args[0].startswith(
"The `backend_tokenizer` provided does not match the expected format." ) )
@require_ftfy
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
super().test_tokenization_python_rust_equals()
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
# CLIP always lower cases letters
pass
| 54 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __snake_case( _lowerCAmelCase ) -> Dict:
snake_case__ : str = []
snake_case__ : str = []
snake_case__ : Optional[int] = []
for rt in rc.restypes:
snake_case__ : Tuple = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
snake_case__ : int = {name: i for i, name in enumerate(_a )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
snake_case__ : Tuple = torch.tensor(
_a , dtype=torch.intaa , device=protein["""aatype"""].device , )
snake_case__ : List[str] = torch.tensor(
_a , dtype=torch.intaa , device=protein["""aatype"""].device , )
snake_case__ : Tuple = torch.tensor(
_a , dtype=torch.floataa , device=protein["""aatype"""].device , )
snake_case__ : Optional[Any] = protein["""aatype"""].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
snake_case__ : str = restype_atomaa_to_atomaa[protein_aatype]
snake_case__ : List[Any] = restype_atomaa_mask[protein_aatype]
snake_case__ : Tuple = residx_atomaa_mask
snake_case__ : Optional[Any] = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
snake_case__ : Any = restype_atomaa_to_atomaa[protein_aatype]
snake_case__ : Union[str, Any] = residx_atomaa_to_atomaa.long()
# create the corresponding mask
snake_case__ : Optional[Any] = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["""aatype"""].device )
for restype, restype_letter in enumerate(rc.restypes ):
snake_case__ : int = rc.restype_atoa[restype_letter]
snake_case__ : List[str] = rc.residue_atoms[restype_name]
for atom_name in atom_names:
snake_case__ : Tuple = rc.atom_order[atom_name]
snake_case__ : Any = 1
snake_case__ : List[str] = restype_atomaa_mask[protein_aatype]
snake_case__ : Dict = residx_atomaa_mask
return protein
def __snake_case( _lowerCAmelCase ) -> Optional[Any]:
snake_case__ : Optional[int] = tree_map(lambda _lowerCAmelCase : torch.tensor(_a , device=batch["""aatype"""].device ) , _a , np.ndarray )
snake_case__ : Optional[Any] = tensor_tree_map(lambda _lowerCAmelCase : np.array(_a ) , make_atomaa_masks(_a ) )
return out
| 364 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __snake_case( _lowerCAmelCase ) -> None:
assert all(row == sorted(_lowerCAmelCase , reverse=_lowerCAmelCase ) for row in grid )
assert all(list(_lowerCAmelCase ) == sorted(_lowerCAmelCase , reverse=_lowerCAmelCase ) for col in zip(*_lowerCAmelCase ) )
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : List[str] = 0
snake_case__ : str = len(_lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
snake_case__ : List[Any] = (left + right) // 2
snake_case__ : Tuple = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
snake_case__ : Tuple = mid + 1
else:
snake_case__ : Tuple = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(_lowerCAmelCase )
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = 0
snake_case__ : Optional[int] = len(grid[0] )
for i in range(len(_lowerCAmelCase ) ):
snake_case__ : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(_lowerCAmelCase ) * len(grid[0] )) - total
def __snake_case( _lowerCAmelCase ) -> int:
return len([number for row in grid for number in row if number < 0] )
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : List[Any] = 0
for row in grid:
for i, number in enumerate(_lowerCAmelCase ):
if number < 0:
total += len(_lowerCAmelCase ) - i
break
return total
def __snake_case( ) -> None:
from timeit import timeit
print("""Running benchmarks""" )
snake_case__ : int = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
snake_case__ : Tuple = timeit(f"{func}(grid=grid)" , setup=_lowerCAmelCase , number=500 )
print(f"{func}() took {time:0.4f} seconds" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 43 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class a__ ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class a__ ( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = ort.SessionOptions()
A__ = False
return options
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
A__ = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
A__ = "A red cat sitting on a park bench"
A__ = np.random.RandomState(0 )
A__ = pipe(
prompt=lowercase , image=lowercase , mask_image=lowercase , guidance_scale=7.5 , num_inference_steps=10 , generator=lowercase , output_type="np" , )
A__ = output.images
A__ = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
A__ = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
A__ = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
A__ = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=lowercase , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
A__ = "A red cat sitting on a park bench"
A__ = np.random.RandomState(0 )
A__ = pipe(
prompt=lowercase , image=lowercase , mask_image=lowercase , guidance_scale=7.5 , num_inference_steps=20 , generator=lowercase , output_type="np" , )
A__ = output.images
A__ = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
A__ = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 68 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
"""simple docstring"""
return word_by_signature[signature(__lowerCAmelCase )]
_a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_a = sorted({word.strip().lower() for word in data.splitlines()})
_a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
_a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 39 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=7 , __UpperCAmelCase=3 , __UpperCAmelCase=30 , __UpperCAmelCase=400 , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=[0.5, 0.5, 0.5] , __UpperCAmelCase=[0.5, 0.5, 0.5] , __UpperCAmelCase=True , __UpperCAmelCase=1 / 255 , __UpperCAmelCase=True , ) -> List[Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_a = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
_a = parent
_a = batch_size
_a = num_channels
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
_a = do_rescale
_a = rescale_factor
_a = do_pad
def _UpperCAmelCase ( self ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False ) -> str:
if not batched:
_a = image_inputs[0]
if isinstance(__UpperCAmelCase , Image.Image ):
_a , _a = image.size
else:
_a , _a = image.shape[1], image.shape[2]
if w < h:
_a = int(self.size['''shortest_edge'''] * h / w )
_a = self.size['''shortest_edge''']
elif w > h:
_a = self.size['''shortest_edge''']
_a = int(self.size['''shortest_edge'''] * w / h )
else:
_a = self.size['''shortest_edge''']
_a = self.size['''shortest_edge''']
else:
_a = []
for image in image_inputs:
_a , _a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_a = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[0] )[0]
_a = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowerCamelCase ( a__ , unittest.TestCase ):
'''simple docstring'''
A_ : int = ConditionalDetrImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> List[str]:
_a = ConditionalDetrImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Any:
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_std''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''size''' ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
_a = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__UpperCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> List[str]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _UpperCAmelCase ( self ) -> Dict:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _UpperCAmelCase ( self ) -> List[str]:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _UpperCAmelCase ( self ) -> Dict:
# prepare image and target
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_a = json.loads(f.read() )
_a = {'''image_id''': 39769, '''annotations''': target}
# encode them
_a = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
_a = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
_a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , __UpperCAmelCase )
_a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
_a = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __UpperCAmelCase ) )
# verify boxes
_a = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __UpperCAmelCase )
_a = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
_a = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __UpperCAmelCase ) )
# verify is_crowd
_a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __UpperCAmelCase ) )
# verify class_labels
_a = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __UpperCAmelCase ) )
# verify orig_size
_a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __UpperCAmelCase ) )
# verify size
_a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __UpperCAmelCase ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
# prepare image, target and masks_path
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_a = json.loads(f.read() )
_a = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
_a = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_a = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
_a = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , masks_path=__UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
_a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , __UpperCAmelCase )
_a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
_a = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __UpperCAmelCase ) )
# verify boxes
_a = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __UpperCAmelCase )
_a = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
_a = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __UpperCAmelCase ) )
# verify is_crowd
_a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __UpperCAmelCase ) )
# verify class_labels
_a = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __UpperCAmelCase ) )
# verify masks
_a = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __UpperCAmelCase )
# verify orig_size
_a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __UpperCAmelCase ) )
# verify size
_a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __UpperCAmelCase ) )
| 366 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> Dict:
_a = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
_a = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
sd_pipe.set_scheduler('''sample_euler''' )
_a = '''A painting of a squirrel eating a burger'''
_a = torch.manual_seed(0 )
_a = sd_pipe([prompt] , generator=__UpperCAmelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
_a = output.images
_a = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_a = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
_a = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
_a = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
sd_pipe.set_scheduler('''sample_euler''' )
_a = '''A painting of a squirrel eating a burger'''
_a = torch.manual_seed(0 )
_a = sd_pipe([prompt] , generator=__UpperCAmelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
_a = output.images
_a = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_a = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def _UpperCAmelCase ( self ) -> str:
_a = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
_a = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
_a = '''A painting of a squirrel eating a burger'''
_a = torch.manual_seed(0 )
_a = sd_pipe(
[prompt] , generator=__UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=__UpperCAmelCase , )
_a = output.images
_a = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_a = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 153 | 0 |
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