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import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Union[str, Any] ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"] , model_result["ss"] ):
SCREAMING_SNAKE_CASE = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(__lowerCamelCase )
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__lowerCamelCase , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = "sgugger/tiny-distilbert-classification"
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , only_pretrain_model=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__lowerCamelCase , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase , [config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = "patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase , configs=[config] )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU" ) ) == 0 , "Cannot do xla on CPU." )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , use_xla=__lowerCamelCase , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__lowerCamelCase , save_to_csv=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__lowerCamelCase , "inf_time.csv" ) , inference_memory_csv_file=os.path.join(__lowerCamelCase , "inf_mem.csv" ) , env_info_csv_file=os.path.join(__lowerCamelCase , "env.csv" ) , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(__lowerCamelCase , "inf_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , "inf_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , "env.csv" ) ).exists() )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(__lowerCamelCase : Dict ):
self.assertTrue(hasattr(__lowerCamelCase , "sequential" ) )
self.assertTrue(hasattr(__lowerCamelCase , "cumulative" ) )
self.assertTrue(hasattr(__lowerCamelCase , "current" ) )
self.assertTrue(hasattr(__lowerCamelCase , "total" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__lowerCamelCase , "log.txt" ) , log_print=__lowerCamelCase , trace_memory_line_by_line=__lowerCamelCase , eager_mode=__lowerCamelCase , multi_process=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = TensorFlowBenchmark(__lowerCamelCase )
SCREAMING_SNAKE_CASE = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(__lowerCamelCase , "log.txt" ) ).exists() ) | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(A__ , A__ , bias=A__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Tuple , A__ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE = {}
for old_key in state_dict.keys():
SCREAMING_SNAKE_CASE = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts.0" , F"ffn.experts.expert_{expert_idx}" )
else:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
SCREAMING_SNAKE_CASE = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
SCREAMING_SNAKE_CASE = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("final_layer_norm" , "ff_layer_norm" )
SCREAMING_SNAKE_CASE = state_dict[old_key]
return new_dict
def __a ( A__ : List[str] , A__ : List[Any] , A__ : str , A__ : Union[str, Any] , A__ : str = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
os.makedirs(A__ , exist_ok=A__ )
for expert in range(A__ ):
SCREAMING_SNAKE_CASE = switch_checkpoint_path + F"-rank-{expert}.pt"
if os.path.isfile(A__ ):
SCREAMING_SNAKE_CASE = torch.load(A__ )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = os.path.join(
A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
torch.save(A__ , A__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A__ )[0]].dtype )
# Add the last block
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
SCREAMING_SNAKE_CASE = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A__ ) == 1:
SCREAMING_SNAKE_CASE = os.path.join(A__ , A__ )
torch.save(A__ , A__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A__ , A__ )
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(A__ ):
SCREAMING_SNAKE_CASE = weights_name.replace(".bin" , F"-{idx+1:05d}-of-{len(A__ ):05d}.bin" )
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
for key in shard:
SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE = {"total_size": total_size}
SCREAMING_SNAKE_CASE = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A__ , A__ ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(A__ , indent=2 , sort_keys=A__ ) + "\n"
f.write(A__ )
return metadata, index
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : Optional[int] = parser.parse_args()
__A , __A : Union[str, Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
__A : Any = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : Any = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path) | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : List[str] = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCamelCase , __lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self : Dict , **__lowerCamelCase : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).tokenizer
def _snake_case ( self : List[Any] , **__lowerCamelCase : Optional[Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).image_processor
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCamelCase ):
processor()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 698 | 1 |
def __a ( A__ : int ):
SCREAMING_SNAKE_CASE = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4)) | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
pass
def __a ( A__ : str ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A : Tuple = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model=__lowerCamelCase , tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
SCREAMING_SNAKE_CASE = "What is the placebo?"
SCREAMING_SNAKE_CASE = [
{
"image": load_image(__lowerCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = dqa_pipeline(__lowerCamelCase , top_k=2 )
self.assertEqual(
__lowerCamelCase , [
[
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "How many cats are there?"
SCREAMING_SNAKE_CASE = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , words=__lowerCamelCase , boxes=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _snake_case ( self : List[Any] ):
pass | 698 | 1 |
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : int = 0 ):
SCREAMING_SNAKE_CASE = key
def _snake_case ( self : str , __lowerCamelCase : str , __lowerCamelCase : int ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(__lowerCamelCase ) ^ key ) for ch in content]
def _snake_case ( self : int , __lowerCamelCase : str , __lowerCamelCase : int ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(__lowerCamelCase ) ^ key ) for ch in content]
def _snake_case ( self : Tuple , __lowerCamelCase : str , __lowerCamelCase : int = 0 ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE = ""
for ch in content:
ans += chr(ord(__lowerCamelCase ) ^ key )
return ans
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : int = 0 ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE = ""
for ch in content:
ans += chr(ord(__lowerCamelCase ) ^ key )
return ans
def _snake_case ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : int = 0 ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
try:
with open(__lowerCamelCase ) as fin, open("encrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(__lowerCamelCase , __lowerCamelCase ) )
except OSError:
return False
return True
def _snake_case ( self : Tuple , __lowerCamelCase : str , __lowerCamelCase : int ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )
try:
with open(__lowerCamelCase ) as fin, open("decrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(__lowerCamelCase , __lowerCamelCase ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful") | 698 |
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "facebook/bart-large-mnli"
lowerCamelCase__ = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
lowerCamelCase__ = "text_classifier"
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSequenceClassification
lowerCamelCase__ = ["text", ["text"]]
lowerCamelCase__ = ["text"]
def _snake_case ( self : Optional[Any] ):
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__lowerCamelCase ) , [f"This example is {label}" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id] | 698 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : 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 _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "informer"
lowerCamelCase__ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self : Dict , __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 = 64 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , __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 = 100 , __lowerCamelCase : float = 0.02 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : str = "prob" , __lowerCamelCase : int = 5 , __lowerCamelCase : bool = True , **__lowerCamelCase : List[str] , ):
# time series specific configuration
SCREAMING_SNAKE_CASE = prediction_length
SCREAMING_SNAKE_CASE = context_length or prediction_length
SCREAMING_SNAKE_CASE = distribution_output
SCREAMING_SNAKE_CASE = loss
SCREAMING_SNAKE_CASE = input_size
SCREAMING_SNAKE_CASE = num_time_features
SCREAMING_SNAKE_CASE = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
SCREAMING_SNAKE_CASE = scaling
SCREAMING_SNAKE_CASE = num_dynamic_real_features
SCREAMING_SNAKE_CASE = num_static_real_features
SCREAMING_SNAKE_CASE = 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`" )
SCREAMING_SNAKE_CASE = cardinality
else:
SCREAMING_SNAKE_CASE = [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`" )
SCREAMING_SNAKE_CASE = embedding_dimension
else:
SCREAMING_SNAKE_CASE = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
SCREAMING_SNAKE_CASE = num_parallel_samples
# Transformer architecture configuration
SCREAMING_SNAKE_CASE = input_size * len(self.lags_sequence ) + self._number_of_features
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = use_cache
# Informer
SCREAMING_SNAKE_CASE = attention_type
SCREAMING_SNAKE_CASE = sampling_factor
SCREAMING_SNAKE_CASE = distil
super().__init__(is_encoder_decoder=__lowerCamelCase , **__lowerCamelCase )
@property
def _snake_case ( self : Optional[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
) | 698 |
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __a ( A__ : str=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("test" )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=A__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
SCREAMING_SNAKE_CASE = script_name
else:
SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
SCREAMING_SNAKE_CASE = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def __a ( ):
SCREAMING_SNAKE_CASE = test_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main() | 698 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[str] ):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
return Dataset.from_dict(__lowerCamelCase )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self._create_example_records()
SCREAMING_SNAKE_CASE = Dataset.from_list(__lowerCamelCase )
self.assertListEqual(dset.column_names , ["col_1", "col_2"] )
for i, r in enumerate(__lowerCamelCase ):
self.assertDictEqual(__lowerCamelCase , example_records[i] )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self._create_example_records()
SCREAMING_SNAKE_CASE = Dataset.from_list(__lowerCamelCase )
SCREAMING_SNAKE_CASE = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def _snake_case ( self : List[str] ): # checks what happens with missing columns
SCREAMING_SNAKE_CASE = [{"col_1": 1}, {"col_2": "x"}]
SCREAMING_SNAKE_CASE = Dataset.from_list(__lowerCamelCase )
self.assertDictEqual(dset[0] , {"col_1": 1} )
self.assertDictEqual(dset[1] , {"col_1": None} ) # NB: first record is used for columns
def _snake_case ( self : Dict ): # checks if the type can be inferred from the second record
SCREAMING_SNAKE_CASE = [{"col_1": []}, {"col_1": [1, 2]}]
SCREAMING_SNAKE_CASE = Dataset.from_list(__lowerCamelCase )
self.assertEqual(dset.info.features["col_1"] , Sequence(Value("int64" ) ) )
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Dataset.from_list([] )
self.assertEqual(len(__lowerCamelCase ) , 0 )
self.assertListEqual(dset.column_names , [] ) | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[int] = logging.get_logger(__name__)
__A : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : Tuple = {
'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'
},
}
__A : Dict = {'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ):
SCREAMING_SNAKE_CASE = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE = bs[:]
SCREAMING_SNAKE_CASE = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def __a ( A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE = char
return pairs
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any]="replace" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : List[Any]="<mask>" , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE = bytes_to_unicode()
SCREAMING_SNAKE_CASE = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case ( self : Dict ):
return len(self.encoder )
def _snake_case ( self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = bigram
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = word
return word
def _snake_case ( self : str , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for token in re.findall(self.pat , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : List[str] , __lowerCamelCase : str ):
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Tuple ):
return self.decoder.get(__lowerCamelCase )
def _snake_case ( self : Tuple , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = "".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
SCREAMING_SNAKE_CASE = 0
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _snake_case ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 : Any , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE = " " + text
return (text, kwargs)
def _snake_case ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case ( self : Tuple , __lowerCamelCase : "Conversation" ):
SCREAMING_SNAKE_CASE = []
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(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
SCREAMING_SNAKE_CASE = 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 | 698 | 1 |
def __a ( A__ : list , A__ : int = 0 ):
SCREAMING_SNAKE_CASE = length or len(A__ )
SCREAMING_SNAKE_CASE = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE = True
return list_data if not swapped else bubble_sort(A__ , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE_CASE = (-b + sqrt(A__ )) / (2 * a)
SCREAMING_SNAKE_CASE = (-b - sqrt(A__ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main() | 698 | 1 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = GPTSanJapaneseTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = {"do_clean_text": False, "add_prefix_space": False}
def _snake_case ( self : Dict ):
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
# fmt: on
SCREAMING_SNAKE_CASE = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀
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["emoji_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.emoji_file , "w" ) as emoji_writer:
emoji_writer.write(json.dumps(__lowerCamelCase ) )
def _snake_case ( self : List[Any] , **__lowerCamelCase : Dict ):
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _snake_case ( self : Optional[Any] , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = "こんにちは、世界。 \nこんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE = "こんにちは、世界。 \nこんばんは、世界。😀"
return input_text, output_text
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.get_input_output_texts(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase , clean_up_tokenization_spaces=__lowerCamelCase )
return text, ids
def _snake_case ( self : str ):
pass # TODO add if relevant
def _snake_case ( self : List[Any] ):
pass # TODO add if relevant
def _snake_case ( self : List[Any] ):
pass # TODO add if relevant
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE = "こんにちは、世界。 こんばんは、㔺界。"
SCREAMING_SNAKE_CASE = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE = "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
SCREAMING_SNAKE_CASE = "こんにちは、、、、世界。こんばんは、、、、世界。"
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
@slow
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
SCREAMING_SNAKE_CASE = "こんにちは、世界。"
SCREAMING_SNAKE_CASE = "こんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE = "こんにちは、世界。こんばんは、世界。😀"
SCREAMING_SNAKE_CASE = tokenizer.encode(prefix_text + input_text )
SCREAMING_SNAKE_CASE = tokenizer.encode("" , prefix_text=prefix_text + input_text )
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , prefix_text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
SCREAMING_SNAKE_CASE = "こんにちは、世界。"
SCREAMING_SNAKE_CASE = "こんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE = len(tokenizer.encode(__lowerCamelCase ) ) - 2
SCREAMING_SNAKE_CASE = len(tokenizer.encode(__lowerCamelCase ) ) - 2
SCREAMING_SNAKE_CASE = [1] + [0] * (len_prefix + len_text + 1)
SCREAMING_SNAKE_CASE = [1] * (len_prefix + len_text + 1) + [0]
SCREAMING_SNAKE_CASE = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
SCREAMING_SNAKE_CASE = tokenizer(prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE = tokenizer("" , prefix_text=prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , prefix_text=__lowerCamelCase ).token_type_ids
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
SCREAMING_SNAKE_CASE = tokenizer.encode("あンいワ" )
SCREAMING_SNAKE_CASE = tokenizer.encode("" , prefix_text="あンいワ" )
SCREAMING_SNAKE_CASE = tokenizer.encode("いワ" , prefix_text="あン" )
self.assertEqual(tokenizer.decode(__lowerCamelCase ) , tokenizer.decode(__lowerCamelCase ) )
self.assertEqual(tokenizer.decode(__lowerCamelCase ) , tokenizer.decode(__lowerCamelCase ) )
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
SCREAMING_SNAKE_CASE = [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_encode_plus(__lowerCamelCase , padding=__lowerCamelCase )
# fmt: off
SCREAMING_SNAKE_CASE = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
SCREAMING_SNAKE_CASE = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
SCREAMING_SNAKE_CASE = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , __lowerCamelCase )
self.assertListEqual(x_token.token_type_ids , __lowerCamelCase )
self.assertListEqual(x_token.attention_mask , __lowerCamelCase )
self.assertListEqual(x_token_a.input_ids , __lowerCamelCase )
self.assertListEqual(x_token_a.token_type_ids , __lowerCamelCase )
self.assertListEqual(x_token_a.attention_mask , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def _snake_case ( self : Tuple ):
# tokenizer has no padding token
pass | 698 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Dict ):
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 _snake_case ( self : int ):
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 _snake_case ( self : List[str] ):
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 _snake_case ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
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 _snake_case ( self : Optional[int] ):
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 | 698 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def __a ( A__ : str , A__ : str , A__ : str , A__ : PreTrainedTokenizer , A__ : int , A__ : Optional[int] = None , ):
SCREAMING_SNAKE_CASE = {}
if train_file is not None:
SCREAMING_SNAKE_CASE = [train_file]
if eval_file is not None:
SCREAMING_SNAKE_CASE = [eval_file]
if test_file is not None:
SCREAMING_SNAKE_CASE = [test_file]
SCREAMING_SNAKE_CASE = datasets.load_dataset("csv" , data_files=A__ )
SCREAMING_SNAKE_CASE = list(ds[list(files.keys() )[0]].features.keys() )
SCREAMING_SNAKE_CASE = features_name.pop(A__ )
SCREAMING_SNAKE_CASE = list(set(ds[list(files.keys() )[0]][label_name] ) )
SCREAMING_SNAKE_CASE = {label: i for i, label in enumerate(A__ )}
SCREAMING_SNAKE_CASE = tokenizer.model_input_names
SCREAMING_SNAKE_CASE = {}
if len(A__ ) == 1:
for k in files.keys():
SCREAMING_SNAKE_CASE = ds[k].map(
lambda A__ : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=A__ , max_length=A__ , padding="max_length" ) , batched=A__ , )
elif len(A__ ) == 2:
for k in files.keys():
SCREAMING_SNAKE_CASE = ds[k].map(
lambda A__ : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=A__ , max_length=A__ , padding="max_length" , ) , batched=A__ , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
SCREAMING_SNAKE_CASE = {k: v for k, v in ex.items() if k in input_names}
SCREAMING_SNAKE_CASE = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
SCREAMING_SNAKE_CASE = {k: v for k, v in ex.items() if k in input_names}
SCREAMING_SNAKE_CASE = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
SCREAMING_SNAKE_CASE = {k: v for k, v in ex.items() if k in input_names}
SCREAMING_SNAKE_CASE = labelaid[ex[label_name]]
yield (d, label)
SCREAMING_SNAKE_CASE = (
tf.data.Dataset.from_generator(
A__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
SCREAMING_SNAKE_CASE = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
SCREAMING_SNAKE_CASE = (
tf.data.Dataset.from_generator(
A__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
SCREAMING_SNAKE_CASE = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
SCREAMING_SNAKE_CASE = (
tf.data.Dataset.from_generator(
A__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
SCREAMING_SNAKE_CASE = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
__A : Optional[Any] = logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(metadata={"help": "Which column contains the label"} )
lowerCamelCase__ = field(default=__snake_case , metadata={"help": "The path of the training file"} )
lowerCamelCase__ = field(default=__snake_case , metadata={"help": "The path of the development file"} )
lowerCamelCase__ = field(default=__snake_case , metadata={"help": "The path of the test file"} )
lowerCamelCase__ = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowerCamelCase__ = field(
default=__snake_case , metadata={"help": "Overwrite the cached training and evaluation sets"} )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCamelCase__ = field(
default=__snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCamelCase__ = field(
default=__snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCamelCase__ = field(default=__snake_case , metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCamelCase__ = field(
default=__snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
def __a ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(
F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, "
F"16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=A__ , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(A__ ) , labelaid=A__ , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="text-classification" , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
SCREAMING_SNAKE_CASE = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(".bin" in model_args.model_name_or_path ) , config=A__ , cache_dir=model_args.cache_dir , )
def compute_metrics(A__ : EvalPrediction ) -> Dict:
SCREAMING_SNAKE_CASE = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
SCREAMING_SNAKE_CASE = TFTrainer(
model=A__ , args=A__ , train_dataset=A__ , eval_dataset=A__ , compute_metrics=A__ , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
SCREAMING_SNAKE_CASE = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
SCREAMING_SNAKE_CASE = trainer.evaluate()
SCREAMING_SNAKE_CASE = os.path.join(training_args.output_dir , "eval_results.txt" )
with open(A__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
results.update(A__ )
return results
if __name__ == "__main__":
main() | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 1 |
import re
from filelock import FileLock
try:
import nltk
__A : List[str] = True
except (ImportError, ModuleNotFoundError):
__A : List[str] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __a ( A__ : str ):
re.sub("<n>" , "" , A__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(A__ ) ) | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 698 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(A__ , A__ ) ) )
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
if dataset.ndim != value_array.ndim:
SCREAMING_SNAKE_CASE = (
"Wrong input data's dimensions... "
F"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
)
raise ValueError(A__ )
try:
if dataset.shape[1] != value_array.shape[1]:
SCREAMING_SNAKE_CASE = (
"Wrong input data's shape... "
F"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
)
raise ValueError(A__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError("Wrong shape" )
if dataset.dtype != value_array.dtype:
SCREAMING_SNAKE_CASE = (
"Input data have different datatype... "
F"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
)
raise TypeError(A__ )
SCREAMING_SNAKE_CASE = []
for value in value_array:
SCREAMING_SNAKE_CASE = euclidean(A__ , dataset[0] )
SCREAMING_SNAKE_CASE = dataset[0].tolist()
for dataset_value in dataset[1:]:
SCREAMING_SNAKE_CASE = euclidean(A__ , A__ )
if dist > temp_dist:
SCREAMING_SNAKE_CASE = temp_dist
SCREAMING_SNAKE_CASE = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
return np.dot(A__ , A__ ) / (norm(A__ ) * norm(A__ ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 |
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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "resnet"
lowerCamelCase__ = ["basic", "bottleneck"]
def __init__( self : Optional[Any] , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=64 , __lowerCamelCase : str=[256, 512, 1024, 2048] , __lowerCamelCase : str=[3, 4, 6, 3] , __lowerCamelCase : Optional[int]="bottleneck" , __lowerCamelCase : int="relu" , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = downsample_in_first_stage
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = version.parse("1.11" )
@property
def _snake_case ( self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : Optional[int] ):
return 1e-3 | 698 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = GPTaTokenizer
lowerCamelCase__ = GPTaTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = {"add_prefix_space": True}
lowerCamelCase__ = False
def _snake_case ( self : Optional[Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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(__lowerCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__lowerCamelCase ) )
def _snake_case ( self : List[str] , **__lowerCamelCase : List[str] ):
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _snake_case ( self : Tuple , **__lowerCamelCase : List[Any] ):
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = "lower newer"
return input_text, output_text
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
# Testing tokenization
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing the unknown token
SCREAMING_SNAKE_CASE = tokens + [rust_tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def _snake_case ( self : int , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Tuple ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _snake_case ( self : Any , __lowerCamelCase : Union[str, Any]=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
# Simple input
SCREAMING_SNAKE_CASE = "This is a simple input"
SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
SCREAMING_SNAKE_CASE = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
SCREAMING_SNAKE_CASE = "This is a simple input"
SCREAMING_SNAKE_CASE = ["This is a simple input looooooooong", "This is a simple input"]
SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
SCREAMING_SNAKE_CASE = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
SCREAMING_SNAKE_CASE = tokenizer.pad_token_id
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding="max_length" , max_length=30 , return_tensors="np" )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding=__lowerCamelCase , truncate=__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = tokenizer(*__lowerCamelCase , padding="max_length" , max_length=60 , return_tensors="np" )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding=__lowerCamelCase , truncate=__lowerCamelCase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = "$$$"
SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowerCamelCase , add_bos_token=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "This is a simple input"
SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
SCREAMING_SNAKE_CASE = tokenizer.bos_token_id
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase )
self.assertEqual(out_s.input_ids[0] , __lowerCamelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
SCREAMING_SNAKE_CASE = tokenizer.decode(out_s.input_ids )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowerCamelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def _snake_case ( self : Optional[int] ):
pass
def _snake_case ( self : str ):
# TODO: change to self.get_tokenizers() when the fast version is implemented
SCREAMING_SNAKE_CASE = [self.get_tokenizer(do_lower_case=__lowerCamelCase , add_bos_token=__lowerCamelCase )]
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE = "Encode this."
SCREAMING_SNAKE_CASE = "This one too please."
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
encoded_sequence += tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.encode_plus(
__lowerCamelCase , __lowerCamelCase , add_special_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , )
SCREAMING_SNAKE_CASE = encoded_sequence_dict["input_ids"]
SCREAMING_SNAKE_CASE = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
SCREAMING_SNAKE_CASE = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCamelCase )
]
SCREAMING_SNAKE_CASE = [x for x in filtered_sequence if x is not None]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "A photo of a cat"
SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [2, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("test_opt" )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("./test_opt" )
SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [2, 250, 1345, 9, 10, 4758] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , use_slow=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "A photo of a cat"
SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
# Same as above
self.assertEqual(__lowerCamelCase , [2, 250, 1345, 9, 10, 4758] )
@unittest.skip("This test is failing because of a bug in the fast tokenizer" )
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "bos"
SCREAMING_SNAKE_CASE = tokenizer.get_vocab()["bos"]
SCREAMING_SNAKE_CASE = "A photo of a cat"
SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
# We changed the bos token
self.assertEqual(__lowerCamelCase , [31957, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained("./tok" )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("./tok" )
self.assertTrue(tokenizer.is_fast )
SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [31957, 250, 1345, 9, 10, 4758] ) | 698 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "imagegpt"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , __lowerCamelCase : Any=512 + 1 , __lowerCamelCase : str=32 * 32 , __lowerCamelCase : Any=512 , __lowerCamelCase : Optional[int]=24 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]="quick_gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[Any]=1e-5 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : int=False , __lowerCamelCase : int=False , **__lowerCamelCase : Union[str, Any] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scale_attn_weights
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE = tie_word_embeddings
super().__init__(tie_word_embeddings=__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def _snake_case ( self : Optional[int] , __lowerCamelCase : "FeatureExtractionMixin" , __lowerCamelCase : int = 1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , ):
SCREAMING_SNAKE_CASE = self._generate_dummy_images(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dict(preprocessor(images=__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return inputs | 698 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 698 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@slow
@require_torch
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE = 4
def _map_to_encoder_decoder_inputs(__lowerCamelCase : str ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE = tokenizer(batch["article"] , padding="max_length" , truncation=__lowerCamelCase , max_length=512 )
SCREAMING_SNAKE_CASE = tokenizer(batch["highlights"] , padding="max_length" , truncation=__lowerCamelCase , max_length=128 )
SCREAMING_SNAKE_CASE = inputs.input_ids
SCREAMING_SNAKE_CASE = inputs.attention_mask
SCREAMING_SNAKE_CASE = outputs.input_ids
SCREAMING_SNAKE_CASE = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
SCREAMING_SNAKE_CASE = outputs.attention_mask
assert all(len(__lowerCamelCase ) == 512 for x in inputs.input_ids )
assert all(len(__lowerCamelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = pred.label_ids
SCREAMING_SNAKE_CASE = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__lowerCamelCase ) )] ) / len(__lowerCamelCase )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE = SeqaSeqTrainingArguments(
output_dir=__lowerCamelCase , per_device_train_batch_size=__lowerCamelCase , per_device_eval_batch_size=__lowerCamelCase , predict_with_generate=__lowerCamelCase , evaluation_strategy="steps" , do_train=__lowerCamelCase , do_eval=__lowerCamelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=__lowerCamelCase , args=__lowerCamelCase , compute_metrics=_compute_metrics , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , tokenizer=__lowerCamelCase , )
# start training
trainer.train() | 698 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def __a ( A__ : Optional[Any] , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = checkpoint
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.conv_in.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.conv_in.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.conv_out.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.conv_out.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.norm_out.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["encoder.norm_out.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.conv_in.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.conv_in.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.conv_out.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.conv_out.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.norm_out.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["decoder.norm_out.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["quant_conv.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["quant_conv.bias"]
SCREAMING_SNAKE_CASE = vae_state_dict["post_quant_conv.weight"]
SCREAMING_SNAKE_CASE = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
SCREAMING_SNAKE_CASE = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "encoder.down" in layer} )
SCREAMING_SNAKE_CASE = {
layer_id: [key for key in vae_state_dict if F"down.{layer_id}" in key] for layer_id in range(A__ )
}
# Retrieves the keys for the decoder up blocks only
SCREAMING_SNAKE_CASE = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "decoder.up" in layer} )
SCREAMING_SNAKE_CASE = {
layer_id: [key for key in vae_state_dict if F"up.{layer_id}" in key] for layer_id in range(A__ )
}
for i in range(A__ ):
SCREAMING_SNAKE_CASE = [key for key in down_blocks[i] if F"down.{i}" in key and F"down.{i}.downsample" not in key]
if F"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
SCREAMING_SNAKE_CASE = vae_state_dict.pop(
F"encoder.down.{i}.downsample.conv.weight" )
SCREAMING_SNAKE_CASE = vae_state_dict.pop(
F"encoder.down.{i}.downsample.conv.bias" )
SCREAMING_SNAKE_CASE = renew_vae_resnet_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": F"down.{i}.block", "new": F"down_blocks.{i}.resnets"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
SCREAMING_SNAKE_CASE = [key for key in vae_state_dict if "encoder.mid.block" in key]
SCREAMING_SNAKE_CASE = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE = [key for key in mid_resnets if F"encoder.mid.block_{i}" in key]
SCREAMING_SNAKE_CASE = renew_vae_resnet_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": F"mid.block_{i}", "new": F"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
SCREAMING_SNAKE_CASE = [key for key in vae_state_dict if "encoder.mid.attn" in key]
SCREAMING_SNAKE_CASE = renew_vae_attention_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
conv_attn_to_linear(A__ )
for i in range(A__ ):
SCREAMING_SNAKE_CASE = num_up_blocks - 1 - i
SCREAMING_SNAKE_CASE = [
key for key in up_blocks[block_id] if F"up.{block_id}" in key and F"up.{block_id}.upsample" not in key
]
if F"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
SCREAMING_SNAKE_CASE = vae_state_dict[
F"decoder.up.{block_id}.upsample.conv.weight"
]
SCREAMING_SNAKE_CASE = vae_state_dict[
F"decoder.up.{block_id}.upsample.conv.bias"
]
SCREAMING_SNAKE_CASE = renew_vae_resnet_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": F"up.{block_id}.block", "new": F"up_blocks.{i}.resnets"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
SCREAMING_SNAKE_CASE = [key for key in vae_state_dict if "decoder.mid.block" in key]
SCREAMING_SNAKE_CASE = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE = [key for key in mid_resnets if F"decoder.mid.block_{i}" in key]
SCREAMING_SNAKE_CASE = renew_vae_resnet_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": F"mid.block_{i}", "new": F"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
SCREAMING_SNAKE_CASE = [key for key in vae_state_dict if "decoder.mid.attn" in key]
SCREAMING_SNAKE_CASE = renew_vae_attention_paths(A__ )
SCREAMING_SNAKE_CASE = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
conv_attn_to_linear(A__ )
return new_checkpoint
def __a ( A__ : str , A__ : str , ):
# Only support V1
SCREAMING_SNAKE_CASE = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" )
SCREAMING_SNAKE_CASE = io.BytesIO(r.content )
SCREAMING_SNAKE_CASE = OmegaConf.load(A__ )
SCREAMING_SNAKE_CASE = 512
SCREAMING_SNAKE_CASE = "cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors" ):
from safetensors import safe_open
SCREAMING_SNAKE_CASE = {}
with safe_open(A__ , framework="pt" , device="cpu" ) as f:
for key in f.keys():
SCREAMING_SNAKE_CASE = f.get_tensor(A__ )
else:
SCREAMING_SNAKE_CASE = torch.load(A__ , map_location=A__ )["state_dict"]
# Convert the VAE model.
SCREAMING_SNAKE_CASE = create_vae_diffusers_config(A__ , image_size=A__ )
SCREAMING_SNAKE_CASE = custom_convert_ldm_vae_checkpoint(A__ , A__ )
SCREAMING_SNAKE_CASE = AutoencoderKL(**A__ )
vae.load_state_dict(A__ )
vae.save_pretrained(A__ )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
__A : int = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) | 698 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "num_attention_heads" ) )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Dict=3 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : int=2 , __lowerCamelCase : List[Any]=1 , __lowerCamelCase : Optional[Any]=16 , __lowerCamelCase : Tuple=[128, 256, 384] , __lowerCamelCase : int=[4, 6, 8] , __lowerCamelCase : Tuple=[2, 3, 4] , __lowerCamelCase : List[str]=[16, 16, 16] , __lowerCamelCase : int=0 , __lowerCamelCase : List[Any]=[2, 2, 2] , __lowerCamelCase : List[str]=[2, 2, 2] , __lowerCamelCase : List[str]=0.02 , __lowerCamelCase : Any=True , __lowerCamelCase : Dict=True , __lowerCamelCase : int=2 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = kernel_size
SCREAMING_SNAKE_CASE = stride
SCREAMING_SNAKE_CASE = padding
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : List[str] ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _snake_case ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : str , __lowerCamelCase : Any ):
SCREAMING_SNAKE_CASE = LevitModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
SCREAMING_SNAKE_CASE = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _snake_case ( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = LevitForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = LevitModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Any ):
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 : Any ):
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def _snake_case ( self : Tuple ):
pass
@unittest.skip(reason="Levit does not output attentions" )
def _snake_case ( self : Tuple ):
pass
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : List[Any] ):
def check_hidden_states_output(__lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = len(self.model_tester.depths ) + 1
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.model_tester.image_size, self.model_tester.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
SCREAMING_SNAKE_CASE = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : int ):
pass
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : int=False ):
SCREAMING_SNAKE_CASE = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _snake_case ( self : List[Any] ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowerCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__lowerCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}" ):
SCREAMING_SNAKE_CASE = problem_type["title"]
SCREAMING_SNAKE_CASE = problem_type["num_labels"]
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
SCREAMING_SNAKE_CASE = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def _snake_case ( self : List[Any] ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = LevitModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Dict ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([1.0_448, -0.3_745, -1.8_317] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1e-4 ) ) | 698 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = XLMTokenizer
lowerCamelCase__ = False
def _snake_case ( self : Union[str, Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
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" ) as fp:
fp.write(json.dumps(__lowerCamelCase ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(__lowerCamelCase ) )
def _snake_case ( self : Tuple , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = "lower newer"
return input_text, output_text
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = XLMTokenizer(self.vocab_file , self.merges_file )
SCREAMING_SNAKE_CASE = "lower"
SCREAMING_SNAKE_CASE = ["low", "er</w>"]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = tokens + ["<unk>"]
SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )
SCREAMING_SNAKE_CASE = tokenizer.encode("sequence builders" , add_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.encode("multi-sequence build" , add_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1] | 698 |
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 _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
return None
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : int ):
return None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : Optional[int] ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(__lowerCamelCase ) )
vocab_file.flush()
SCREAMING_SNAKE_CASE = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
SCREAMING_SNAKE_CASE = BertModel(BertConfig(vocab_size=len(__lowerCamelCase ) ) )
model.save_pretrained(__lowerCamelCase )
self._test_export(__lowerCamelCase , "pt" , 12 , __lowerCamelCase )
@require_tf
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(Path(__lowerCamelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(__lowerCamelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def _snake_case ( self : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , **__lowerCamelCase : List[str] ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE = Path(__lowerCamelCase ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
return path
except Exception as e:
self.fail(__lowerCamelCase )
@require_torch
@require_tokenizers
@slow
def _snake_case ( self : Dict ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "pt" )
@require_tf
@require_tokenizers
@slow
def _snake_case ( self : int ):
from transformers import TFBertModel
SCREAMING_SNAKE_CASE = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "tf" )
def _snake_case ( self : int , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = FeatureExtractionPipeline(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = infer_shapes(__lowerCamelCase , __lowerCamelCase )
# Assert all variables are present
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __lowerCamelCase )
self.assertSequenceEqual(variable_names[3:] , __lowerCamelCase )
# 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 _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask", "token_type_ids"]
SCREAMING_SNAKE_CASE = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__lowerCamelCase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__lowerCamelCase ) , set(__lowerCamelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__lowerCamelCase , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncNonContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__lowerCamelCase ) , 1 )
self.assertEqual(len(__lowerCamelCase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() ) | 698 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = None
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = 2
@register_to_config
def __init__( self : Tuple , __lowerCamelCase : float = 0.02 , __lowerCamelCase : float = 100 , __lowerCamelCase : float = 1.007 , __lowerCamelCase : float = 80 , __lowerCamelCase : float = 0.05 , __lowerCamelCase : float = 50 , ):
# standard deviation of the initial noise distribution
SCREAMING_SNAKE_CASE = sigma_max
# setable values
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None # sigma(t_i)
def _snake_case ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : Optional[int] = None ):
return sample
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : Union[str, torch.device] = None ):
SCREAMING_SNAKE_CASE = num_inference_steps
SCREAMING_SNAKE_CASE = np.arange(0 , self.num_inference_steps )[::-1].copy()
SCREAMING_SNAKE_CASE = torch.from_numpy(__lowerCamelCase ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
SCREAMING_SNAKE_CASE = torch.tensor(__lowerCamelCase , dtype=torch.floataa , device=__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : float , __lowerCamelCase : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
SCREAMING_SNAKE_CASE = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
SCREAMING_SNAKE_CASE = 0
# sample eps ~ N(0, S_noise^2 * I)
SCREAMING_SNAKE_CASE = self.config.s_noise * randn_tensor(sample.shape , generator=__lowerCamelCase ).to(sample.device )
SCREAMING_SNAKE_CASE = sigma + gamma * sigma
SCREAMING_SNAKE_CASE = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _snake_case ( self : str , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True , ):
SCREAMING_SNAKE_CASE = sample_hat + sigma_hat * model_output
SCREAMING_SNAKE_CASE = (sample_hat - pred_original_sample) / sigma_hat
SCREAMING_SNAKE_CASE = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__lowerCamelCase , derivative=__lowerCamelCase , pred_original_sample=__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : bool = True , ):
SCREAMING_SNAKE_CASE = sample_prev + sigma_prev * model_output
SCREAMING_SNAKE_CASE = (sample_prev - pred_original_sample) / sigma_prev
SCREAMING_SNAKE_CASE = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=__lowerCamelCase , derivative=__lowerCamelCase , pred_original_sample=__lowerCamelCase )
def _snake_case ( self : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
raise NotImplementedError() | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
rect.set_stroke(__lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__lowerCamelCase , buff=0.0 )
self.add(__lowerCamelCase )
cpu_targs.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Loaded Checkpoint" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , aligned_edge=__lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCamelCase ) , Write(__lowerCamelCase ) )
self.play(Write(__lowerCamelCase , run_time=1 ) , Create(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(__lowerCamelCase , opacity=0.7 )
target.move_to(__lowerCamelCase )
first_animations.append(GrowFromCenter(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__lowerCamelCase , run_time=1.5 ) )
self.play(*__lowerCamelCase )
self.play(*__lowerCamelCase )
self.wait() | 698 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
__A : str = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=1 ):
SCREAMING_SNAKE_CASE = tokenizer
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = len(__lowerCamelCase ) if n_tasks is None else n_tasks
SCREAMING_SNAKE_CASE = n_copies
def __iter__( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip() )
SCREAMING_SNAKE_CASE = self.tokenizer(__lowerCamelCase , padding=__lowerCamelCase , return_tensors="pt" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : List[str] , __lowerCamelCase : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = start_length
SCREAMING_SNAKE_CASE = eof_strings
SCREAMING_SNAKE_CASE = tokenizer
def __call__( self : Tuple , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] , **__lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
SCREAMING_SNAKE_CASE = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__lowerCamelCase )
def __a ( A__ : Dict ):
SCREAMING_SNAKE_CASE = re.split("(%s)" % "|".join(A__ ) , A__ )
# last string should be ""
return "".join(string_list[:-2] )
def __a ( A__ : Any , A__ : Tuple , A__ : Any , A__ : Dict , A__ : str , A__ : Dict=20 , **A__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = defaultdict(A__ ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(A__ ) ):
with torch.no_grad():
SCREAMING_SNAKE_CASE = batch["ids"].shape[-1]
SCREAMING_SNAKE_CASE = accelerator.unwrap_model(A__ ).generate(
input_ids=batch["ids"][:, : batch["input_len"]] , num_return_sequences=A__ , **A__ )
# each task is generated batch_size times
SCREAMING_SNAKE_CASE = batch["task_id"].repeat(A__ )
SCREAMING_SNAKE_CASE = accelerator.pad_across_processes(
A__ , dim=1 , pad_index=tokenizer.pad_token_id )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather((generated_tokens, generated_tasks) )
SCREAMING_SNAKE_CASE = generated_tokens.cpu().numpy()
SCREAMING_SNAKE_CASE = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(A__ , A__ ):
gen_token_dict[task].append(A__ )
SCREAMING_SNAKE_CASE = [[] for _ in range(A__ )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
SCREAMING_SNAKE_CASE = tokenizer.decode(A__ , skip_special_tokens=A__ , clean_up_tokenization_spaces=A__ )
code_gens[task].append(remove_last_block(A__ ) )
return code_gens
def __a ( ):
# Setup configuration
SCREAMING_SNAKE_CASE = HfArgumentParser(A__ )
SCREAMING_SNAKE_CASE = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
SCREAMING_SNAKE_CASE = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
SCREAMING_SNAKE_CASE = "false"
if args.num_workers is None:
SCREAMING_SNAKE_CASE = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
SCREAMING_SNAKE_CASE = Accelerator()
set_seed(args.seed , device_specific=A__ )
# Load model and tokenizer
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(args.model_ckpt )
SCREAMING_SNAKE_CASE = tokenizer.eos_token
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
SCREAMING_SNAKE_CASE = {
"do_sample": args.do_sample,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"top_p": args.top_p,
"top_k": args.top_k,
"stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0 , A__ , A__ )] ),
}
# Load evaluation dataset and metric
SCREAMING_SNAKE_CASE = load_dataset("openai_humaneval" )
SCREAMING_SNAKE_CASE = load_metric("code_eval" )
SCREAMING_SNAKE_CASE = args.num_tasks if args.num_tasks is not None else len(human_eval["test"] )
SCREAMING_SNAKE_CASE = args.n_samples // args.batch_size
SCREAMING_SNAKE_CASE = TokenizedDataset(A__ , human_eval["test"] , n_copies=A__ , n_tasks=A__ )
# do not confuse args.batch_size, which is actually the num_return_sequences
SCREAMING_SNAKE_CASE = DataLoader(A__ , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
SCREAMING_SNAKE_CASE = code_eval_metric.compute(references=[""] , predictions=[[""]] )
except ValueError as exception:
print(
"Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
" flag to enable code evaluation." )
raise exception
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(A__ , A__ )
SCREAMING_SNAKE_CASE = complete_code(
A__ , A__ , A__ , A__ , n_tasks=A__ , batch_size=args.batch_size , **A__ , )
if accelerator.is_main_process:
SCREAMING_SNAKE_CASE = []
for task in tqdm(range(A__ ) ):
SCREAMING_SNAKE_CASE = human_eval["test"][task]["test"]
SCREAMING_SNAKE_CASE = F"check({human_eval['test'][task]['entry_point']})"
references.append("\n" + test_func + "\n" + entry_point )
# Evaluate completions with "code_eval" metric
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = code_eval_metric.compute(
references=A__ , predictions=A__ , num_workers=args.num_workers )
print(F"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file , "w" ) as fp:
json.dump(A__ , A__ )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main() | 698 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Any , **__lowerCamelCase : Any ):
pass
@is_pipeline_test
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCamelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@require_tf
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@slow
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , ) | 698 | 1 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer("This is me" , return_tensors="pt" )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
SCREAMING_SNAKE_CASE = model.generate(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
self.assertFalse(
any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
SCREAMING_SNAKE_CASE = model_reloaded.generate(**__lowerCamelCase )
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase ) )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__lowerCamelCase ):
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
model.save_pretrained(__lowerCamelCase ) | 698 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.6_0217_6634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def __a ( A__ : str , A__ : str , A__ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE = (
F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
F"Valid values are: {', '.join(A__ )}"
)
raise ValueError(A__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : List[str] = logging.get_logger(__name__)
__A : Optional[int] = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "instructblip_vision_model"
def __init__( self : str , __lowerCamelCase : Union[str, Any]=1408 , __lowerCamelCase : str=6144 , __lowerCamelCase : Any=39 , __lowerCamelCase : Tuple=16 , __lowerCamelCase : str=224 , __lowerCamelCase : List[str]=14 , __lowerCamelCase : Dict="gelu" , __lowerCamelCase : Optional[int]=1e-6 , __lowerCamelCase : Any=0.0 , __lowerCamelCase : Any=1e-10 , __lowerCamelCase : Any=True , **__lowerCamelCase : Tuple , ):
super().__init__(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = qkv_bias
@classmethod
def _snake_case ( cls : Optional[int] , __lowerCamelCase : Union[str, os.PathLike] , **__lowerCamelCase : Any ):
cls._set_token_in_kwargs(__lowerCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
SCREAMING_SNAKE_CASE = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "instructblip_qformer"
def __init__( self : List[Any] , __lowerCamelCase : Tuple=30522 , __lowerCamelCase : List[Any]=768 , __lowerCamelCase : int=12 , __lowerCamelCase : Union[str, Any]=12 , __lowerCamelCase : str=3072 , __lowerCamelCase : Optional[Any]="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Optional[Any]=512 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : List[Any]=1e-12 , __lowerCamelCase : Tuple=0 , __lowerCamelCase : Union[str, Any]="absolute" , __lowerCamelCase : Union[str, Any]=2 , __lowerCamelCase : Dict=1408 , **__lowerCamelCase : Optional[Any] , ):
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = cross_attention_frequency
SCREAMING_SNAKE_CASE = encoder_hidden_size
@classmethod
def _snake_case ( cls : Optional[int] , __lowerCamelCase : Union[str, os.PathLike] , **__lowerCamelCase : Dict ):
cls._set_token_in_kwargs(__lowerCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
SCREAMING_SNAKE_CASE = config_dict["qformer_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "instructblip"
lowerCamelCase__ = True
def __init__( self : int , __lowerCamelCase : int=None , __lowerCamelCase : Any=None , __lowerCamelCase : str=None , __lowerCamelCase : Union[str, Any]=32 , **__lowerCamelCase : List[Any] ):
super().__init__(**__lowerCamelCase )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )
if qformer_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )
if text_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
SCREAMING_SNAKE_CASE = InstructBlipVisionConfig(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = InstructBlipQFormerConfig(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = text_config["model_type"] if "model_type" in text_config else "opt"
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[text_model_type](**__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE = self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE = num_query_tokens
SCREAMING_SNAKE_CASE = self.vision_config.hidden_size
SCREAMING_SNAKE_CASE = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE = 1.0
SCREAMING_SNAKE_CASE = 0.02
@classmethod
def _snake_case ( cls : List[str] , __lowerCamelCase : InstructBlipVisionConfig , __lowerCamelCase : InstructBlipQFormerConfig , __lowerCamelCase : PretrainedConfig , **__lowerCamelCase : Union[str, Any] , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowerCamelCase , )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE = self.text_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output | 698 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.values[key]
def _snake_case ( self : Union[str, Any] ):
return (
sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Tuple=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(__lowerCamelCase , __lowerCamelCase ) | 698 | 1 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ = ["transformers", "torch", "note_seq"]
def __init__( self : Optional[int] , *__lowerCamelCase : Optional[int] , **__lowerCamelCase : List[str] ):
requires_backends(self , ["transformers", "torch", "note_seq"] )
@classmethod
def _snake_case ( cls : Tuple , *__lowerCamelCase : Optional[int] , **__lowerCamelCase : Union[str, Any] ):
requires_backends(cls , ["transformers", "torch", "note_seq"] )
@classmethod
def _snake_case ( cls : Tuple , *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : List[Any] ):
requires_backends(cls , ["transformers", "torch", "note_seq"] ) | 698 |
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
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {
'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 _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "gpt_neo"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : str , __lowerCamelCase : Dict=50257 , __lowerCamelCase : Tuple=2048 , __lowerCamelCase : Optional[Any]=2048 , __lowerCamelCase : int=24 , __lowerCamelCase : int=[[["global", "local"], 12]] , __lowerCamelCase : int=16 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]=256 , __lowerCamelCase : Tuple="gelu_new" , __lowerCamelCase : Optional[Any]=0.0 , __lowerCamelCase : str=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : List[Any]=1e-5 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Dict=True , __lowerCamelCase : Dict=50256 , __lowerCamelCase : Optional[int]=50256 , **__lowerCamelCase : Dict , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_layers
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = window_size
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_dropout
SCREAMING_SNAKE_CASE = embed_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = attention_types
SCREAMING_SNAKE_CASE = self.expand_attention_types_params(__lowerCamelCase )
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=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
@staticmethod
def _snake_case ( __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __a ( A__ : str , A__ : List[Any] , A__ : List[str] , A__ : Union[str, Any] ):
import torch
SCREAMING_SNAKE_CASE = input.size()
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = shape[dimension]
SCREAMING_SNAKE_CASE = torch.arange(0 , A__ , A__ )
SCREAMING_SNAKE_CASE = torch.div(sizedim - size , A__ , rounding_mode="floor" ) + 1
SCREAMING_SNAKE_CASE = torch.arange(A__ ) + low_indices[:min_length][:, None]
SCREAMING_SNAKE_CASE = [slice(A__ )] * rank
SCREAMING_SNAKE_CASE = indices
SCREAMING_SNAKE_CASE = input[s]
SCREAMING_SNAKE_CASE = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A__ )
def __a ( A__ : Union[str, Any] , A__ : Optional[int] ):
import torch
SCREAMING_SNAKE_CASE = torch.arange(1 , A__ )
SCREAMING_SNAKE_CASE = torch.remainder(A__ , A__ )
SCREAMING_SNAKE_CASE = remainders == 0
SCREAMING_SNAKE_CASE = candidates[divisor_indices]
SCREAMING_SNAKE_CASE = torch.max(A__ )
return largest_divisor, torch.div(A__ , A__ , rounding_mode="floor" )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(__lowerCamelCase , direction="inputs" )
SCREAMING_SNAKE_CASE = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _snake_case ( self : Optional[int] ):
return self._config.num_heads
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE = super(__lowerCamelCase , self ).generate_dummy_inputs(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE = seqlen + 2
SCREAMING_SNAKE_CASE = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE = [
(torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase , dtype=__lowerCamelCase )] , dim=1 )
return ordered_inputs
@property
def _snake_case ( self : Optional[int] ):
return 13 | 698 | 1 |
import os
def __a ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
SCREAMING_SNAKE_CASE = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
SCREAMING_SNAKE_CASE = 0
# right
for i in range(20 ):
for j in range(17 ):
SCREAMING_SNAKE_CASE = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
SCREAMING_SNAKE_CASE = temp
# down
for i in range(17 ):
for j in range(20 ):
SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
SCREAMING_SNAKE_CASE = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
SCREAMING_SNAKE_CASE = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
SCREAMING_SNAKE_CASE = temp
return maximum
if __name__ == "__main__":
print(solution()) | 698 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'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
__A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Dict = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'GraphormerForGraphClassification',
'GraphormerModel',
'GraphormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__A : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 1 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(A__ , A__ , bias=A__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Tuple , A__ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE = {}
for old_key in state_dict.keys():
SCREAMING_SNAKE_CASE = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts.0" , F"ffn.experts.expert_{expert_idx}" )
else:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
SCREAMING_SNAKE_CASE = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
SCREAMING_SNAKE_CASE = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("final_layer_norm" , "ff_layer_norm" )
SCREAMING_SNAKE_CASE = state_dict[old_key]
return new_dict
def __a ( A__ : List[str] , A__ : List[Any] , A__ : str , A__ : Union[str, Any] , A__ : str = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
os.makedirs(A__ , exist_ok=A__ )
for expert in range(A__ ):
SCREAMING_SNAKE_CASE = switch_checkpoint_path + F"-rank-{expert}.pt"
if os.path.isfile(A__ ):
SCREAMING_SNAKE_CASE = torch.load(A__ )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = os.path.join(
A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
torch.save(A__ , A__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A__ )[0]].dtype )
# Add the last block
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
SCREAMING_SNAKE_CASE = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A__ ) == 1:
SCREAMING_SNAKE_CASE = os.path.join(A__ , A__ )
torch.save(A__ , A__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A__ , A__ )
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(A__ ):
SCREAMING_SNAKE_CASE = weights_name.replace(".bin" , F"-{idx+1:05d}-of-{len(A__ ):05d}.bin" )
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
for key in shard:
SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE = {"total_size": total_size}
SCREAMING_SNAKE_CASE = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A__ , A__ ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(A__ , indent=2 , sort_keys=A__ ) + "\n"
f.write(A__ )
return metadata, index
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : Optional[int] = parser.parse_args()
__A , __A : Union[str, Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
__A : Any = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : Any = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path) | 698 | 1 |
from __future__ import annotations
import math
def __a ( A__ : float , A__ : int ):
SCREAMING_SNAKE_CASE = u
for i in range(1 , A__ ):
SCREAMING_SNAKE_CASE = temp * (u - i)
return temp
def __a ( ):
SCREAMING_SNAKE_CASE = int(input("enter the numbers of values: " ) )
SCREAMING_SNAKE_CASE = []
for _ in range(A__ ):
y.append([] )
for i in range(A__ ):
for j in range(A__ ):
y[i].append(A__ )
SCREAMING_SNAKE_CASE = 0
print("enter the values of parameters in a list: " )
SCREAMING_SNAKE_CASE = list(map(A__ , input().split() ) )
print("enter the values of corresponding parameters: " )
for i in range(A__ ):
SCREAMING_SNAKE_CASE = float(input() )
SCREAMING_SNAKE_CASE = int(input("enter the value to interpolate: " ) )
SCREAMING_SNAKE_CASE = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , A__ ):
for j in range(n - i ):
SCREAMING_SNAKE_CASE = y[j + 1][i - 1] - y[j][i - 1]
SCREAMING_SNAKE_CASE = y[0][0]
for i in range(1 , A__ ):
summ += (ucal(A__ , A__ ) * y[0][i]) / math.factorial(A__ )
print(F"the value at {value} is {summ}" )
if __name__ == "__main__":
main() | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCamelCase , __lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self : Dict , **__lowerCamelCase : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).tokenizer
def _snake_case ( self : List[Any] , **__lowerCamelCase : Optional[Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).image_processor
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCamelCase ):
processor()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 698 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : int = logging.get_logger(__name__)
__A : Optional[int] = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "bit"
lowerCamelCase__ = ["preactivation", "bottleneck"]
lowerCamelCase__ = ["SAME", "VALID"]
def __init__( self : List[str] , __lowerCamelCase : List[Any]=3 , __lowerCamelCase : Optional[Any]=64 , __lowerCamelCase : int=[256, 512, 1024, 2048] , __lowerCamelCase : int=[3, 4, 6, 3] , __lowerCamelCase : Tuple="preactivation" , __lowerCamelCase : Any="relu" , __lowerCamelCase : str=None , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : Union[str, Any]=0.0 , __lowerCamelCase : List[Any]=False , __lowerCamelCase : Optional[int]=32 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Any , ):
super().__init__(**__lowerCamelCase )
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:
SCREAMING_SNAKE_CASE = global_padding.upper()
else:
raise ValueError(f"Padding strategy {global_padding} not supported" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = global_padding
SCREAMING_SNAKE_CASE = num_groups
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = embedding_dynamic_padding
SCREAMING_SNAKE_CASE = output_stride
SCREAMING_SNAKE_CASE = width_factor
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names ) | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
pass
def __a ( A__ : str ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A : Tuple = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model=__lowerCamelCase , tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
SCREAMING_SNAKE_CASE = "What is the placebo?"
SCREAMING_SNAKE_CASE = [
{
"image": load_image(__lowerCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = dqa_pipeline(__lowerCamelCase , top_k=2 )
self.assertEqual(
__lowerCamelCase , [
[
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "How many cats are there?"
SCREAMING_SNAKE_CASE = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , words=__lowerCamelCase , boxes=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _snake_case ( self : List[Any] ):
pass | 698 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
__A : Dict = random.Random()
if is_torch_available():
import torch
def __a ( A__ : List[str] , A__ : Tuple=1.0 , A__ : List[str]=None , A__ : Union[str, Any]=None ):
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : Any=7 , __lowerCamelCase : List[str]=400 , __lowerCamelCase : Tuple=2000 , __lowerCamelCase : Tuple=1 , __lowerCamelCase : Optional[int]=0.0 , __lowerCamelCase : Any=16000 , __lowerCamelCase : Any=True , __lowerCamelCase : Optional[Any]=True , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = padding_value
SCREAMING_SNAKE_CASE = sampling_rate
SCREAMING_SNAKE_CASE = return_attention_mask
SCREAMING_SNAKE_CASE = do_normalize
def _snake_case ( self : Optional[int] ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _snake_case ( self : int , __lowerCamelCase : Optional[int]=False , __lowerCamelCase : Tuple=False ):
def _flatten(__lowerCamelCase : Optional[int] ):
return list(itertools.chain(*__lowerCamelCase ) )
if equal_length:
SCREAMING_SNAKE_CASE = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(__lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = ASTFeatureExtractor
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = ASTFeatureExtractionTester(self )
def _snake_case ( self : Optional[int] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE = [np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
SCREAMING_SNAKE_CASE = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE = feat_extract(__lowerCamelCase , padding=__lowerCamelCase , return_tensors="np" ).input_values
SCREAMING_SNAKE_CASE = feat_extract(__lowerCamelCase , padding=__lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(__lowerCamelCase )
SCREAMING_SNAKE_CASE = feat_extract(__lowerCamelCase , return_tensors="np" ).input_values
SCREAMING_SNAKE_CASE = feat_extract(__lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 ) )
@require_torch
def _snake_case ( self : Union[str, Any] ):
import torch
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Tuple ):
from datasets import load_dataset
SCREAMING_SNAKE_CASE = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("id" ).select(range(__lowerCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
def _snake_case ( self : List[Any] ):
# fmt: off
SCREAMING_SNAKE_CASE = torch.tensor(
[-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
-1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
-1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
-0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869] )
# fmt: on
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = ASTFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(__lowerCamelCase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 1024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , __lowerCamelCase , atol=1e-4 ) ) | 698 |
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "facebook/bart-large-mnli"
lowerCamelCase__ = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
lowerCamelCase__ = "text_classifier"
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSequenceClassification
lowerCamelCase__ = ["text", ["text"]]
lowerCamelCase__ = ["text"]
def _snake_case ( self : Optional[Any] ):
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__lowerCamelCase ) , [f"This example is {label}" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id] | 698 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : List[Any] = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
),
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "swinv2"
lowerCamelCase__ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : str , __lowerCamelCase : Optional[Any]=224 , __lowerCamelCase : List[str]=4 , __lowerCamelCase : List[str]=3 , __lowerCamelCase : Dict=96 , __lowerCamelCase : List[str]=[2, 2, 6, 2] , __lowerCamelCase : List[str]=[3, 6, 12, 24] , __lowerCamelCase : Any=7 , __lowerCamelCase : Any=4.0 , __lowerCamelCase : str=True , __lowerCamelCase : Any=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]="gelu" , __lowerCamelCase : Dict=False , __lowerCamelCase : List[Any]=0.02 , __lowerCamelCase : Optional[int]=1e-5 , __lowerCamelCase : str=32 , **__lowerCamelCase : Tuple , ):
super().__init__(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = qkv_bias
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = use_absolute_embeddings
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE = int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )
SCREAMING_SNAKE_CASE = (0, 0, 0, 0) | 698 |
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __a ( A__ : str=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("test" )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=A__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
SCREAMING_SNAKE_CASE = script_name
else:
SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
SCREAMING_SNAKE_CASE = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def __a ( ):
SCREAMING_SNAKE_CASE = test_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main() | 698 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = {
"task_specific_params": {
"summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
"summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
"summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
}
}
SCREAMING_SNAKE_CASE = {
"task_specific_params.summarization.length_penalty": 1.0,
"task_specific_params.summarization.max_length": 128,
"task_specific_params.summarization.min_length": 12,
"task_specific_params.summarization.num_beams": 4,
"task_specific_params.summarization_cnn.length_penalty": 2.0,
"task_specific_params.summarization_cnn.max_length": 142,
"task_specific_params.summarization_cnn.min_length": 56,
"task_specific_params.summarization_cnn.num_beams": 4,
"task_specific_params.summarization_xsum.length_penalty": 1.0,
"task_specific_params.summarization_xsum.max_length": 62,
"task_specific_params.summarization_xsum.min_length": 11,
"task_specific_params.summarization_xsum.num_beams": 6,
}
self.assertEqual(flatten_dict(A__ ) , A__ )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(A__ ) , x.transpose() ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , np.asarray(transpose(A__ ) ) ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , np.asarray(transpose(A__ , axes=(1, 2, 0) ) ) ) )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.reshape(A__ , (4, 3) ) ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.reshape(A__ , (12, 5) ) ) )
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_tf
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_flax
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.asarray(reshape(A__ , (4, 3) ) ) ) )
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.asarray(reshape(A__ , (12, 5) ) ) ) )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(A__ ) , np.squeeze(A__ ) ) )
SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.squeeze(A__ , axis=2 ) ) )
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_tf
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_flax
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , np.asarray(squeeze(A__ ) ) ) )
SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.asarray(squeeze(A__ , axis=2 ) ) ) )
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.expand_dims(A__ , axis=1 ) ) )
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_tf
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_flax
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.asarray(expand_dims(A__ , axis=1 ) ) ) ) | 700 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[int] = logging.get_logger(__name__)
__A : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : Tuple = {
'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'
},
}
__A : Dict = {'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ):
SCREAMING_SNAKE_CASE = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE = bs[:]
SCREAMING_SNAKE_CASE = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def __a ( A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE = char
return pairs
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any]="replace" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : List[Any]="<mask>" , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE = bytes_to_unicode()
SCREAMING_SNAKE_CASE = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case ( self : Dict ):
return len(self.encoder )
def _snake_case ( self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = bigram
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = word
return word
def _snake_case ( self : str , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for token in re.findall(self.pat , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : List[str] , __lowerCamelCase : str ):
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Tuple ):
return self.decoder.get(__lowerCamelCase )
def _snake_case ( self : Tuple , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = "".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
SCREAMING_SNAKE_CASE = 0
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _snake_case ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 : Any , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE = " " + text
return (text, kwargs)
def _snake_case ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case ( self : Tuple , __lowerCamelCase : "Conversation" ):
SCREAMING_SNAKE_CASE = []
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(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
SCREAMING_SNAKE_CASE = 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 | 698 | 0 |
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 _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int , __lowerCamelCase : Any , __lowerCamelCase : Union[str, Any]=3 , __lowerCamelCase : Optional[int]=32 , __lowerCamelCase : Tuple=3 , __lowerCamelCase : str=10 , __lowerCamelCase : Optional[Any]=[10, 20, 30, 40] , __lowerCamelCase : List[str]=[1, 1, 2, 1] , __lowerCamelCase : List[Any]=True , __lowerCamelCase : int=True , __lowerCamelCase : Optional[int]="relu" , __lowerCamelCase : str=3 , __lowerCamelCase : Optional[int]=None , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embeddings_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = len(lowerCAmelCase__ )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Any ):
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 : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = TFResNetModel(config=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCAmelCase__ )
# 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 : Union[str, Any] , __lowerCamelCase : Tuple , __lowerCamelCase : str , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = TFResNetForImageClassification(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _SCREAMING_SNAKE_CASE ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
lowerCamelCase__ = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = TFResNetModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _snake_case ( self : Dict ):
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 : Optional[Any] ):
return
@unittest.skip(reason="ResNet does not use inputs_embeds" )
def _snake_case ( self : int ):
pass
@unittest.skip(reason="ResNet does not support input and output embeddings" )
def _snake_case ( self : str ):
pass
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _snake_case ( self : Optional[Any] ):
def check_hidden_states_output(__lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase__ ) , 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] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
@slow
def _snake_case ( self : Union[str, Any] ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = TFResNetModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Optional[Any] ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=lowerCAmelCase__ , return_tensors="tf" )
# forward pass
SCREAMING_SNAKE_CASE = model(**lowerCAmelCase__ )
# verify the logits
SCREAMING_SNAKE_CASE = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCAmelCase__ , atol=1e-4 ) ) | 701 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE_CASE = (-b + sqrt(A__ )) / (2 * a)
SCREAMING_SNAKE_CASE = (-b - sqrt(A__ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main() | 698 | 0 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : float = 0 ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = row, column
SCREAMING_SNAKE_CASE = [[default_value for c in range(UpperCAmelCase_ )] for r in range(UpperCAmelCase_ )]
def __str__( self : Optional[int] ):
SCREAMING_SNAKE_CASE = f"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
SCREAMING_SNAKE_CASE = 0
for row_vector in self.array:
for obj in row_vector:
SCREAMING_SNAKE_CASE = max(UpperCAmelCase_ , len(str(UpperCAmelCase_ ) ) )
SCREAMING_SNAKE_CASE = f"%{max_element_length}s"
# Make string and return
def single_line(__lowerCamelCase : list[float] ) -> str:
nonlocal string_format_identifier
SCREAMING_SNAKE_CASE = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(UpperCAmelCase_ ) for row_vector in self.array )
return s
def __repr__( self : Tuple ):
return str(self )
def _snake_case ( self : List[str] , __lowerCamelCase : tuple[int, int] ):
if not (isinstance(UpperCAmelCase_ , (list, tuple) ) and len(UpperCAmelCase_ ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : Union[str, Any] , __lowerCamelCase : tuple[int, int] ):
assert self.validate_indicies(UpperCAmelCase_ )
return self.array[loc[0]][loc[1]]
def __setitem__( self : Dict , __lowerCamelCase : tuple[int, int] , __lowerCamelCase : float ):
assert self.validate_indicies(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = value
def __add__( self : Optional[int] , __lowerCamelCase : Matrix ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
assert self.row == another.row and self.column == another.column
# Add
SCREAMING_SNAKE_CASE = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
SCREAMING_SNAKE_CASE = self[r, c] + another[r, c]
return result
def __neg__( self : int ):
SCREAMING_SNAKE_CASE = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
SCREAMING_SNAKE_CASE = -self[r, c]
return result
def __sub__( self : Optional[Any] , __lowerCamelCase : Matrix ):
return self + (-another)
def __mul__( self : List[str] , __lowerCamelCase : int | float | Matrix ):
if isinstance(UpperCAmelCase_ , (int, float) ): # Scalar multiplication
SCREAMING_SNAKE_CASE = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
SCREAMING_SNAKE_CASE = self[r, c] * another
return result
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ): # Matrix multiplication
assert self.column == another.row
SCREAMING_SNAKE_CASE = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
SCREAMING_SNAKE_CASE = f"Unsupported type given for another ({type(UpperCAmelCase_ )})"
raise TypeError(UpperCAmelCase_ )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
SCREAMING_SNAKE_CASE = self[r, c]
return result
def _snake_case ( self : str , __lowerCamelCase : Matrix , __lowerCamelCase : Matrix ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
SCREAMING_SNAKE_CASE = v.transpose()
SCREAMING_SNAKE_CASE = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def __a ( ):
# a^(-1)
SCREAMING_SNAKE_CASE = Matrix(3 , 3 , 0 )
for i in range(3 ):
SCREAMING_SNAKE_CASE = 1
print(F"a^(-1) is {ainv}" )
# u, v
SCREAMING_SNAKE_CASE = Matrix(3 , 1 , 0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1, 2, -3
SCREAMING_SNAKE_CASE = Matrix(3 , 1 , 0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 4, -2, 5
print(F"u is {u}" )
print(F"v is {v}" )
print(F"uv^T is {u * v.transpose()}" )
# Sherman Morrison
print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}" )
def __a ( ):
import doctest
doctest.testmod()
testa() | 702 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Dict ):
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 _snake_case ( self : int ):
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 _snake_case ( self : List[str] ):
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 _snake_case ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
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 _snake_case ( self : Optional[int] ):
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 | 698 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( A__ : Optional[int] , A__ : List[Any] , A__ : Any ):
SCREAMING_SNAKE_CASE = BertConfig.from_json_file(A__ )
print(F"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE = BertForPreTraining(A__ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(A__ , A__ , A__ )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , A__ )
if __name__ == "__main__":
__A : 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(
'--bert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT 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.'
)
__A : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path) | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Any = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['__file__'], _import_structure) | 704 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 698 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 705 |
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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "resnet"
lowerCamelCase__ = ["basic", "bottleneck"]
def __init__( self : Optional[Any] , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=64 , __lowerCamelCase : str=[256, 512, 1024, 2048] , __lowerCamelCase : str=[3, 4, 6, 3] , __lowerCamelCase : Optional[int]="bottleneck" , __lowerCamelCase : int="relu" , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = downsample_in_first_stage
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = version.parse("1.11" )
@property
def _snake_case ( self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : Optional[int] ):
return 1e-3 | 698 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = UnCLIPImageVariationPipeline
lowerCamelCase__ = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
lowerCamelCase__ = IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ = [
"generator",
"return_dict",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
lowerCamelCase__ = False
@property
def _snake_case ( self : Tuple ):
return 32
@property
def _snake_case ( self : Tuple ):
return 32
@property
def _snake_case ( self : int ):
return self.time_input_dim
@property
def _snake_case ( self : Any ):
return self.time_input_dim * 4
@property
def _snake_case ( self : List[Any] ):
return 100
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def _snake_case ( self : Union[str, Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCAmelCase_ )
@property
def _snake_case ( self : Any ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(UpperCAmelCase_ )
@property
def _snake_case ( self : Any ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
SCREAMING_SNAKE_CASE = UnCLIPTextProjModel(**UpperCAmelCase_ )
return model
@property
def _snake_case ( self : Union[str, Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
'sample_size': 32,
# RGB in channels
'in_channels': 3,
# Out channels is double in channels because predicts mean and variance
'out_channels': 6,
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': 'identity',
}
SCREAMING_SNAKE_CASE = UNetaDConditionModel(**UpperCAmelCase_ )
return model
@property
def _snake_case ( self : List[str] ):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def _snake_case ( self : List[Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def _snake_case ( self : Optional[Any] ):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
SCREAMING_SNAKE_CASE = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = self.dummy_decoder
SCREAMING_SNAKE_CASE = self.dummy_text_proj
SCREAMING_SNAKE_CASE = self.dummy_text_encoder
SCREAMING_SNAKE_CASE = self.dummy_tokenizer
SCREAMING_SNAKE_CASE = self.dummy_super_res_first
SCREAMING_SNAKE_CASE = self.dummy_super_res_last
SCREAMING_SNAKE_CASE = UnCLIPScheduler(
variance_type="learned_range" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE = CLIPImageProcessor(crop_size=32 , size=32 )
SCREAMING_SNAKE_CASE = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any]=0 , __lowerCamelCase : Dict=True ):
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
if str(UpperCAmelCase_ ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
if pil_image:
SCREAMING_SNAKE_CASE = input_image * 0.5 + 0.5
SCREAMING_SNAKE_CASE = input_image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE = DiffusionPipeline.numpy_to_pil(UpperCAmelCase_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = 'cpu'
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe(
**UpperCAmelCase_ , return_dict=UpperCAmelCase_ , )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE = np.array(
[
0.9_997,
0.0_002,
0.9_997,
0.9_997,
0.9_969,
0.0_023,
0.9_997,
0.9_969,
0.9_970,
] )
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 _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = 'cpu'
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe(
**UpperCAmelCase_ , return_dict=UpperCAmelCase_ , )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971] )
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 _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = 'cpu'
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
SCREAMING_SNAKE_CASE = pipe(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
SCREAMING_SNAKE_CASE = pipe(
**UpperCAmelCase_ , return_dict=UpperCAmelCase_ , )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
SCREAMING_SNAKE_CASE = np.array(
[
0.9_997,
0.9_989,
0.0_008,
0.0_021,
0.9_960,
0.0_018,
0.0_014,
0.0_002,
0.9_933,
] )
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 _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = torch.device("cpu" )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = 1
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCAmelCase_ ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe.decoder.dtype
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
SCREAMING_SNAKE_CASE = pipe.prepare_latents(
UpperCAmelCase_ , dtype=UpperCAmelCase_ , device=UpperCAmelCase_ , generator=UpperCAmelCase_ , latents=UpperCAmelCase_ , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
SCREAMING_SNAKE_CASE = pipe.prepare_latents(
UpperCAmelCase_ , dtype=UpperCAmelCase_ , device=UpperCAmelCase_ , generator=UpperCAmelCase_ , latents=UpperCAmelCase_ , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = pipe(
**UpperCAmelCase_ , decoder_latents=UpperCAmelCase_ , super_res_latents=UpperCAmelCase_ ).images
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase_ , pil_image=UpperCAmelCase_ )
# Don't pass image, instead pass embedding
SCREAMING_SNAKE_CASE = pipeline_inputs.pop("image" )
SCREAMING_SNAKE_CASE = pipe.image_encoder(UpperCAmelCase_ ).image_embeds
SCREAMING_SNAKE_CASE = pipe(
**UpperCAmelCase_ , decoder_latents=UpperCAmelCase_ , super_res_latents=UpperCAmelCase_ , image_embeddings=UpperCAmelCase_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
SCREAMING_SNAKE_CASE = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=UpperCAmelCase_ , expected_max_diff=UpperCAmelCase_ )
@skip_mps
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = torch_device == 'cpu'
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=UpperCAmelCase_ , relax_max_difference=UpperCAmelCase_ , additional_params_copy_to_batched_inputs=UpperCAmelCase_ , )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
SCREAMING_SNAKE_CASE = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=UpperCAmelCase_ , additional_params_copy_to_batched_inputs=UpperCAmelCase_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=UpperCAmelCase_ )
@skip_mps
def _snake_case ( self : List[Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def _snake_case ( self : str ):
return super().test_save_load_local()
@skip_mps
def _snake_case ( self : Optional[Any] ):
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
SCREAMING_SNAKE_CASE = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )
SCREAMING_SNAKE_CASE = UnCLIPImageVariationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE = pipeline.to(UpperCAmelCase_ )
pipeline.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = torch.Generator(device="cpu" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipeline(
UpperCAmelCase_ , generator=UpperCAmelCase_ , output_type="np" , )
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(UpperCAmelCase_ , UpperCAmelCase_ , 15 ) | 706 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "imagegpt"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , __lowerCamelCase : Any=512 + 1 , __lowerCamelCase : str=32 * 32 , __lowerCamelCase : Any=512 , __lowerCamelCase : Optional[int]=24 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]="quick_gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[Any]=1e-5 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : int=False , __lowerCamelCase : int=False , **__lowerCamelCase : Union[str, Any] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scale_attn_weights
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE = tie_word_embeddings
super().__init__(tie_word_embeddings=__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def _snake_case ( self : Optional[int] , __lowerCamelCase : "FeatureExtractionMixin" , __lowerCamelCase : int = 1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , ):
SCREAMING_SNAKE_CASE = self._generate_dummy_images(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dict(preprocessor(images=__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return inputs | 698 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Tuple = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ["ConvNextFeatureExtractor"]
__A : Dict = ["ConvNextImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
"ConvNextBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__A : Any = _LazyModule(__name__, globals()['__file__'], _import_structure) | 707 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@slow
@require_torch
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE = 4
def _map_to_encoder_decoder_inputs(__lowerCamelCase : str ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE = tokenizer(batch["article"] , padding="max_length" , truncation=__lowerCamelCase , max_length=512 )
SCREAMING_SNAKE_CASE = tokenizer(batch["highlights"] , padding="max_length" , truncation=__lowerCamelCase , max_length=128 )
SCREAMING_SNAKE_CASE = inputs.input_ids
SCREAMING_SNAKE_CASE = inputs.attention_mask
SCREAMING_SNAKE_CASE = outputs.input_ids
SCREAMING_SNAKE_CASE = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
SCREAMING_SNAKE_CASE = outputs.attention_mask
assert all(len(__lowerCamelCase ) == 512 for x in inputs.input_ids )
assert all(len(__lowerCamelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = pred.label_ids
SCREAMING_SNAKE_CASE = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__lowerCamelCase ) )] ) / len(__lowerCamelCase )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE = SeqaSeqTrainingArguments(
output_dir=__lowerCamelCase , per_device_train_batch_size=__lowerCamelCase , per_device_eval_batch_size=__lowerCamelCase , predict_with_generate=__lowerCamelCase , evaluation_strategy="steps" , do_train=__lowerCamelCase , do_eval=__lowerCamelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=__lowerCamelCase , args=__lowerCamelCase , compute_metrics=_compute_metrics , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , tokenizer=__lowerCamelCase , )
# start training
trainer.train() | 698 | 0 |
def __a ( A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = [0] * len(__A )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] == 0:
queue.append(__A )
while queue:
SCREAMING_SNAKE_CASE = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
SCREAMING_SNAKE_CASE = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__A )
print(max(__A ) )
# Adjacency list of Graph
__A : List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 708 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "num_attention_heads" ) )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Dict=3 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : int=2 , __lowerCamelCase : List[Any]=1 , __lowerCamelCase : Optional[Any]=16 , __lowerCamelCase : Tuple=[128, 256, 384] , __lowerCamelCase : int=[4, 6, 8] , __lowerCamelCase : Tuple=[2, 3, 4] , __lowerCamelCase : List[str]=[16, 16, 16] , __lowerCamelCase : int=0 , __lowerCamelCase : List[Any]=[2, 2, 2] , __lowerCamelCase : List[str]=[2, 2, 2] , __lowerCamelCase : List[str]=0.02 , __lowerCamelCase : Any=True , __lowerCamelCase : Dict=True , __lowerCamelCase : int=2 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = kernel_size
SCREAMING_SNAKE_CASE = stride
SCREAMING_SNAKE_CASE = padding
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : List[str] ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _snake_case ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : str , __lowerCamelCase : Any ):
SCREAMING_SNAKE_CASE = LevitModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
SCREAMING_SNAKE_CASE = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _snake_case ( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = LevitForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = LevitModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Any ):
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 : Any ):
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def _snake_case ( self : Tuple ):
pass
@unittest.skip(reason="Levit does not output attentions" )
def _snake_case ( self : Tuple ):
pass
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : List[Any] ):
def check_hidden_states_output(__lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = len(self.model_tester.depths ) + 1
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.model_tester.image_size, self.model_tester.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
SCREAMING_SNAKE_CASE = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : int ):
pass
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : int=False ):
SCREAMING_SNAKE_CASE = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _snake_case ( self : List[Any] ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowerCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__lowerCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}" ):
SCREAMING_SNAKE_CASE = problem_type["title"]
SCREAMING_SNAKE_CASE = problem_type["num_labels"]
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
SCREAMING_SNAKE_CASE = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def _snake_case ( self : List[Any] ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = LevitModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Dict ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([1.0_448, -0.3_745, -1.8_317] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1e-4 ) ) | 698 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : List[str] , *__lowerCamelCase : Dict , __lowerCamelCase : str=None , __lowerCamelCase : int=None , **__lowerCamelCase : Optional[int] ):
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
SCREAMING_SNAKE_CASE = eval_examples
SCREAMING_SNAKE_CASE = post_process_function
def _snake_case ( self : int , __lowerCamelCase : List[str] = None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : List[str] = None , __lowerCamelCase : Tuple = "eval" , **__lowerCamelCase : str , ):
SCREAMING_SNAKE_CASE = gen_kwargs.copy()
SCREAMING_SNAKE_CASE = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
SCREAMING_SNAKE_CASE = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
SCREAMING_SNAKE_CASE = gen_kwargs
SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE = self.get_eval_dataloader(_lowerCAmelCase )
SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE = self.compute_metrics
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = time.time()
SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE = eval_loop(
_lowerCAmelCase , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCAmelCase , metric_key_prefix=_lowerCAmelCase , )
finally:
SCREAMING_SNAKE_CASE = compute_metrics
SCREAMING_SNAKE_CASE = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
_lowerCAmelCase , _lowerCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
SCREAMING_SNAKE_CASE = self.post_process_function(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
SCREAMING_SNAKE_CASE = self.compute_metrics(_lowerCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
SCREAMING_SNAKE_CASE = metrics.pop(_lowerCAmelCase )
metrics.update(output.metrics )
else:
SCREAMING_SNAKE_CASE = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_lowerCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , _lowerCAmelCase )
return metrics
def _snake_case ( self : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : int=None , __lowerCamelCase : Optional[Any] = "test" , **__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = gen_kwargs.copy()
SCREAMING_SNAKE_CASE = self.get_test_dataloader(_lowerCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE = self.compute_metrics
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = time.time()
SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE = eval_loop(
_lowerCAmelCase , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCAmelCase , metric_key_prefix=_lowerCAmelCase , )
finally:
SCREAMING_SNAKE_CASE = compute_metrics
SCREAMING_SNAKE_CASE = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
_lowerCAmelCase , _lowerCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE = self.post_process_function(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , "predict" )
SCREAMING_SNAKE_CASE = self.compute_metrics(_lowerCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
SCREAMING_SNAKE_CASE = metrics.pop(_lowerCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_lowerCAmelCase ) | 709 |
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 _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
return None
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : int ):
return None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : Optional[int] ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(__lowerCamelCase ) )
vocab_file.flush()
SCREAMING_SNAKE_CASE = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
SCREAMING_SNAKE_CASE = BertModel(BertConfig(vocab_size=len(__lowerCamelCase ) ) )
model.save_pretrained(__lowerCamelCase )
self._test_export(__lowerCamelCase , "pt" , 12 , __lowerCamelCase )
@require_tf
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(Path(__lowerCamelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(__lowerCamelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def _snake_case ( self : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , **__lowerCamelCase : List[str] ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE = Path(__lowerCamelCase ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
return path
except Exception as e:
self.fail(__lowerCamelCase )
@require_torch
@require_tokenizers
@slow
def _snake_case ( self : Dict ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "pt" )
@require_tf
@require_tokenizers
@slow
def _snake_case ( self : int ):
from transformers import TFBertModel
SCREAMING_SNAKE_CASE = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "tf" )
def _snake_case ( self : int , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = FeatureExtractionPipeline(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = infer_shapes(__lowerCamelCase , __lowerCamelCase )
# Assert all variables are present
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __lowerCamelCase )
self.assertSequenceEqual(variable_names[3:] , __lowerCamelCase )
# 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 _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask", "token_type_ids"]
SCREAMING_SNAKE_CASE = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__lowerCamelCase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__lowerCamelCase ) , set(__lowerCamelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__lowerCamelCase , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncNonContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__lowerCamelCase ) , 1 )
self.assertEqual(len(__lowerCamelCase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() ) | 698 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = ShapEPipeline
lowerCamelCase__ = ["prompt"]
lowerCamelCase__ = ["prompt"]
lowerCamelCase__ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowerCamelCase__ = False
@property
def _snake_case ( self : List[str] ):
return 32
@property
def _snake_case ( self : str ):
return 32
@property
def _snake_case ( self : str ):
return self.time_input_dim * 4
@property
def _snake_case ( self : str ):
return 8
@property
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def _snake_case ( self : List[str] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_ )
@property
def _snake_case ( self : int ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
SCREAMING_SNAKE_CASE = PriorTransformer(**UpperCamelCase_ )
return model
@property
def _snake_case ( self : List[Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
SCREAMING_SNAKE_CASE = ShapERenderer(**UpperCamelCase_ )
return model
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.dummy_prior
SCREAMING_SNAKE_CASE = self.dummy_text_encoder
SCREAMING_SNAKE_CASE = self.dummy_tokenizer
SCREAMING_SNAKE_CASE = self.dummy_renderer
SCREAMING_SNAKE_CASE = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def _snake_case ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Any=0 ):
if str(UpperCamelCase_ ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(UpperCamelCase_ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
SCREAMING_SNAKE_CASE = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = "cpu"
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
SCREAMING_SNAKE_CASE = output.images[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _snake_case ( self : int ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = torch_device == "cpu"
SCREAMING_SNAKE_CASE = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase_ )
SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase_ )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE = batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy" )
SCREAMING_SNAKE_CASE = ShapEPipeline.from_pretrained("openai/shap-e" )
SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 710 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
rect.set_stroke(__lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__lowerCamelCase , buff=0.0 )
self.add(__lowerCamelCase )
cpu_targs.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Loaded Checkpoint" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , aligned_edge=__lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCamelCase ) , Write(__lowerCamelCase ) )
self.play(Write(__lowerCamelCase , run_time=1 ) , Create(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(__lowerCamelCase , opacity=0.7 )
target.move_to(__lowerCamelCase )
first_animations.append(GrowFromCenter(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__lowerCamelCase , run_time=1.5 ) )
self.play(*__lowerCamelCase )
self.play(*__lowerCamelCase )
self.wait() | 698 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = 4_2
lowerCamelCase__ = None
def __a ( A__ : Optional[int] , A__ : List[Any]=0.9_9_9 , A__ : Any="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(A__ : List[Any] ):
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(A__ : Optional[Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
SCREAMING_SNAKE_CASE = []
for i in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE = i / num_diffusion_timesteps
SCREAMING_SNAKE_CASE = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase ) / alpha_bar_fn(_lowerCAmelCase ) , _lowerCAmelCase ) )
return torch.tensor(_lowerCAmelCase , dtype=torch.floataa )
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
@register_to_config
def __init__( self : Any , __lowerCamelCase : int = 1000 , __lowerCamelCase : str = "fixed_small_log" , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[float] = 1.0 , __lowerCamelCase : str = "epsilon" , __lowerCamelCase : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
SCREAMING_SNAKE_CASE = betas_for_alpha_bar(__A )
SCREAMING_SNAKE_CASE = 1.0 - self.betas
SCREAMING_SNAKE_CASE = torch.cumprod(self.alphas , dim=0 )
SCREAMING_SNAKE_CASE = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
SCREAMING_SNAKE_CASE = 1.0
# setable values
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = torch.from_numpy(np.arange(0 , __A )[::-1].copy() )
SCREAMING_SNAKE_CASE = variance_type
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : Optional[int] = None ):
return sample
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : Union[str, torch.device] = None ):
SCREAMING_SNAKE_CASE = num_inference_steps
SCREAMING_SNAKE_CASE = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
SCREAMING_SNAKE_CASE = (np.arange(0 , __A ) * step_ratio).round()[::-1].copy().astype(np.intaa )
SCREAMING_SNAKE_CASE = torch.from_numpy(__A ).to(__A )
def _snake_case ( self : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : str=None , __lowerCamelCase : str=None , __lowerCamelCase : Union[str, Any]=None ):
if prev_timestep is None:
SCREAMING_SNAKE_CASE = t - 1
SCREAMING_SNAKE_CASE = self.alphas_cumprod[t]
SCREAMING_SNAKE_CASE = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
SCREAMING_SNAKE_CASE = self.betas[t]
else:
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
SCREAMING_SNAKE_CASE = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
SCREAMING_SNAKE_CASE = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
SCREAMING_SNAKE_CASE = torch.log(torch.clamp(__A , min=1e-20 ) )
SCREAMING_SNAKE_CASE = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
SCREAMING_SNAKE_CASE = variance.log()
SCREAMING_SNAKE_CASE = beta.log()
SCREAMING_SNAKE_CASE = (predicted_variance + 1) / 2
SCREAMING_SNAKE_CASE = frac * max_log + (1 - frac) * min_log
return variance
def _snake_case ( self : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : int , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : bool = True , ):
SCREAMING_SNAKE_CASE = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
SCREAMING_SNAKE_CASE = torch.split(__A , sample.shape[1] , dim=1 )
else:
SCREAMING_SNAKE_CASE = None
# 1. compute alphas, betas
if prev_timestep is None:
SCREAMING_SNAKE_CASE = t - 1
SCREAMING_SNAKE_CASE = self.alphas_cumprod[t]
SCREAMING_SNAKE_CASE = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
SCREAMING_SNAKE_CASE = self.betas[t]
SCREAMING_SNAKE_CASE = self.alphas[t]
else:
SCREAMING_SNAKE_CASE = 1 - alpha_prod_t / alpha_prod_t_prev
SCREAMING_SNAKE_CASE = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
SCREAMING_SNAKE_CASE = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
SCREAMING_SNAKE_CASE = model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
SCREAMING_SNAKE_CASE = torch.clamp(
__A , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
SCREAMING_SNAKE_CASE = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
SCREAMING_SNAKE_CASE = 0
if t > 0:
SCREAMING_SNAKE_CASE = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=__A , device=model_output.device )
SCREAMING_SNAKE_CASE = self._get_variance(
__A , predicted_variance=__A , prev_timestep=__A , )
if self.variance_type == "fixed_small_log":
SCREAMING_SNAKE_CASE = variance
elif self.variance_type == "learned_range":
SCREAMING_SNAKE_CASE = (0.5 * variance).exp()
else:
raise ValueError(
f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
" for the UnCLIPScheduler." )
SCREAMING_SNAKE_CASE = variance * variance_noise
SCREAMING_SNAKE_CASE = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=__A , pred_original_sample=__A )
def _snake_case ( self : Optional[Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
SCREAMING_SNAKE_CASE = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE = alphas_cumprod[timesteps] ** 0.5
SCREAMING_SNAKE_CASE = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
SCREAMING_SNAKE_CASE = sqrt_alpha_prod.unsqueeze(-1 )
SCREAMING_SNAKE_CASE = (1 - alphas_cumprod[timesteps]) ** 0.5
SCREAMING_SNAKE_CASE = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
SCREAMING_SNAKE_CASE = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
SCREAMING_SNAKE_CASE = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples | 711 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Any , **__lowerCamelCase : Any ):
pass
@is_pipeline_test
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCamelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@require_tf
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@slow
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , ) | 698 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __a ( A__ : Union[str, Any] , A__ : Any ):
SCREAMING_SNAKE_CASE = int(__lowerCAmelCase )
assert noofclusters < len(__lowerCAmelCase )
# Find out the dimensionality
SCREAMING_SNAKE_CASE = len(vectors[0] )
# Will help select random centroids from among the available vectors
SCREAMING_SNAKE_CASE = list(range(len(__lowerCAmelCase ) ) )
shuffle(__lowerCAmelCase )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
SCREAMING_SNAKE_CASE = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
SCREAMING_SNAKE_CASE = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
SCREAMING_SNAKE_CASE = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(__lowerCAmelCase )
]
##These nodes will assign the centroid Variables the appropriate
##values
SCREAMING_SNAKE_CASE = tf.placeholder("float64" , [dim] )
SCREAMING_SNAKE_CASE = []
for centroid in centroids:
cent_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
SCREAMING_SNAKE_CASE = [tf.Variable(0 ) for i in range(len(__lowerCAmelCase ) )]
##These nodes will assign an assignment Variable the appropriate
##value
SCREAMING_SNAKE_CASE = tf.placeholder("int32" )
SCREAMING_SNAKE_CASE = []
for assignment in assignments:
cluster_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
SCREAMING_SNAKE_CASE = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
SCREAMING_SNAKE_CASE = tf.reduce_mean(__lowerCAmelCase , 0 )
##Node for computing Euclidean distances
# Placeholders for input
SCREAMING_SNAKE_CASE = tf.placeholder("float" , [dim] )
SCREAMING_SNAKE_CASE = tf.placeholder("float" , [dim] )
SCREAMING_SNAKE_CASE = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCAmelCase , __lowerCAmelCase ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
SCREAMING_SNAKE_CASE = tf.placeholder("float" , [noofclusters] )
SCREAMING_SNAKE_CASE = tf.argmin(__lowerCAmelCase , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
SCREAMING_SNAKE_CASE = tf.initialize_all_variables()
# Initialize all variables
sess.run(__lowerCAmelCase )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
SCREAMING_SNAKE_CASE = 100
for _ in range(__lowerCAmelCase ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
SCREAMING_SNAKE_CASE = [
sess.run(__lowerCAmelCase , feed_dict={va: vect, va: sess.run(__lowerCAmelCase )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
SCREAMING_SNAKE_CASE = sess.run(
__lowerCAmelCase , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(__lowerCAmelCase ):
# Collect all the vectors assigned to this cluster
SCREAMING_SNAKE_CASE = [
vectors[i]
for i in range(len(__lowerCAmelCase ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
SCREAMING_SNAKE_CASE = sess.run(
__lowerCAmelCase , feed_dict={mean_input: array(__lowerCAmelCase )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
SCREAMING_SNAKE_CASE = sess.run(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = sess.run(__lowerCAmelCase )
return centroids, assignments
| 712 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.6_0217_6634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def __a ( A__ : str , A__ : str , A__ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE = (
F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
F"Valid values are: {', '.join(A__ )}"
)
raise ValueError(A__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __a ( ):
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument(
"-m" , "--pretrained_model_name_or_path" , type=__UpperCAmelCase , default=__UpperCAmelCase , required=__UpperCAmelCase , help="Path to pretrained model or model identifier from huggingface.co/models." , )
parser.add_argument(
"-c" , "--caption" , type=__UpperCAmelCase , default="robotic cat with wings" , help="Text used to generate images." , )
parser.add_argument(
"-n" , "--images_num" , type=__UpperCAmelCase , default=4 , help="How much images to generate." , )
parser.add_argument(
"-s" , "--seed" , type=__UpperCAmelCase , default=42 , help="Seed for random process." , )
parser.add_argument(
"-ci" , "--cuda_id" , type=__UpperCAmelCase , default=0 , help="cuda_id." , )
SCREAMING_SNAKE_CASE = parser.parse_args()
return args
def __a ( A__ : str , A__ : Optional[Any] , A__ : Optional[Any] ):
if not len(__UpperCAmelCase ) == rows * cols:
raise ValueError("The specified number of rows and columns are not correct." )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = imgs[0].size
SCREAMING_SNAKE_CASE = Image.new("RGB" , size=(cols * w, rows * h) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = grid.size
for i, img in enumerate(__UpperCAmelCase ):
grid.paste(__UpperCAmelCase , box=(i % cols * w, i // cols * h) )
return grid
def __a ( A__ : str , A__ : Tuple="robotic cat with wings" , A__ : Union[str, Any]=7.5 , A__ : Tuple=50 , A__ : Optional[int]=1 , A__ : Any=42 , ):
SCREAMING_SNAKE_CASE = torch.Generator(pipeline.device ).manual_seed(__UpperCAmelCase )
SCREAMING_SNAKE_CASE = pipeline(
__UpperCAmelCase , guidance_scale=__UpperCAmelCase , num_inference_steps=__UpperCAmelCase , generator=__UpperCAmelCase , num_images_per_prompt=__UpperCAmelCase , ).images
SCREAMING_SNAKE_CASE = int(math.sqrt(__UpperCAmelCase ) )
SCREAMING_SNAKE_CASE = image_grid(__UpperCAmelCase , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__A : Optional[int] = parse_args()
# Load models and create wrapper for stable diffusion
__A : Any = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__A : List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__A : Union[str, Any] = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__A : Optional[Any] = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__A : Any = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__A : List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__A : Union[str, Any] = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__A : Tuple = unet.to(torch.device('cuda', args.cuda_id))
__A : str = pipeline.to(unet.device)
__A , __A : Optional[Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__A : List[str] = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1))) | 713 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.values[key]
def _snake_case ( self : Union[str, Any] ):
return (
sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Tuple=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(__lowerCamelCase , __lowerCamelCase ) | 698 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = JukeboxTokenizer
lowerCamelCase__ = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": "I met a traveller from an antique land,\n Who said \"Two vast and trunkless legs of stone\n Stand in the desert. . . . Near them, on the sand,\n Half sunk a shattered visage lies, whose frown,\n And wrinkled lip, and sneer of cold command,\n Tell that its sculptor well those passions read\n Which yet survive, stamped on these lifeless things,\n The hand that mocked them, and the heart that fed;\n And on the pedestal, these words appear:\n My name is Ozymandias, King of Kings;\n Look on my Works, ye Mighty, and despair!\n Nothing beside remains. Round the decay\n Of that colossal Wreck, boundless and bare\n The lone and level sands stretch far away\n ",
}
@require_torch
def _snake_case ( self : int ):
import torch
SCREAMING_SNAKE_CASE = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics" )
SCREAMING_SNAKE_CASE = tokenizer(**self.metas )["input_ids"]
# fmt: off
SCREAMING_SNAKE_CASE = [
torch.tensor([[
0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def _snake_case ( self : Tuple ):
import torch
SCREAMING_SNAKE_CASE = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics" )
SCREAMING_SNAKE_CASE = tokenizer(**self.metas )["input_ids"]
# fmt: off
SCREAMING_SNAKE_CASE = [
torch.tensor([[
0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 714 |
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
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {
'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 _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "gpt_neo"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : str , __lowerCamelCase : Dict=50257 , __lowerCamelCase : Tuple=2048 , __lowerCamelCase : Optional[Any]=2048 , __lowerCamelCase : int=24 , __lowerCamelCase : int=[[["global", "local"], 12]] , __lowerCamelCase : int=16 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]=256 , __lowerCamelCase : Tuple="gelu_new" , __lowerCamelCase : Optional[Any]=0.0 , __lowerCamelCase : str=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : List[Any]=1e-5 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Dict=True , __lowerCamelCase : Dict=50256 , __lowerCamelCase : Optional[int]=50256 , **__lowerCamelCase : Dict , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_layers
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = window_size
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_dropout
SCREAMING_SNAKE_CASE = embed_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = attention_types
SCREAMING_SNAKE_CASE = self.expand_attention_types_params(__lowerCamelCase )
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=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
@staticmethod
def _snake_case ( __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __a ( A__ : str , A__ : List[Any] , A__ : List[str] , A__ : Union[str, Any] ):
import torch
SCREAMING_SNAKE_CASE = input.size()
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = shape[dimension]
SCREAMING_SNAKE_CASE = torch.arange(0 , A__ , A__ )
SCREAMING_SNAKE_CASE = torch.div(sizedim - size , A__ , rounding_mode="floor" ) + 1
SCREAMING_SNAKE_CASE = torch.arange(A__ ) + low_indices[:min_length][:, None]
SCREAMING_SNAKE_CASE = [slice(A__ )] * rank
SCREAMING_SNAKE_CASE = indices
SCREAMING_SNAKE_CASE = input[s]
SCREAMING_SNAKE_CASE = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A__ )
def __a ( A__ : Union[str, Any] , A__ : Optional[int] ):
import torch
SCREAMING_SNAKE_CASE = torch.arange(1 , A__ )
SCREAMING_SNAKE_CASE = torch.remainder(A__ , A__ )
SCREAMING_SNAKE_CASE = remainders == 0
SCREAMING_SNAKE_CASE = candidates[divisor_indices]
SCREAMING_SNAKE_CASE = torch.max(A__ )
return largest_divisor, torch.div(A__ , A__ , rounding_mode="floor" )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(__lowerCamelCase , direction="inputs" )
SCREAMING_SNAKE_CASE = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _snake_case ( self : Optional[int] ):
return self._config.num_heads
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE = super(__lowerCamelCase , self ).generate_dummy_inputs(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE = seqlen + 2
SCREAMING_SNAKE_CASE = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE = [
(torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase , dtype=__lowerCamelCase )] , dim=1 )
return ordered_inputs
@property
def _snake_case ( self : Optional[int] ):
return 13 | 698 | 0 |
from ...configuration_utils import PretrainedConfig
__A : str = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class _SCREAMING_SNAKE_CASE ( __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = "tapas"
def __init__( self : Optional[Any] , __lowerCamelCase : Dict=30522 , __lowerCamelCase : int=768 , __lowerCamelCase : int=12 , __lowerCamelCase : List[Any]=12 , __lowerCamelCase : List[Any]=3072 , __lowerCamelCase : int="gelu" , __lowerCamelCase : Tuple=0.1 , __lowerCamelCase : Union[str, Any]=0.1 , __lowerCamelCase : Tuple=1024 , __lowerCamelCase : str=[3, 256, 256, 2, 256, 256, 10] , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : Union[str, Any]=1e-12 , __lowerCamelCase : Dict=0 , __lowerCamelCase : int=10.0 , __lowerCamelCase : Dict=0 , __lowerCamelCase : List[Any]=1.0 , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : str=1.0 , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : Any=None , __lowerCamelCase : Optional[Any]=1.0 , __lowerCamelCase : Any=1.0 , __lowerCamelCase : Tuple=False , __lowerCamelCase : List[Any]=False , __lowerCamelCase : Any="ratio" , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : int=None , __lowerCamelCase : Dict=64 , __lowerCamelCase : int=32 , __lowerCamelCase : List[Any]=False , __lowerCamelCase : List[str]=True , __lowerCamelCase : int=False , __lowerCamelCase : Dict=False , __lowerCamelCase : List[str]=True , __lowerCamelCase : List[str]=False , __lowerCamelCase : str=None , __lowerCamelCase : List[Any]=None , **__lowerCamelCase : int , ):
super().__init__(pad_token_id=_UpperCamelCase , **_UpperCamelCase )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_sizes
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
# Fine-tuning task hyperparameters
SCREAMING_SNAKE_CASE = positive_label_weight
SCREAMING_SNAKE_CASE = num_aggregation_labels
SCREAMING_SNAKE_CASE = aggregation_loss_weight
SCREAMING_SNAKE_CASE = use_answer_as_supervision
SCREAMING_SNAKE_CASE = answer_loss_importance
SCREAMING_SNAKE_CASE = use_normalized_answer_loss
SCREAMING_SNAKE_CASE = huber_loss_delta
SCREAMING_SNAKE_CASE = temperature
SCREAMING_SNAKE_CASE = aggregation_temperature
SCREAMING_SNAKE_CASE = use_gumbel_for_cells
SCREAMING_SNAKE_CASE = use_gumbel_for_aggregation
SCREAMING_SNAKE_CASE = average_approximation_function
SCREAMING_SNAKE_CASE = cell_selection_preference
SCREAMING_SNAKE_CASE = answer_loss_cutoff
SCREAMING_SNAKE_CASE = max_num_rows
SCREAMING_SNAKE_CASE = max_num_columns
SCREAMING_SNAKE_CASE = average_logits_per_cell
SCREAMING_SNAKE_CASE = select_one_column
SCREAMING_SNAKE_CASE = allow_empty_column_selection
SCREAMING_SNAKE_CASE = init_cell_selection_weights_to_zero
SCREAMING_SNAKE_CASE = reset_position_index_per_cell
SCREAMING_SNAKE_CASE = disable_per_token_loss
# Aggregation hyperparameters
SCREAMING_SNAKE_CASE = aggregation_labels
SCREAMING_SNAKE_CASE = no_aggregation_label_index
if isinstance(self.aggregation_labels , _UpperCamelCase ):
SCREAMING_SNAKE_CASE = {int(_UpperCamelCase ): v for k, v in aggregation_labels.items()}
| 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'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
__A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
import numpy as np
def __a ( A__ : List[str] ):
return 1 / (1 + np.exp(-vector ))
def __a ( A__ : int ):
return vector * sigmoid(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 716 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
from math import factorial
def __a ( A__ : Optional[Any] = 100 ):
return sum(map(_lowerCamelCase , str(factorial(_lowerCamelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 717 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(A__ , A__ , bias=A__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Tuple , A__ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE = {}
for old_key in state_dict.keys():
SCREAMING_SNAKE_CASE = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts.0" , F"ffn.experts.expert_{expert_idx}" )
else:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
SCREAMING_SNAKE_CASE = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
SCREAMING_SNAKE_CASE = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("final_layer_norm" , "ff_layer_norm" )
SCREAMING_SNAKE_CASE = state_dict[old_key]
return new_dict
def __a ( A__ : List[str] , A__ : List[Any] , A__ : str , A__ : Union[str, Any] , A__ : str = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
os.makedirs(A__ , exist_ok=A__ )
for expert in range(A__ ):
SCREAMING_SNAKE_CASE = switch_checkpoint_path + F"-rank-{expert}.pt"
if os.path.isfile(A__ ):
SCREAMING_SNAKE_CASE = torch.load(A__ )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = os.path.join(
A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
torch.save(A__ , A__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A__ )[0]].dtype )
# Add the last block
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
SCREAMING_SNAKE_CASE = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A__ ) == 1:
SCREAMING_SNAKE_CASE = os.path.join(A__ , A__ )
torch.save(A__ , A__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A__ , A__ )
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(A__ ):
SCREAMING_SNAKE_CASE = weights_name.replace(".bin" , F"-{idx+1:05d}-of-{len(A__ ):05d}.bin" )
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
for key in shard:
SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE = {"total_size": total_size}
SCREAMING_SNAKE_CASE = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A__ , A__ ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(A__ , indent=2 , sort_keys=A__ ) + "\n"
f.write(A__ )
return metadata, index
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : Optional[int] = parser.parse_args()
__A , __A : Union[str, Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
__A : Any = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : Any = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path) | 698 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = (UnCLIPScheduler,)
def _snake_case ( self : str , **__lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = {
'''num_train_timesteps''': 1000,
'''variance_type''': '''fixed_small_log''',
'''clip_sample''': True,
'''clip_sample_range''': 1.0,
'''prediction_type''': '''epsilon''',
}
config.update(**lowerCamelCase_ )
return config
def _snake_case ( self : Tuple ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase_ )
def _snake_case ( self : Optional[Any] ):
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=lowerCamelCase_ )
def _snake_case ( self : str ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCamelCase_ )
def _snake_case ( self : Union[str, Any] ):
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=lowerCamelCase_ )
def _snake_case ( self : Dict ):
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=lowerCamelCase_ )
def _snake_case ( self : Union[str, Any] ):
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=lowerCamelCase_ , prev_timestep=lowerCamelCase_ )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config(variance_type="fixed_small_log" )
SCREAMING_SNAKE_CASE = scheduler_class(**lowerCamelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_994_987 ) ) < 1e-5
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config(variance_type="learned_range" )
SCREAMING_SNAKE_CASE = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE = 0.5
assert scheduler._get_variance(1 , predicted_variance=lowerCamelCase_ ) - -10.1712790 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=lowerCamelCase_ ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=lowerCamelCase_ ) - -0.0_010_011 < 1e-5
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE = scheduler.timesteps
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for i, t in enumerate(lowerCamelCase_ ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE = model(lowerCamelCase_ , lowerCamelCase_ )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE = pred_prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 252.2682495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**lowerCamelCase_ )
scheduler.set_timesteps(25 )
SCREAMING_SNAKE_CASE = scheduler.timesteps
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for i, t in enumerate(lowerCamelCase_ ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE = model(lowerCamelCase_ , lowerCamelCase_ )
if i + 1 == timesteps.shape[0]:
SCREAMING_SNAKE_CASE = None
else:
SCREAMING_SNAKE_CASE = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE = scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , prev_timestep=lowerCamelCase_ , generator=lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE = pred_prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 258.2044983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def _snake_case ( self : Tuple ):
pass
def _snake_case ( self : Optional[int] ):
pass | 718 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCamelCase , __lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self : Dict , **__lowerCamelCase : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).tokenizer
def _snake_case ( self : List[Any] , **__lowerCamelCase : Optional[Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).image_processor
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCamelCase ):
processor()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 698 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__A : List[str] = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) )
SCREAMING_SNAKE_CASE = self.diffusers_dir
shutil.copy(
os.path.join(__UpperCamelCase , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = "src/diffusers"
shutil.rmtree(self.diffusers_dir )
def _snake_case ( self : str , __lowerCamelCase : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any]=None ):
SCREAMING_SNAKE_CASE = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
SCREAMING_SNAKE_CASE = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
SCREAMING_SNAKE_CASE = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
SCREAMING_SNAKE_CASE = black.format_str(__UpperCamelCase , mode=__UpperCamelCase )
SCREAMING_SNAKE_CASE = os.path.join(self.diffusers_dir , "new_code.py" )
with open(__UpperCamelCase , "w" , newline="\n" ) as f:
f.write(__UpperCamelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__UpperCamelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__UpperCamelCase )
with open(__UpperCamelCase , "r" ) as f:
self.assertTrue(f.read() , __UpperCamelCase )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def _snake_case ( self : List[Any] ):
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , __UpperCamelCase , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , __UpperCamelCase ) , )
# Copy consistency with a really long name
SCREAMING_SNAKE_CASE = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , f"{long_class_name}SchedulerOutput" , re.sub("Bert" , __UpperCamelCase , __UpperCamelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , __UpperCamelCase , overwrite_result=re.sub("DDPM" , "Test" , __UpperCamelCase ) , ) | 719 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
pass
def __a ( A__ : str ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A : Tuple = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model=__lowerCamelCase , tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
SCREAMING_SNAKE_CASE = "What is the placebo?"
SCREAMING_SNAKE_CASE = [
{
"image": load_image(__lowerCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = dqa_pipeline(__lowerCamelCase , top_k=2 )
self.assertEqual(
__lowerCamelCase , [
[
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "How many cats are there?"
SCREAMING_SNAKE_CASE = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , words=__lowerCamelCase , boxes=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _snake_case ( self : List[Any] ):
pass | 698 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__A : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__A : list[int] = [ord(letter) for letter in string.ascii_lowercase]
__A : set[int] = {ord(char) for char in VALID_CHARS}
__A : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __a ( A__ : Any , A__ : str ):
SCREAMING_SNAKE_CASE = ""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
for keychar, cipherchar in zip(cycle(A__ ) , A__ ):
SCREAMING_SNAKE_CASE = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(A__ )
return decoded
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = []
for key in product(A__ , repeat=3 ):
SCREAMING_SNAKE_CASE = try_key(A__ , A__ )
if encoded is not None:
possibles.append(A__ )
return possibles
def __a ( A__ : Dict , A__ : Union[str, Any] ):
return [possible for possible in possibles if common_word in possible.lower()]
def __a ( A__ : Optional[Any] = "p059_cipher.txt" ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = Path(A__ ).parent.joinpath(A__ ).read_text(encoding="utf-8" )
SCREAMING_SNAKE_CASE = [int(A__ ) for number in data.strip().split("," )]
SCREAMING_SNAKE_CASE = filter_valid_chars(A__ )
for common_word in COMMON_WORDS:
SCREAMING_SNAKE_CASE = filter_common_word(A__ , A__ )
if len(A__ ) == 1:
break
SCREAMING_SNAKE_CASE = possibles[0]
return sum(ord(A__ ) for char in decoded_text )
if __name__ == "__main__":
print(f'{solution() = }') | 720 |
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "facebook/bart-large-mnli"
lowerCamelCase__ = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
lowerCamelCase__ = "text_classifier"
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSequenceClassification
lowerCamelCase__ = ["text", ["text"]]
lowerCamelCase__ = ["text"]
def _snake_case ( self : Optional[Any] ):
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__lowerCamelCase ) , [f"This example is {label}" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id] | 698 | 0 |
def __a ( A__ : int , A__ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
SCREAMING_SNAKE_CASE = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 721 |
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __a ( A__ : str=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("test" )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=A__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
SCREAMING_SNAKE_CASE = script_name
else:
SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
SCREAMING_SNAKE_CASE = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def __a ( ):
SCREAMING_SNAKE_CASE = test_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main() | 698 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__A : Any = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( _lowercase ):
'''simple docstring'''
lowerCamelCase__ = '''albert'''
def __init__( self : Any , __lowerCamelCase : List[str]=30000 , __lowerCamelCase : Tuple=128 , __lowerCamelCase : Tuple=4096 , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : Dict=1 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : str=16384 , __lowerCamelCase : Union[str, Any]=1 , __lowerCamelCase : List[Any]="gelu_new" , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : Any=0 , __lowerCamelCase : Dict=512 , __lowerCamelCase : Dict=2 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[int]=1e-12 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : List[Any]="absolute" , __lowerCamelCase : Optional[int]=0 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : List[str]=3 , **__lowerCamelCase : List[Any] , ):
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_hidden_groups
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = inner_group_num
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = classifier_dropout_prob
SCREAMING_SNAKE_CASE = position_embedding_type
class _SCREAMING_SNAKE_CASE ( _lowercase ):
'''simple docstring'''
@property
def _snake_case ( self : List[str] ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 700 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[int] = logging.get_logger(__name__)
__A : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : Tuple = {
'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'
},
}
__A : Dict = {'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ):
SCREAMING_SNAKE_CASE = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE = bs[:]
SCREAMING_SNAKE_CASE = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def __a ( A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE = char
return pairs
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any]="replace" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : List[Any]="<mask>" , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE = bytes_to_unicode()
SCREAMING_SNAKE_CASE = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case ( self : Dict ):
return len(self.encoder )
def _snake_case ( self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = bigram
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = word
return word
def _snake_case ( self : str , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for token in re.findall(self.pat , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : List[str] , __lowerCamelCase : str ):
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Tuple ):
return self.decoder.get(__lowerCamelCase )
def _snake_case ( self : Tuple , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = "".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
SCREAMING_SNAKE_CASE = 0
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _snake_case ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 : Any , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE = " " + text
return (text, kwargs)
def _snake_case ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case ( self : Tuple , __lowerCamelCase : "Conversation" ):
SCREAMING_SNAKE_CASE = []
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(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
SCREAMING_SNAKE_CASE = 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 | 698 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Any = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
__A : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 701 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE_CASE = (-b + sqrt(A__ )) / (2 * a)
SCREAMING_SNAKE_CASE = (-b - sqrt(A__ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main() | 698 | 0 |
__A : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A : int = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A : Any = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
} | 702 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Dict ):
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 _snake_case ( self : int ):
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 _snake_case ( self : List[str] ):
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 _snake_case ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
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 _snake_case ( self : Optional[int] ):
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 | 698 | 0 |
from ...processing_utils import ProcessorMixin
class _SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ = "SpeechT5FeatureExtractor"
lowerCamelCase__ = "SpeechT5Tokenizer"
def __init__( self : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Any ):
super().__init__(__A , __A )
def __call__( self : Any , *__lowerCamelCase : Tuple , **__lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = kwargs.pop("audio" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("text" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("text_target" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("audio_target" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("sampling_rate" , __A )
if audio is not None and text is not None:
raise ValueError(
"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" )
if audio_target is not None and text_target is not None:
raise ValueError(
"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." )
if audio is not None:
SCREAMING_SNAKE_CASE = self.feature_extractor(__A , *__A , sampling_rate=__A , **__A )
elif text is not None:
SCREAMING_SNAKE_CASE = self.tokenizer(__A , **__A )
else:
SCREAMING_SNAKE_CASE = None
if audio_target is not None:
SCREAMING_SNAKE_CASE = self.feature_extractor(audio_target=__A , *__A , sampling_rate=__A , **__A )
SCREAMING_SNAKE_CASE = targets["input_values"]
elif text_target is not None:
SCREAMING_SNAKE_CASE = self.tokenizer(__A , **__A )
SCREAMING_SNAKE_CASE = targets["input_ids"]
else:
SCREAMING_SNAKE_CASE = None
if inputs is None:
return targets
if targets is not None:
SCREAMING_SNAKE_CASE = labels
SCREAMING_SNAKE_CASE = targets.get("attention_mask" )
if decoder_attention_mask is not None:
SCREAMING_SNAKE_CASE = decoder_attention_mask
return inputs
def _snake_case ( self : Optional[int] , *__lowerCamelCase : Any , **__lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = kwargs.pop("input_values" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("input_ids" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("labels" , __A )
if input_values is not None and input_ids is not None:
raise ValueError("Cannot process both `input_values` and `input_ids` inputs." )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." )
if input_values is not None:
SCREAMING_SNAKE_CASE = self.feature_extractor.pad(__A , *__A , **__A )
elif input_ids is not None:
SCREAMING_SNAKE_CASE = self.tokenizer.pad(__A , **__A )
else:
SCREAMING_SNAKE_CASE = None
if labels is not None:
if "input_ids" in labels or (isinstance(__A , __A ) and "input_ids" in labels[0]):
SCREAMING_SNAKE_CASE = self.tokenizer.pad(__A , **__A )
SCREAMING_SNAKE_CASE = targets["input_ids"]
else:
SCREAMING_SNAKE_CASE = self.feature_extractor.feature_size
SCREAMING_SNAKE_CASE = self.feature_extractor.num_mel_bins
SCREAMING_SNAKE_CASE = self.feature_extractor.pad(__A , *__A , **__A )
SCREAMING_SNAKE_CASE = feature_size_hack
SCREAMING_SNAKE_CASE = targets["input_values"]
else:
SCREAMING_SNAKE_CASE = None
if inputs is None:
return targets
if targets is not None:
SCREAMING_SNAKE_CASE = labels
SCREAMING_SNAKE_CASE = targets.get("attention_mask" )
if decoder_attention_mask is not None:
SCREAMING_SNAKE_CASE = decoder_attention_mask
return inputs
def _snake_case ( self : int , *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Tuple ):
return self.tokenizer.batch_decode(*__A , **__A )
def _snake_case ( self : str , *__lowerCamelCase : Dict , **__lowerCamelCase : List[Any] ):
return self.tokenizer.decode(*__A , **__A ) | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers | 704 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 698 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__A : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__A : int = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(1_0_0_0_0):
out_file.write(data)
__A : Tuple = BeautifulSoup(res.text, 'html.parser')
__A : List[str] = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f'https://google.com{link.get("href")}')
| 705 |
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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "resnet"
lowerCamelCase__ = ["basic", "bottleneck"]
def __init__( self : Optional[Any] , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=64 , __lowerCamelCase : str=[256, 512, 1024, 2048] , __lowerCamelCase : str=[3, 4, 6, 3] , __lowerCamelCase : Optional[int]="bottleneck" , __lowerCamelCase : int="relu" , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = downsample_in_first_stage
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = version.parse("1.11" )
@property
def _snake_case ( self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : Optional[int] ):
return 1e-3 | 698 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__A : Dict = logging.get_logger('transformers.models.speecht5')
def __a ( A__ : int , A__ : int , A__ : Tuple ):
hf_model.apply_weight_norm()
SCREAMING_SNAKE_CASE = checkpoint["""input_conv.weight_g"""]
SCREAMING_SNAKE_CASE = checkpoint["""input_conv.weight_v"""]
SCREAMING_SNAKE_CASE = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
SCREAMING_SNAKE_CASE = checkpoint[F"upsamples.{i}.1.weight_g"]
SCREAMING_SNAKE_CASE = checkpoint[F"upsamples.{i}.1.weight_v"]
SCREAMING_SNAKE_CASE = checkpoint[F"upsamples.{i}.1.bias"]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
SCREAMING_SNAKE_CASE = checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]
SCREAMING_SNAKE_CASE = checkpoint["""output_conv.1.weight_g"""]
SCREAMING_SNAKE_CASE = checkpoint["""output_conv.1.weight_v"""]
SCREAMING_SNAKE_CASE = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def __a ( A__ : List[Any] , A__ : Union[str, Any] , A__ : int , A__ : str=None , A__ : Dict=None , ):
if config_path is not None:
SCREAMING_SNAKE_CASE = SpeechTaHifiGanConfig.from_pretrained(snake_case__ )
else:
SCREAMING_SNAKE_CASE = SpeechTaHifiGanConfig()
SCREAMING_SNAKE_CASE = SpeechTaHifiGan(snake_case__ )
SCREAMING_SNAKE_CASE = torch.load(snake_case__ )
load_weights(orig_checkpoint["model"]["generator"] , snake_case__ , snake_case__ )
SCREAMING_SNAKE_CASE = np.load(snake_case__ )
SCREAMING_SNAKE_CASE = stats[0].reshape(-1 )
SCREAMING_SNAKE_CASE = stats[1].reshape(-1 )
SCREAMING_SNAKE_CASE = torch.from_numpy(snake_case__ ).float()
SCREAMING_SNAKE_CASE = torch.from_numpy(snake_case__ ).float()
model.save_pretrained(snake_case__ )
if repo_id:
print("Pushing to the hub..." )
model.push_to_hub(snake_case__ )
if __name__ == "__main__":
__A : str = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
__A : int = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
) | 706 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "imagegpt"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , __lowerCamelCase : Any=512 + 1 , __lowerCamelCase : str=32 * 32 , __lowerCamelCase : Any=512 , __lowerCamelCase : Optional[int]=24 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]="quick_gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[Any]=1e-5 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : int=False , __lowerCamelCase : int=False , **__lowerCamelCase : Union[str, Any] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scale_attn_weights
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE = tie_word_embeddings
super().__init__(tie_word_embeddings=__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def _snake_case ( self : Optional[int] , __lowerCamelCase : "FeatureExtractionMixin" , __lowerCamelCase : int = 1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , ):
SCREAMING_SNAKE_CASE = self._generate_dummy_images(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dict(preprocessor(images=__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return inputs | 698 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = "ZinengTang/tvlt-base"
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
def _snake_case ( self : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
return TvltImageProcessor.from_pretrained(self.checkpoint , **__lowerCAmelCase )
def _snake_case ( self : Any , **__lowerCamelCase : Optional[int] ):
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **__lowerCAmelCase )
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , __lowerCAmelCase )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = np.ones([12000] )
SCREAMING_SNAKE_CASE = feature_extractor(__lowerCAmelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(audio=__lowerCAmelCase , return_tensors="np" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = np.ones([3, 224, 224] )
SCREAMING_SNAKE_CASE = image_processor(__lowerCAmelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCAmelCase , return_tensors="np" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
SCREAMING_SNAKE_CASE = np.ones([12000] )
SCREAMING_SNAKE_CASE = np.ones([3, 224, 224] )
SCREAMING_SNAKE_CASE = processor(audio=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["audio_values", "audio_mask", "pixel_values", "pixel_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = TvltProcessor(image_processor=__lowerCAmelCase , feature_extractor=__lowerCAmelCase )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="`processor` and `image_processor`+`feature_extractor` model input names do not match" , ) | 707 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@slow
@require_torch
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE = 4
def _map_to_encoder_decoder_inputs(__lowerCamelCase : str ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE = tokenizer(batch["article"] , padding="max_length" , truncation=__lowerCamelCase , max_length=512 )
SCREAMING_SNAKE_CASE = tokenizer(batch["highlights"] , padding="max_length" , truncation=__lowerCamelCase , max_length=128 )
SCREAMING_SNAKE_CASE = inputs.input_ids
SCREAMING_SNAKE_CASE = inputs.attention_mask
SCREAMING_SNAKE_CASE = outputs.input_ids
SCREAMING_SNAKE_CASE = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
SCREAMING_SNAKE_CASE = outputs.attention_mask
assert all(len(__lowerCamelCase ) == 512 for x in inputs.input_ids )
assert all(len(__lowerCamelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = pred.label_ids
SCREAMING_SNAKE_CASE = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__lowerCamelCase ) )] ) / len(__lowerCamelCase )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE = SeqaSeqTrainingArguments(
output_dir=__lowerCamelCase , per_device_train_batch_size=__lowerCamelCase , per_device_eval_batch_size=__lowerCamelCase , predict_with_generate=__lowerCamelCase , evaluation_strategy="steps" , do_train=__lowerCamelCase , do_eval=__lowerCamelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=__lowerCamelCase , args=__lowerCamelCase , compute_metrics=_compute_metrics , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , tokenizer=__lowerCamelCase , )
# start training
trainer.train() | 698 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
@slow
@require_torch
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE = 4
def _map_to_encoder_decoder_inputs(__lowerCamelCase : List[Any] ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE = tokenizer(batch["article"] , padding="max_length" , truncation=lowercase_ , max_length=512 )
SCREAMING_SNAKE_CASE = tokenizer(batch["highlights"] , padding="max_length" , truncation=lowercase_ , max_length=128 )
SCREAMING_SNAKE_CASE = inputs.input_ids
SCREAMING_SNAKE_CASE = inputs.attention_mask
SCREAMING_SNAKE_CASE = outputs.input_ids
SCREAMING_SNAKE_CASE = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
SCREAMING_SNAKE_CASE = outputs.attention_mask
assert all(len(lowercase_ ) == 512 for x in inputs.input_ids )
assert all(len(lowercase_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = pred.label_ids
SCREAMING_SNAKE_CASE = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
SCREAMING_SNAKE_CASE = sum([int(pred_str[i] == label_str[i] ) for i in range(len(lowercase_ ) )] ) / len(lowercase_ )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=lowercase_ , batch_size=lowercase_ , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=lowercase_ , batch_size=lowercase_ , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE = SeqaSeqTrainingArguments(
output_dir=lowercase_ , per_device_train_batch_size=lowercase_ , per_device_eval_batch_size=lowercase_ , predict_with_generate=lowercase_ , evaluation_strategy="steps" , do_train=lowercase_ , do_eval=lowercase_ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , compute_metrics=_compute_metrics , train_dataset=lowercase_ , eval_dataset=lowercase_ , tokenizer=lowercase_ , )
# start training
trainer.train() | 708 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "num_attention_heads" ) )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Dict=3 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : int=2 , __lowerCamelCase : List[Any]=1 , __lowerCamelCase : Optional[Any]=16 , __lowerCamelCase : Tuple=[128, 256, 384] , __lowerCamelCase : int=[4, 6, 8] , __lowerCamelCase : Tuple=[2, 3, 4] , __lowerCamelCase : List[str]=[16, 16, 16] , __lowerCamelCase : int=0 , __lowerCamelCase : List[Any]=[2, 2, 2] , __lowerCamelCase : List[str]=[2, 2, 2] , __lowerCamelCase : List[str]=0.02 , __lowerCamelCase : Any=True , __lowerCamelCase : Dict=True , __lowerCamelCase : int=2 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = kernel_size
SCREAMING_SNAKE_CASE = stride
SCREAMING_SNAKE_CASE = padding
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : List[str] ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _snake_case ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : str , __lowerCamelCase : Any ):
SCREAMING_SNAKE_CASE = LevitModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
SCREAMING_SNAKE_CASE = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _snake_case ( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = LevitForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = LevitModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Any ):
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 : Any ):
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def _snake_case ( self : Tuple ):
pass
@unittest.skip(reason="Levit does not output attentions" )
def _snake_case ( self : Tuple ):
pass
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : List[Any] ):
def check_hidden_states_output(__lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = len(self.model_tester.depths ) + 1
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.model_tester.image_size, self.model_tester.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
SCREAMING_SNAKE_CASE = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : int ):
pass
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : int=False ):
SCREAMING_SNAKE_CASE = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _snake_case ( self : List[Any] ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowerCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__lowerCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}" ):
SCREAMING_SNAKE_CASE = problem_type["title"]
SCREAMING_SNAKE_CASE = problem_type["num_labels"]
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
SCREAMING_SNAKE_CASE = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def _snake_case ( self : List[Any] ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = LevitModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Dict ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([1.0_448, -0.3_745, -1.8_317] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1e-4 ) ) | 698 | 0 |
import random
def __a ( A__ : list , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = [], [], []
for element in data:
if element < pivot:
less.append(_lowercase )
elif element > pivot:
greater.append(_lowercase )
else:
equal.append(_lowercase )
return less, equal, greater
def __a ( A__ : list , A__ : int ):
if index >= len(_lowercase ) or index < 0:
return None
SCREAMING_SNAKE_CASE = items[random.randint(0 , len(_lowercase ) - 1 )]
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = _partition(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE = len(_lowercase )
SCREAMING_SNAKE_CASE = len(_lowercase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(_lowercase , _lowercase )
# must be in larger
else:
return quick_select(_lowercase , index - (m + count) ) | 709 |
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 _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
return None
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : int ):
return None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : Optional[int] ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(__lowerCamelCase ) )
vocab_file.flush()
SCREAMING_SNAKE_CASE = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
SCREAMING_SNAKE_CASE = BertModel(BertConfig(vocab_size=len(__lowerCamelCase ) ) )
model.save_pretrained(__lowerCamelCase )
self._test_export(__lowerCamelCase , "pt" , 12 , __lowerCamelCase )
@require_tf
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(Path(__lowerCamelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(__lowerCamelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def _snake_case ( self : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , **__lowerCamelCase : List[str] ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE = Path(__lowerCamelCase ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
return path
except Exception as e:
self.fail(__lowerCamelCase )
@require_torch
@require_tokenizers
@slow
def _snake_case ( self : Dict ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "pt" )
@require_tf
@require_tokenizers
@slow
def _snake_case ( self : int ):
from transformers import TFBertModel
SCREAMING_SNAKE_CASE = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "tf" )
def _snake_case ( self : int , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = FeatureExtractionPipeline(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = infer_shapes(__lowerCamelCase , __lowerCamelCase )
# Assert all variables are present
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __lowerCamelCase )
self.assertSequenceEqual(variable_names[3:] , __lowerCamelCase )
# 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 _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask", "token_type_ids"]
SCREAMING_SNAKE_CASE = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__lowerCamelCase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__lowerCamelCase ) , set(__lowerCamelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__lowerCamelCase , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncNonContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__lowerCamelCase ) , 1 )
self.assertEqual(len(__lowerCamelCase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() ) | 698 | 0 |
def __a ( A__ : int = 2000000 ):
SCREAMING_SNAKE_CASE = [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 0
for i in range(UpperCAmelCase__ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 710 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
rect.set_stroke(__lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__lowerCamelCase , buff=0.0 )
self.add(__lowerCamelCase )
cpu_targs.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Loaded Checkpoint" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , aligned_edge=__lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCamelCase ) , Write(__lowerCamelCase ) )
self.play(Write(__lowerCamelCase , run_time=1 ) , Create(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(__lowerCamelCase , opacity=0.7 )
target.move_to(__lowerCamelCase )
first_animations.append(GrowFromCenter(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__lowerCamelCase , run_time=1.5 ) )
self.play(*__lowerCamelCase )
self.play(*__lowerCamelCase )
self.wait() | 698 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = OpenAIGPTTokenizer
lowerCamelCase__ = OpenAIGPTTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = False
def _snake_case ( self : List[Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
SCREAMING_SNAKE_CASE = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
SCREAMING_SNAKE_CASE = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
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" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(UpperCAmelCase_ ) )
def _snake_case ( self : str , __lowerCamelCase : Tuple ):
return "lower newer", "lower newer"
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
SCREAMING_SNAKE_CASE = "lower"
SCREAMING_SNAKE_CASE = ["low", "er</w>"]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE = tokens + ["<unk>"]
SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _snake_case ( self : List[str] , __lowerCamelCase : Tuple=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
# Simple input
SCREAMING_SNAKE_CASE = "This is a simple input"
SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
SCREAMING_SNAKE_CASE = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" )
# Simple input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" )
# Simple input
self.assertRaises(
UpperCAmelCase_ , tokenizer_r.batch_encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" , )
# Pair input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" )
# Pair input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" )
# Pair input
self.assertRaises(
UpperCAmelCase_ , tokenizer_r.batch_encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="max_length" , )
def _snake_case ( self : List[Any] ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
'''simple docstring'''
pass | 711 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Any , **__lowerCamelCase : Any ):
pass
@is_pipeline_test
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCamelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@require_tf
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@slow
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , ) | 698 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__A : Optional[int] = pytest.mark.integration
__A : int = {'comet'}
__A : Optional[int] = importlib.util.find_spec('fairseq') is not None
__A : Optional[Any] = {'code_eval'}
__A : List[str] = os.name == 'nt'
__A : Optional[Any] = {'bertscore', 'frugalscore', 'perplexity'}
__A : Dict = importlib.util.find_spec('transformers') is not None
def __a ( A__ : Any ):
@wraps(_lowercase )
def wrapper(self : Optional[Any] , A__ : Dict ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , _lowercase )
return wrapper
def __a ( A__ : int ):
@wraps(_lowercase )
def wrapper(self : List[str] , A__ : Any ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , _lowercase )
return wrapper
def __a ( A__ : Union[str, Any] ):
@wraps(_lowercase )
def wrapper(self : Optional[int] , A__ : Any ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , _lowercase )
return wrapper
def __a ( ):
SCREAMING_SNAKE_CASE = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@local
class _SCREAMING_SNAKE_CASE ( parameterized.TestCase ):
'''simple docstring'''
lowerCamelCase__ = {}
lowerCamelCase__ = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def _snake_case ( self : Dict , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = '''[...]'''
SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , UpperCamelCase__ ) ).module_path )
SCREAMING_SNAKE_CASE = datasets.load.import_main_class(metric_module.__name__ , dataset=UpperCamelCase__ )
# check parameters
SCREAMING_SNAKE_CASE = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(UpperCamelCase__ , metric_module.__name__ ):
with self.use_local_metrics():
try:
SCREAMING_SNAKE_CASE = doctest.testmod(UpperCamelCase__ , verbose=UpperCamelCase__ , raise_on_error=UpperCamelCase__ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = '''[...]'''
SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , UpperCamelCase__ ) ).module_path )
# run doctest
with self.use_local_metrics():
SCREAMING_SNAKE_CASE = doctest.testmod(UpperCamelCase__ , verbose=UpperCamelCase__ , raise_on_error=UpperCamelCase__ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def _snake_case ( self : int , __lowerCamelCase : Any , __lowerCamelCase : List[str] ):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCamelCase__ ):
yield
else:
yield
@contextmanager
def _snake_case ( self : Optional[Any] ):
def load_local_metric(__lowerCamelCase : List[Any] , *__lowerCamelCase : str , **__lowerCamelCase : Any ):
return load_metric(os.path.join("metrics" , UpperCamelCase__ ) , *UpperCamelCase__ , **UpperCamelCase__ )
with patch("datasets.load_metric" ) as mock_load_metric:
SCREAMING_SNAKE_CASE = load_local_metric
yield
@classmethod
def _snake_case ( cls : str , __lowerCamelCase : Dict ):
def wrapper(__lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = contextmanager(UpperCamelCase__ )
SCREAMING_SNAKE_CASE = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def __a ( A__ : List[Any] ):
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class _SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
'''simple docstring'''
def _snake_case ( self : Optional[int] , __lowerCamelCase : str ):
assert len(input_dict["input_ids"] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
SCREAMING_SNAKE_CASE = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def __a ( A__ : Optional[int] ):
import torch
def bert_cos_score_idf(A__ : Union[str, Any] , A__ : Any , *A__ : str , **A__ : Dict ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_lowercase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
SCREAMING_SNAKE_CASE = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def __a ( A__ : Optional[int] ):
def load_from_checkpoint(A__ : List[Any] ):
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Any , __lowerCamelCase : Optional[int] , *__lowerCamelCase : List[str] , **__lowerCamelCase : int ):
assert len(UpperCamelCase__ ) == 2
SCREAMING_SNAKE_CASE = [0.19, 0.92]
return scores, sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
SCREAMING_SNAKE_CASE = None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
SCREAMING_SNAKE_CASE = load_from_checkpoint
yield
def __a ( ):
SCREAMING_SNAKE_CASE = load_metric(os.path.join("metrics" , "seqeval" ) )
SCREAMING_SNAKE_CASE = '''ERROR'''
SCREAMING_SNAKE_CASE = F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
with pytest.raises(_lowercase , match=re.escape(_lowercase ) ):
metric.compute(predictions=[] , references=[] , scheme=_lowercase )
| 712 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.6_0217_6634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def __a ( A__ : str , A__ : str , A__ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE = (
F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
F"Valid values are: {', '.join(A__ )}"
)
raise ValueError(A__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
'''simple docstring'''
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__A : List[str] = 'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def __a ( A__ : Tuple=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("tpu-config" , description=_description )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
SCREAMING_SNAKE_CASE = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=__UpperCamelCase , default=__UpperCamelCase , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=__UpperCamelCase , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=__UpperCamelCase , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
SCREAMING_SNAKE_CASE = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=__UpperCamelCase , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(__UpperCamelCase ):
SCREAMING_SNAKE_CASE = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
SCREAMING_SNAKE_CASE = defaults.command_file
if not args.command and defaults.commands is not None:
SCREAMING_SNAKE_CASE = defaults.commands
if not args.tpu_name:
SCREAMING_SNAKE_CASE = defaults.tpu_name
if not args.tpu_zone:
SCREAMING_SNAKE_CASE = defaults.tpu_zone
if args.accelerate_version == "dev":
SCREAMING_SNAKE_CASE = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
SCREAMING_SNAKE_CASE = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , __UpperCamelCase ):
SCREAMING_SNAKE_CASE = F"accelerate=={args.accelerate_version}"
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
SCREAMING_SNAKE_CASE = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , __UpperCamelCase ):
SCREAMING_SNAKE_CASE = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
SCREAMING_SNAKE_CASE = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [F"pip install {args.accelerate_version}"]
new_cmd += args.command
SCREAMING_SNAKE_CASE = "; ".join(__UpperCamelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
SCREAMING_SNAKE_CASE = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"Running {' '.join(__UpperCamelCase )}" )
return
subprocess.run(__UpperCamelCase )
print("Successfully setup pod." )
def __a ( ):
SCREAMING_SNAKE_CASE = tpu_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
tpu_command_launcher(__UpperCamelCase ) | 713 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.values[key]
def _snake_case ( self : Union[str, Any] ):
return (
sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Tuple=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(__lowerCamelCase , __lowerCamelCase ) | 698 | 0 |
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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__A : Dict = logging.get_logger(__name__)
if is_vision_available():
import PIL
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
'''simple docstring'''
lowerCamelCase__ = ['pixel_values']
def __init__( self : Optional[Any] , __lowerCamelCase : bool = True , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , __lowerCamelCase : bool = True , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : bool = True , __lowerCamelCase : Union[int, float] = 1 / 255 , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[Union[float, List[float]]] = None , __lowerCamelCase : Optional[Union[float, List[float]]] = None , __lowerCamelCase : bool = True , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase , param_name="crop_size" )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_center_crop
SCREAMING_SNAKE_CASE = crop_size
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE = do_convert_rgb
def _snake_case ( self : Optional[Any] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Dict[str, int] , __lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : str , ):
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
SCREAMING_SNAKE_CASE = get_resize_output_image_size(__lowerCamelCase , size=size["shortest_edge"] , default_to_square=__lowerCamelCase )
return resize(__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : np.ndarray , __lowerCamelCase : Dict[str, int] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(__lowerCamelCase , size=(size["height"], size["width"]) , data_format=__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Optional[int] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Union[int, float] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : Optional[Any] , ):
return rescale(__lowerCamelCase , scale=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : np.ndarray , __lowerCamelCase : Union[float, List[float]] , __lowerCamelCase : Union[float, List[float]] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : str , ):
return normalize(__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Dict , __lowerCamelCase : ImageInput , __lowerCamelCase : bool = None , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : PILImageResampling = None , __lowerCamelCase : bool = None , __lowerCamelCase : int = None , __lowerCamelCase : bool = None , __lowerCamelCase : float = None , __lowerCamelCase : bool = None , __lowerCamelCase : Optional[Union[float, List[float]]] = None , __lowerCamelCase : Optional[Union[float, List[float]]] = None , __lowerCamelCase : bool = None , __lowerCamelCase : Optional[Union[str, TensorType]] = None , __lowerCamelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , **__lowerCamelCase : List[str] , ):
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase , param_name="size" , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE = get_size_dict(__lowerCamelCase , param_name="crop_size" , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE = make_list_of_images(__lowerCamelCase )
if not valid_images(__lowerCamelCase ):
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:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
SCREAMING_SNAKE_CASE = [convert_to_rgb(__lowerCamelCase ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(__lowerCamelCase ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE = [self.center_crop(image=__lowerCamelCase , size=__lowerCamelCase ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE = [self.rescale(image=__lowerCamelCase , scale=__lowerCamelCase ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE = [self.normalize(image=__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(__lowerCamelCase , __lowerCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = {"""pixel_values""": images}
return BatchFeature(data=__lowerCamelCase , tensor_type=__lowerCamelCase ) | 714 |
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
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {
'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 _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "gpt_neo"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : str , __lowerCamelCase : Dict=50257 , __lowerCamelCase : Tuple=2048 , __lowerCamelCase : Optional[Any]=2048 , __lowerCamelCase : int=24 , __lowerCamelCase : int=[[["global", "local"], 12]] , __lowerCamelCase : int=16 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]=256 , __lowerCamelCase : Tuple="gelu_new" , __lowerCamelCase : Optional[Any]=0.0 , __lowerCamelCase : str=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : List[Any]=1e-5 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Dict=True , __lowerCamelCase : Dict=50256 , __lowerCamelCase : Optional[int]=50256 , **__lowerCamelCase : Dict , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_layers
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = window_size
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_dropout
SCREAMING_SNAKE_CASE = embed_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = attention_types
SCREAMING_SNAKE_CASE = self.expand_attention_types_params(__lowerCamelCase )
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=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
@staticmethod
def _snake_case ( __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __a ( A__ : str , A__ : List[Any] , A__ : List[str] , A__ : Union[str, Any] ):
import torch
SCREAMING_SNAKE_CASE = input.size()
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = shape[dimension]
SCREAMING_SNAKE_CASE = torch.arange(0 , A__ , A__ )
SCREAMING_SNAKE_CASE = torch.div(sizedim - size , A__ , rounding_mode="floor" ) + 1
SCREAMING_SNAKE_CASE = torch.arange(A__ ) + low_indices[:min_length][:, None]
SCREAMING_SNAKE_CASE = [slice(A__ )] * rank
SCREAMING_SNAKE_CASE = indices
SCREAMING_SNAKE_CASE = input[s]
SCREAMING_SNAKE_CASE = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A__ )
def __a ( A__ : Union[str, Any] , A__ : Optional[int] ):
import torch
SCREAMING_SNAKE_CASE = torch.arange(1 , A__ )
SCREAMING_SNAKE_CASE = torch.remainder(A__ , A__ )
SCREAMING_SNAKE_CASE = remainders == 0
SCREAMING_SNAKE_CASE = candidates[divisor_indices]
SCREAMING_SNAKE_CASE = torch.max(A__ )
return largest_divisor, torch.div(A__ , A__ , rounding_mode="floor" )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(__lowerCamelCase , direction="inputs" )
SCREAMING_SNAKE_CASE = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _snake_case ( self : Optional[int] ):
return self._config.num_heads
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE = super(__lowerCamelCase , self ).generate_dummy_inputs(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE = seqlen + 2
SCREAMING_SNAKE_CASE = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE = [
(torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase , dtype=__lowerCamelCase )] , dim=1 )
return ordered_inputs
@property
def _snake_case ( self : Optional[int] ):
return 13 | 698 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple=None , __lowerCamelCase : str=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : Dict="resnet50" , __lowerCamelCase : Tuple=3 , __lowerCamelCase : Optional[int]=32 , __lowerCamelCase : List[str]=3 , __lowerCamelCase : Dict=True , __lowerCamelCase : List[str]=True , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = out_indices if out_indices is not None else [4]
SCREAMING_SNAKE_CASE = stage_names
SCREAMING_SNAKE_CASE = out_features
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = is_training
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values
def _snake_case ( self : int ):
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def _snake_case ( self : str , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = TimmBackbone(config=__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(__lowercase )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class _SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (TimmBackbone,) if is_torch_available() else ()
lowerCamelCase__ = {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = TimmBackboneModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase )
def _snake_case ( self : Union[str, Any] ):
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 : Optional[int] ):
SCREAMING_SNAKE_CASE = "resnet18"
SCREAMING_SNAKE_CASE = "microsoft/resnet-18"
SCREAMING_SNAKE_CASE = AutoBackbone.from_pretrained(__lowercase , use_timm_backbone=__lowercase )
SCREAMING_SNAKE_CASE = AutoBackbone.from_pretrained(__lowercase )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
SCREAMING_SNAKE_CASE = AutoBackbone.from_pretrained(__lowercase , use_timm_backbone=__lowercase , out_indices=[1, 2, 3] )
SCREAMING_SNAKE_CASE = AutoBackbone.from_pretrained(__lowercase , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("TimmBackbone doesn't support feed forward chunking" )
def _snake_case ( self : Optional[Any] ):
pass
@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
def _snake_case ( self : Optional[int] ):
pass
@unittest.skip("TimmBackbone initialization is managed on the timm side" )
def _snake_case ( self : Optional[int] ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def _snake_case ( self : Optional[Any] ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def _snake_case ( self : Dict ):
pass
@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
def _snake_case ( self : Optional[int] ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def _snake_case ( self : Dict ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def _snake_case ( self : List[Any] ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def _snake_case ( self : str ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def _snake_case ( self : List[str] ):
pass
@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
def _snake_case ( self : Any ):
pass
@unittest.skip("TimmBackbone doesn't support output_attentions." )
def _snake_case ( self : Dict ):
pass
@unittest.skip("Safetensors is not supported by timm." )
def _snake_case ( self : Optional[int] ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : Dict ):
pass
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowercase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowercase )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.has_attentions
# no need to test all models as different heads yield the same functionality
SCREAMING_SNAKE_CASE = self.all_model_classes[0]
SCREAMING_SNAKE_CASE = model_class(__lowercase )
model.to(__lowercase )
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE = model(**__lowercase )
SCREAMING_SNAKE_CASE = outputs[0][-1]
# Encoder-/Decoder-only models
SCREAMING_SNAKE_CASE = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
SCREAMING_SNAKE_CASE = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=__lowercase )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE = model(**__lowercase )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
SCREAMING_SNAKE_CASE = copy.deepcopy(__lowercase )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = model_class(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE = model(**__lowercase )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
SCREAMING_SNAKE_CASE = copy.deepcopy(__lowercase )
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = model_class(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE = model(**__lowercase )
| 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'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
__A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = ['image_processor']
lowerCamelCase__ = 'SamImageProcessor'
def __init__( self : List[Any] , __lowerCamelCase : Dict ):
super().__init__(A_ )
SCREAMING_SNAKE_CASE = self.image_processor
SCREAMING_SNAKE_CASE = -10
SCREAMING_SNAKE_CASE = self.image_processor.size["longest_edge"]
def __call__( self : Tuple , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Dict=None , __lowerCamelCase : int=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Optional[int] = None , **__lowerCamelCase : Optional[Any] , ):
SCREAMING_SNAKE_CASE = self.image_processor(
A_ , return_tensors=A_ , **A_ , )
# pop arguments that are not used in the foward but used nevertheless
SCREAMING_SNAKE_CASE = encoding_image_processor["original_sizes"]
if hasattr(A_ , "numpy" ): # Checks if Torch or TF tensor
SCREAMING_SNAKE_CASE = original_sizes.numpy()
SCREAMING_SNAKE_CASE = self._check_and_preprocess_points(
input_points=A_ , input_labels=A_ , input_boxes=A_ , )
SCREAMING_SNAKE_CASE = self._normalize_and_convert(
A_ , A_ , input_points=A_ , input_labels=A_ , input_boxes=A_ , return_tensors=A_ , )
return encoding_image_processor
def _snake_case ( self : Optional[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : str , __lowerCamelCase : int=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Tuple="pt" , ):
if input_points is not None:
if len(A_ ) != len(A_ ):
SCREAMING_SNAKE_CASE = [
self._normalize_coordinates(self.target_size , A_ , original_sizes[0] ) for point in input_points
]
else:
SCREAMING_SNAKE_CASE = [
self._normalize_coordinates(self.target_size , A_ , A_ )
for point, original_size in zip(A_ , A_ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
SCREAMING_SNAKE_CASE = self._pad_points_and_labels(A_ , A_ )
SCREAMING_SNAKE_CASE = np.array(A_ )
if input_labels is not None:
SCREAMING_SNAKE_CASE = np.array(A_ )
if input_boxes is not None:
if len(A_ ) != len(A_ ):
SCREAMING_SNAKE_CASE = [
self._normalize_coordinates(self.target_size , A_ , original_sizes[0] , is_bounding_box=A_ )
for box in input_boxes
]
else:
SCREAMING_SNAKE_CASE = [
self._normalize_coordinates(self.target_size , A_ , A_ , is_bounding_box=A_ )
for box, original_size in zip(A_ , A_ )
]
SCREAMING_SNAKE_CASE = np.array(A_ )
if input_boxes is not None:
if return_tensors == "pt":
SCREAMING_SNAKE_CASE = torch.from_numpy(A_ )
# boxes batch size of 1 by default
SCREAMING_SNAKE_CASE = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(A_ )
# boxes batch size of 1 by default
SCREAMING_SNAKE_CASE = tf.expand_dims(A_ , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"input_boxes": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
SCREAMING_SNAKE_CASE = torch.from_numpy(A_ )
# point batch size of 1 by default
SCREAMING_SNAKE_CASE = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(A_ )
# point batch size of 1 by default
SCREAMING_SNAKE_CASE = tf.expand_dims(A_ , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"input_points": input_points} )
if input_labels is not None:
if return_tensors == "pt":
SCREAMING_SNAKE_CASE = torch.from_numpy(A_ )
# point batch size of 1 by default
SCREAMING_SNAKE_CASE = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(A_ )
# point batch size of 1 by default
SCREAMING_SNAKE_CASE = tf.expand_dims(A_ , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"input_labels": input_labels} )
return encoding_image_processor
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = max([point.shape[0] for point in input_points] )
SCREAMING_SNAKE_CASE = []
for i, point in enumerate(A_ ):
if point.shape[0] != expected_nb_points:
SCREAMING_SNAKE_CASE = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
SCREAMING_SNAKE_CASE = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(A_ )
SCREAMING_SNAKE_CASE = processed_input_points
return input_points, input_labels
def _snake_case ( self : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : Union[str, Any]=False ):
SCREAMING_SNAKE_CASE = original_size
SCREAMING_SNAKE_CASE = self.image_processor._get_preprocess_shape(A_ , longest_edge=A_ )
SCREAMING_SNAKE_CASE = deepcopy(A_ ).astype(A_ )
if is_bounding_box:
SCREAMING_SNAKE_CASE = coords.reshape(-1 , 2 , 2 )
SCREAMING_SNAKE_CASE = coords[..., 0] * (new_w / old_w)
SCREAMING_SNAKE_CASE = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
SCREAMING_SNAKE_CASE = coords.reshape(-1 , 4 )
return coords
def _snake_case ( self : Optional[Any] , __lowerCamelCase : List[Any]=None , __lowerCamelCase : int=None , __lowerCamelCase : Tuple=None , ):
if input_points is not None:
if hasattr(A_ , "numpy" ): # Checks for TF or Torch tensor
SCREAMING_SNAKE_CASE = input_points.numpy().tolist()
if not isinstance(A_ , A_ ) or not isinstance(input_points[0] , A_ ):
raise ValueError("Input points must be a list of list of floating points." )
SCREAMING_SNAKE_CASE = [np.array(A_ ) for input_point in input_points]
else:
SCREAMING_SNAKE_CASE = None
if input_labels is not None:
if hasattr(A_ , "numpy" ):
SCREAMING_SNAKE_CASE = input_labels.numpy().tolist()
if not isinstance(A_ , A_ ) or not isinstance(input_labels[0] , A_ ):
raise ValueError("Input labels must be a list of list integers." )
SCREAMING_SNAKE_CASE = [np.array(A_ ) for label in input_labels]
else:
SCREAMING_SNAKE_CASE = None
if input_boxes is not None:
if hasattr(A_ , "numpy" ):
SCREAMING_SNAKE_CASE = input_boxes.numpy().tolist()
if (
not isinstance(A_ , A_ )
or not isinstance(input_boxes[0] , A_ )
or not isinstance(input_boxes[0][0] , A_ )
):
raise ValueError("Input boxes must be a list of list of list of floating points." )
SCREAMING_SNAKE_CASE = [np.array(A_ ).astype(np.floataa ) for box in input_boxes]
else:
SCREAMING_SNAKE_CASE = None
return input_points, input_labels, input_boxes
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(A_ ) )
def _snake_case ( self : Optional[int] , *__lowerCamelCase : Any , **__lowerCamelCase : int ):
return self.image_processor.post_process_masks(*A_ , **A_ ) | 716 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Optional[Any] = logging.get_logger(__name__)
__A : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : str = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
__A : Optional[Any] = {
'''squeezebert/squeezebert-uncased''': 5_1_2,
'''squeezebert/squeezebert-mnli''': 5_1_2,
'''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}
__A : Tuple = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class _SCREAMING_SNAKE_CASE ( UpperCamelCase__ ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = SqueezeBertTokenizer
def __init__( self : Optional[Any] , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Any="[UNK]" , __lowerCamelCase : List[str]="[SEP]" , __lowerCamelCase : List[str]="[PAD]" , __lowerCamelCase : List[Any]="[CLS]" , __lowerCamelCase : Optional[int]="[MASK]" , __lowerCamelCase : Any=True , __lowerCamelCase : Any=None , **__lowerCamelCase : List[str] , ):
super().__init__(
__lowerCamelCase , tokenizer_file=__lowerCamelCase , do_lower_case=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , tokenize_chinese_chars=__lowerCamelCase , strip_accents=__lowerCamelCase , **__lowerCamelCase , )
SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , __lowerCamelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , __lowerCamelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , __lowerCamelCase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE = getattr(__lowerCamelCase , normalizer_state.pop("type" ) )
SCREAMING_SNAKE_CASE = do_lower_case
SCREAMING_SNAKE_CASE = strip_accents
SCREAMING_SNAKE_CASE = tokenize_chinese_chars
SCREAMING_SNAKE_CASE = normalizer_class(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = do_lower_case
def _snake_case ( self : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : List[str]=None ):
SCREAMING_SNAKE_CASE = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _snake_case ( self : Tuple , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 _snake_case ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any = None ):
SCREAMING_SNAKE_CASE = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase )
return tuple(__lowerCamelCase ) | 717 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(A__ , A__ , bias=A__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Tuple , A__ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE = {}
for old_key in state_dict.keys():
SCREAMING_SNAKE_CASE = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts.0" , F"ffn.experts.expert_{expert_idx}" )
else:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
SCREAMING_SNAKE_CASE = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
SCREAMING_SNAKE_CASE = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("final_layer_norm" , "ff_layer_norm" )
SCREAMING_SNAKE_CASE = state_dict[old_key]
return new_dict
def __a ( A__ : List[str] , A__ : List[Any] , A__ : str , A__ : Union[str, Any] , A__ : str = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
os.makedirs(A__ , exist_ok=A__ )
for expert in range(A__ ):
SCREAMING_SNAKE_CASE = switch_checkpoint_path + F"-rank-{expert}.pt"
if os.path.isfile(A__ ):
SCREAMING_SNAKE_CASE = torch.load(A__ )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = os.path.join(
A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
torch.save(A__ , A__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A__ )[0]].dtype )
# Add the last block
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
SCREAMING_SNAKE_CASE = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A__ ) == 1:
SCREAMING_SNAKE_CASE = os.path.join(A__ , A__ )
torch.save(A__ , A__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A__ , A__ )
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(A__ ):
SCREAMING_SNAKE_CASE = weights_name.replace(".bin" , F"-{idx+1:05d}-of-{len(A__ ):05d}.bin" )
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
for key in shard:
SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE = {"total_size": total_size}
SCREAMING_SNAKE_CASE = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A__ , A__ ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(A__ , indent=2 , sort_keys=A__ ) + "\n"
f.write(A__ )
return metadata, index
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : Optional[int] = parser.parse_args()
__A , __A : Union[str, Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
__A : Any = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : Any = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path) | 698 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : List[Any] = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
'''simple docstring'''
lowerCamelCase__ = "gptj"
lowerCamelCase__ = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[Any] , __lowerCamelCase : str=50400 , __lowerCamelCase : Optional[Any]=2048 , __lowerCamelCase : List[str]=4096 , __lowerCamelCase : List[str]=28 , __lowerCamelCase : List[str]=16 , __lowerCamelCase : List[str]=64 , __lowerCamelCase : Dict=None , __lowerCamelCase : Optional[int]="gelu_new" , __lowerCamelCase : Dict=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=0.0 , __lowerCamelCase : Tuple=1e-5 , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : List[str]=True , __lowerCamelCase : Tuple=50256 , __lowerCamelCase : Dict=50256 , __lowerCamelCase : List[str]=False , **__lowerCamelCase : Optional[int] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = rotary_dim
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
super().__init__(
bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , tie_word_embeddings=lowerCamelCase__ , **lowerCamelCase__ )
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : PretrainedConfig , __lowerCamelCase : str = "default" , __lowerCamelCase : List[PatchingSpec] = None , __lowerCamelCase : bool = False , ):
super().__init__(lowerCamelCase__ , task=lowerCamelCase__ , patching_specs=lowerCamelCase__ , use_past=lowerCamelCase__ )
if not getattr(self._config , "pad_token_id" , lowerCamelCase__ ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE = 0
@property
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ , direction="inputs" )
SCREAMING_SNAKE_CASE = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _snake_case ( self : List[str] ):
return self._config.n_layer
@property
def _snake_case ( self : Dict ):
return self._config.n_head
def _snake_case ( self : int , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE = super(lowerCamelCase__ , self ).generate_dummy_inputs(
lowerCamelCase__ , batch_size=lowerCamelCase__ , seq_length=lowerCamelCase__ , is_pair=lowerCamelCase__ , framework=lowerCamelCase__ )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE = seqlen + 2
SCREAMING_SNAKE_CASE = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE = [
(torch.zeros(lowerCamelCase__ ), torch.zeros(lowerCamelCase__ )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(lowerCamelCase__ , lowerCamelCase__ , dtype=lowerCamelCase__ )] , dim=1 )
return ordered_inputs
@property
def _snake_case ( self : List[str] ):
return 13 | 718 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCamelCase , __lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self : Dict , **__lowerCamelCase : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).tokenizer
def _snake_case ( self : List[Any] , **__lowerCamelCase : Optional[Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).image_processor
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCamelCase ):
processor()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 698 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __a ( A__ : List[Any] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def __a ( A__ : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(lowercase__ , lowercase__ , bias=lowercase__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Any , A__ : Tuple="facebook/mbart-large-en-ro" , A__ : List[str]=False , A__ : Union[str, Any]=False ):
SCREAMING_SNAKE_CASE = torch.load(lowercase__ , map_location="cpu" )["model"]
remove_ignore_keys_(lowercase__ )
SCREAMING_SNAKE_CASE = state_dict["encoder.embed_tokens.weight"].shape[0]
SCREAMING_SNAKE_CASE = MBartConfig.from_pretrained(lowercase__ , vocab_size=lowercase__ )
if mbart_aa and finetuned:
SCREAMING_SNAKE_CASE = "relu"
SCREAMING_SNAKE_CASE = state_dict["decoder.embed_tokens.weight"]
SCREAMING_SNAKE_CASE = MBartForConditionalGeneration(lowercase__ )
model.model.load_state_dict(lowercase__ )
if finetuned:
SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
)
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--hf_config',
default='facebook/mbart-large-cc25',
type=str,
help='Which huggingface architecture to use: mbart-large',
)
parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
__A : Union[str, Any] = parser.parse_args()
__A : List[Any] = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path) | 719 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
pass
def __a ( A__ : str ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A : Tuple = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model=__lowerCamelCase , tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
SCREAMING_SNAKE_CASE = "What is the placebo?"
SCREAMING_SNAKE_CASE = [
{
"image": load_image(__lowerCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = dqa_pipeline(__lowerCamelCase , top_k=2 )
self.assertEqual(
__lowerCamelCase , [
[
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "How many cats are there?"
SCREAMING_SNAKE_CASE = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , words=__lowerCamelCase , boxes=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _snake_case ( self : List[Any] ):
pass | 698 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "external_deps", "test_metrics.py"] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
SCREAMING_SNAKE_CASE = test_metrics
@require_cpu
def _snake_case ( self : Tuple ):
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def _snake_case ( self : int ):
debug_launcher(self.test_metrics.main )
@require_single_gpu
def _snake_case ( self : Dict ):
self.test_metrics.main()
@require_multi_gpu
def _snake_case ( self : str ):
print(f"Found {torch.cuda.device_count()} devices." )
SCREAMING_SNAKE_CASE = ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__a , env=os.environ.copy() ) | 720 |
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "facebook/bart-large-mnli"
lowerCamelCase__ = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
lowerCamelCase__ = "text_classifier"
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSequenceClassification
lowerCamelCase__ = ["text", ["text"]]
lowerCamelCase__ = ["text"]
def _snake_case ( self : Optional[Any] ):
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__lowerCamelCase ) , [f"This example is {label}" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id] | 698 | 0 |
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 ( A__ : Dataset , A__ : Dict[str, str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = args.log_outputs
SCREAMING_SNAKE_CASE = "_".join(args.dataset.split("/" ) + [args.config, args.split] )
# load metric
SCREAMING_SNAKE_CASE = load_metric("wer" )
SCREAMING_SNAKE_CASE = load_metric("cer" )
# compute metrics
SCREAMING_SNAKE_CASE = wer.compute(references=result["target"] , predictions=result["prediction"] )
SCREAMING_SNAKE_CASE = cer.compute(references=result["target"] , predictions=result["prediction"] )
# print & log results
SCREAMING_SNAKE_CASE = F"WER: {wer_result}\nCER: {cer_result}"
print(_UpperCamelCase )
with open(F"{dataset_id}_eval_results.txt" , "w" ) as f:
f.write(_UpperCamelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
SCREAMING_SNAKE_CASE = F"log_{dataset_id}_predictions.txt"
SCREAMING_SNAKE_CASE = F"log_{dataset_id}_targets.txt"
with open(_UpperCamelCase , "w" ) as p, open(_UpperCamelCase , "w" ) as t:
# mapping function to write output
def write_to_file(A__ : Optional[Any] , A__ : List[Any] ):
p.write(F"{i}" + "\n" )
p.write(batch["prediction"] + "\n" )
t.write(F"{i}" + "\n" )
t.write(batch["target"] + "\n" )
result.map(_UpperCamelCase , with_indices=_UpperCamelCase )
def __a ( A__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "[,?.!\-\;\:\"“%‘”�—’…–]" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
SCREAMING_SNAKE_CASE = re.sub(_UpperCamelCase , "" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
SCREAMING_SNAKE_CASE = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
SCREAMING_SNAKE_CASE = " ".join(text.split(_UpperCamelCase ) )
return text
def __a ( A__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_UpperCamelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
SCREAMING_SNAKE_CASE = AutoFeatureExtractor.from_pretrained(args.model_id )
SCREAMING_SNAKE_CASE = feature_extractor.sampling_rate
# resample audio
SCREAMING_SNAKE_CASE = dataset.cast_column("audio" , Audio(sampling_rate=_UpperCamelCase ) )
# load eval pipeline
if args.device is None:
SCREAMING_SNAKE_CASE = 0 if torch.cuda.is_available() else -1
SCREAMING_SNAKE_CASE = pipeline("automatic-speech-recognition" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(A__ : List[str] ):
SCREAMING_SNAKE_CASE = asr(
batch["audio"]["array"] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
SCREAMING_SNAKE_CASE = prediction["text"]
SCREAMING_SNAKE_CASE = normalize_text(batch["sentence"] )
return batch
# run inference on all examples
SCREAMING_SNAKE_CASE = dataset.map(_UpperCamelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
__A : Any = 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 : Union[str, Any] = parser.parse_args()
main(args) | 721 |
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __a ( A__ : str=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("test" )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=A__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
SCREAMING_SNAKE_CASE = script_name
else:
SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
SCREAMING_SNAKE_CASE = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def __a ( ):
SCREAMING_SNAKE_CASE = test_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main() | 698 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( __a , __a , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = StableDiffusionSAGPipeline
lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ = False
def _snake_case ( self : Union[str, Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 , )
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=A__ , set_alpha_to_one=A__ , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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=1000 , )
SCREAMING_SNAKE_CASE = CLIPTextModel(A__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple=0 ):
if str(A__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(A__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=A__ ).manual_seed(A__ )
SCREAMING_SNAKE_CASE = {
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "numpy",
}
return inputs
def _snake_case ( self : List[str] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE = sag_pipe.to(A__ )
sag_pipe.set_progress_bar_config(disable=A__ )
SCREAMING_SNAKE_CASE = "."
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = sag_pipe(
[prompt] , generator=A__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE = sag_pipe.to(A__ )
sag_pipe.set_progress_bar_config(disable=A__ )
SCREAMING_SNAKE_CASE = "."
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = sag_pipe(
[prompt] , generator=A__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE = sag_pipe.to(A__ )
sag_pipe.set_progress_bar_config(disable=A__ )
SCREAMING_SNAKE_CASE = "."
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = sag_pipe(
[prompt] , width=768 , height=512 , generator=A__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" , )
SCREAMING_SNAKE_CASE = output.images
assert image.shape == (1, 512, 768, 3) | 700 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[int] = logging.get_logger(__name__)
__A : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : Tuple = {
'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'
},
}
__A : Dict = {'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ):
SCREAMING_SNAKE_CASE = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE = bs[:]
SCREAMING_SNAKE_CASE = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def __a ( A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE = char
return pairs
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any]="replace" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : List[Any]="<mask>" , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE = bytes_to_unicode()
SCREAMING_SNAKE_CASE = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case ( self : Dict ):
return len(self.encoder )
def _snake_case ( self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = bigram
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = word
return word
def _snake_case ( self : str , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for token in re.findall(self.pat , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : List[str] , __lowerCamelCase : str ):
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Tuple ):
return self.decoder.get(__lowerCamelCase )
def _snake_case ( self : Tuple , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = "".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
SCREAMING_SNAKE_CASE = 0
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _snake_case ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 : Any , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE = " " + text
return (text, kwargs)
def _snake_case ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case ( self : Tuple , __lowerCamelCase : "Conversation" ):
SCREAMING_SNAKE_CASE = []
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(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
SCREAMING_SNAKE_CASE = 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 | 698 | 0 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__A : List[str] = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
__A : int = logging.WARNING
def __a ( ):
SCREAMING_SNAKE_CASE = os.getenv("DATASETS_VERBOSITY" , _SCREAMING_SNAKE_CASE )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"Unknown option DATASETS_VERBOSITY={env_level_str}, "
F"has to be one of: { ', '.join(log_levels.keys() ) }" )
return _default_log_level
def __a ( ):
return __name__.split("." )[0]
def __a ( ):
return logging.getLogger(_get_library_name() )
def __a ( ):
# Apply our default configuration to the library root logger.
SCREAMING_SNAKE_CASE = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def __a ( ):
SCREAMING_SNAKE_CASE = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def __a ( A__ : Optional[int] = None ):
if name is None:
SCREAMING_SNAKE_CASE = _get_library_name()
return logging.getLogger(_SCREAMING_SNAKE_CASE )
def __a ( ):
return _get_library_root_logger().getEffectiveLevel()
def __a ( A__ : List[Any] ):
_get_library_root_logger().setLevel(_SCREAMING_SNAKE_CASE )
def __a ( ):
return set_verbosity(_SCREAMING_SNAKE_CASE )
def __a ( ):
return set_verbosity(_SCREAMING_SNAKE_CASE )
def __a ( ):
return set_verbosity(_SCREAMING_SNAKE_CASE )
def __a ( ):
return set_verbosity(_SCREAMING_SNAKE_CASE )
def __a ( ):
SCREAMING_SNAKE_CASE = False
def __a ( ):
SCREAMING_SNAKE_CASE = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any] , *__lowerCamelCase : int , **__lowerCamelCase : Optional[Any] ): # pylint: disable=unused-argument
SCREAMING_SNAKE_CASE = args[0] if args else None
def __iter__( self : Dict ):
return iter(self._iterator )
def __getattr__( self : Union[str, Any] , __lowerCamelCase : Any ):
def empty_fn(*__lowerCamelCase : Optional[int] , **__lowerCamelCase : Dict ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : Optional[Any] ):
return self
def __exit__( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : Any ):
return
__A : Union[str, Any] = True
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __call__( self : Optional[int] , *__lowerCamelCase : List[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*__lowerCamelCase , **__lowerCamelCase )
else:
return EmptyTqdm(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Any , *__lowerCamelCase : Optional[int] , **__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Optional[int] ):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__A : Dict = _tqdm_cls()
def __a ( ):
global _tqdm_active
return bool(_tqdm_active )
def __a ( ):
global _tqdm_active
SCREAMING_SNAKE_CASE = True
def __a ( ):
global _tqdm_active
SCREAMING_SNAKE_CASE = False | 701 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE_CASE = (-b + sqrt(A__ )) / (2 * a)
SCREAMING_SNAKE_CASE = (-b - sqrt(A__ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main() | 698 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__A : int = ['gpt2']
__A : List[str] = 'gpt2'
if is_tf_available():
class _SCREAMING_SNAKE_CASE ( tf.Module ):
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : Optional[Any] ):
super().__init__()
SCREAMING_SNAKE_CASE = tokenizer
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__UpperCamelCase )
SCREAMING_SNAKE_CASE = TFGPTaLMHeadModel.from_config(__UpperCamelCase )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="text" ),) )
def _snake_case ( self : Any , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = self.tokenizer(__UpperCamelCase )
SCREAMING_SNAKE_CASE = tokenized["input_ids"].to_tensor()
SCREAMING_SNAKE_CASE = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
SCREAMING_SNAKE_CASE = self.model(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase )["logits"]
return outputs
@require_tf
@require_keras_nlp
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
super().setUp()
SCREAMING_SNAKE_CASE = [GPTaTokenizer.from_pretrained(__UpperCamelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
SCREAMING_SNAKE_CASE = [TFGPTaTokenizer.from_pretrained(__UpperCamelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
SCREAMING_SNAKE_CASE = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we\'re going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
SCREAMING_SNAKE_CASE = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _snake_case ( self : Any ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
SCREAMING_SNAKE_CASE = tokenizer([test_inputs] , return_tensors="tf" )
SCREAMING_SNAKE_CASE = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
SCREAMING_SNAKE_CASE = python_outputs[key].numpy()
SCREAMING_SNAKE_CASE = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(__UpperCamelCase , tf.intaa ) == tf_outputs_values ) )
@slow
def _snake_case ( self : int ):
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE = tf.function(__UpperCamelCase )
for test_inputs in self.test_sentences:
SCREAMING_SNAKE_CASE = tf.constant(__UpperCamelCase )
SCREAMING_SNAKE_CASE = compiled_tokenizer(__UpperCamelCase )
SCREAMING_SNAKE_CASE = tf_tokenizer(__UpperCamelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _snake_case ( self : int ):
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE = ModelToSave(tokenizer=__UpperCamelCase )
SCREAMING_SNAKE_CASE = tf.convert_to_tensor([self.test_sentences[0]] )
SCREAMING_SNAKE_CASE = model.serving(__UpperCamelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE = Path(__UpperCamelCase ) / "saved.model"
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={"serving_default": model.serving} )
SCREAMING_SNAKE_CASE = tf.saved_model.load(__UpperCamelCase )
SCREAMING_SNAKE_CASE = loaded_model.signatures["serving_default"](__UpperCamelCase )["output_0"]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def _snake_case ( self : str ):
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE = tf.convert_to_tensor([self.test_sentences[0]] )
SCREAMING_SNAKE_CASE = tf_tokenizer(__UpperCamelCase ) # Build model with some sample inputs
SCREAMING_SNAKE_CASE = tf_tokenizer.get_config()
SCREAMING_SNAKE_CASE = TFGPTaTokenizer.from_config(__UpperCamelCase )
SCREAMING_SNAKE_CASE = model_from_config(__UpperCamelCase )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def _snake_case ( self : Optional[int] ):
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
SCREAMING_SNAKE_CASE = 123123
for max_length in [3, 5, 1024]:
SCREAMING_SNAKE_CASE = tf.convert_to_tensor([self.test_sentences[0]] )
SCREAMING_SNAKE_CASE = tf_tokenizer(__UpperCamelCase , max_length=__UpperCamelCase )
SCREAMING_SNAKE_CASE = out["input_ids"].numpy().shape[1]
assert out_length == max_length | 702 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Dict ):
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 _snake_case ( self : int ):
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 _snake_case ( self : List[str] ):
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 _snake_case ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
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 _snake_case ( self : Optional[int] ):
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 | 698 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
__A : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
__A : List[str] = '\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n >>> repo = "openai/shap-e-img2img"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n >>> image = load_image(image_url).convert("RGB")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n ```\n'
@dataclass
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = 42
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : int , ):
super().__init__()
self.register_modules(
prior=lowercase__ , image_encoder=lowercase__ , image_processor=lowercase__ , scheduler=lowercase__ , renderer=lowercase__ , )
def _snake_case ( self : List[str] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int ):
if latents is None:
SCREAMING_SNAKE_CASE = randn_tensor(lowercase__ , generator=lowercase__ , device=lowercase__ , dtype=lowercase__ )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
SCREAMING_SNAKE_CASE = latents.to(lowercase__ )
SCREAMING_SNAKE_CASE = latents * scheduler.init_noise_sigma
return latents
def _snake_case ( self : Optional[Any] , __lowerCamelCase : str=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
SCREAMING_SNAKE_CASE = torch.device(f"cuda:{gpu_id}" )
SCREAMING_SNAKE_CASE = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase__ , lowercase__ )
@property
def _snake_case ( self : str ):
if self.device != torch.device("meta" ) or not hasattr(self.image_encoder , "_hf_hook" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowercase__ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def _snake_case ( self : List[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any] , ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(image[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE = torch.cat(lowercase__ , axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase__ , axis=0 )
if not isinstance(lowercase__ , torch.Tensor ):
SCREAMING_SNAKE_CASE = self.image_processor(lowercase__ , return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
SCREAMING_SNAKE_CASE = image.to(dtype=self.image_encoder.dtype , device=lowercase__ )
SCREAMING_SNAKE_CASE = self.image_encoder(lowercase__ )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
SCREAMING_SNAKE_CASE = image_embeds.repeat_interleave(lowercase__ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE = torch.zeros_like(lowercase__ )
# 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
SCREAMING_SNAKE_CASE = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowercase__ )
def __call__( self : Union[str, Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple = 1 , __lowerCamelCase : Tuple = 25 , __lowerCamelCase : Union[str, Any] = None , __lowerCamelCase : Tuple = None , __lowerCamelCase : int = 4.0 , __lowerCamelCase : str = 64 , __lowerCamelCase : Any = "pil" , __lowerCamelCase : Optional[int] = True , ):
if isinstance(lowercase__ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE = 1
elif isinstance(lowercase__ , torch.Tensor ):
SCREAMING_SNAKE_CASE = image.shape[0]
elif isinstance(lowercase__ , lowercase__ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
SCREAMING_SNAKE_CASE = len(lowercase__ )
else:
raise ValueError(
f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase__ )}" )
SCREAMING_SNAKE_CASE = self._execution_device
SCREAMING_SNAKE_CASE = batch_size * num_images_per_prompt
SCREAMING_SNAKE_CASE = guidance_scale > 1.0
SCREAMING_SNAKE_CASE = self._encode_image(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# prior
self.scheduler.set_timesteps(lowercase__ , device=lowercase__ )
SCREAMING_SNAKE_CASE = self.scheduler.timesteps
SCREAMING_SNAKE_CASE = self.prior.config.num_embeddings
SCREAMING_SNAKE_CASE = self.prior.config.embedding_dim
SCREAMING_SNAKE_CASE = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowercase__ , lowercase__ , lowercase__ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
SCREAMING_SNAKE_CASE = latents.reshape(latents.shape[0] , lowercase__ , lowercase__ )
for i, t in enumerate(self.progress_bar(lowercase__ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE = self.scheduler.scale_model_input(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE = self.prior(
lowercase__ , timestep=lowercase__ , proj_embedding=lowercase__ , ).predicted_image_embedding
# remove the variance
SCREAMING_SNAKE_CASE = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
SCREAMING_SNAKE_CASE = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
SCREAMING_SNAKE_CASE = self.scheduler.step(
lowercase__ , timestep=lowercase__ , sample=lowercase__ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowercase__ )
SCREAMING_SNAKE_CASE = []
for i, latent in enumerate(lowercase__ ):
print()
SCREAMING_SNAKE_CASE = self.renderer.decode(
latent[None, :] , lowercase__ , size=lowercase__ , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(lowercase__ )
SCREAMING_SNAKE_CASE = torch.stack(lowercase__ )
if output_type not in ["np", "pil"]:
raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}" )
SCREAMING_SNAKE_CASE = images.cpu().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE = [self.numpy_to_pil(lowercase__ ) for image in images]
# Offload last model to CPU
if hasattr(self , "final_offload_hook" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowercase__ ) | 704 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 698 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : List[Any] = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = "camembert"
def __init__( self : int , __lowerCamelCase : Union[str, Any]=30522 , __lowerCamelCase : Optional[int]=768 , __lowerCamelCase : List[Any]=12 , __lowerCamelCase : Tuple=12 , __lowerCamelCase : Union[str, Any]=3072 , __lowerCamelCase : Any="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : int=512 , __lowerCamelCase : Any=2 , __lowerCamelCase : List[str]=0.02 , __lowerCamelCase : List[str]=1e-12 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : int=0 , __lowerCamelCase : Union[str, Any]=2 , __lowerCamelCase : Union[str, Any]="absolute" , __lowerCamelCase : Any=True , __lowerCamelCase : Optional[int]=None , **__lowerCamelCase : Any , ):
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = classifier_dropout
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _snake_case ( self : Dict ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 705 |
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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "resnet"
lowerCamelCase__ = ["basic", "bottleneck"]
def __init__( self : Optional[Any] , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=64 , __lowerCamelCase : str=[256, 512, 1024, 2048] , __lowerCamelCase : str=[3, 4, 6, 3] , __lowerCamelCase : Optional[int]="bottleneck" , __lowerCamelCase : int="relu" , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = downsample_in_first_stage
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = version.parse("1.11" )
@property
def _snake_case ( self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : Optional[int] ):
return 1e-3 | 698 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __a ( A__ : List[str] ):
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def __a ( A__ : str ):
for char in word:
SCREAMING_SNAKE_CASE = ord(A__ )
if not _is_chinese_char(A__ ):
return 0
return 1
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = set()
for token in tokens:
SCREAMING_SNAKE_CASE = len(A__ ) > 1 and is_chinese(A__ )
if chinese_word:
word_set.add(A__ )
SCREAMING_SNAKE_CASE = list(A__ )
return word_list
def __a ( A__ : List[str] , A__ : set() ):
if not chinese_word_set:
return bert_tokens
SCREAMING_SNAKE_CASE = max([len(A__ ) for w in chinese_word_set] )
SCREAMING_SNAKE_CASE = bert_tokens
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0, len(A__ )
while start < end:
SCREAMING_SNAKE_CASE = True
if is_chinese(bert_word[start] ):
SCREAMING_SNAKE_CASE = min(end - start , A__ )
for i in range(A__ , 1 , -1 ):
SCREAMING_SNAKE_CASE = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
SCREAMING_SNAKE_CASE = "##" + bert_word[j]
SCREAMING_SNAKE_CASE = start + i
SCREAMING_SNAKE_CASE = False
break
if single_word:
start += 1
return bert_word
def __a ( A__ : List[str] , A__ : LTP , A__ : BertTokenizer ):
SCREAMING_SNAKE_CASE = []
for i in range(0 , len(A__ ) , 100 ):
SCREAMING_SNAKE_CASE = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["cws"] ).cws
SCREAMING_SNAKE_CASE = [get_chinese_word(A__ ) for r in res]
ltp_res.extend(A__ )
assert len(A__ ) == len(A__ )
SCREAMING_SNAKE_CASE = []
for i in range(0 , len(A__ ) , 100 ):
SCREAMING_SNAKE_CASE = bert_tokenizer(lines[i : i + 100] , add_special_tokens=A__ , truncation=A__ , max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(A__ ) == len(A__ )
SCREAMING_SNAKE_CASE = []
for input_ids, chinese_word in zip(A__ , A__ ):
SCREAMING_SNAKE_CASE = []
for id in input_ids:
SCREAMING_SNAKE_CASE = bert_tokenizer._convert_id_to_token(A__ )
input_tokens.append(A__ )
SCREAMING_SNAKE_CASE = add_sub_symbol(A__ , A__ )
SCREAMING_SNAKE_CASE = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(A__ ):
if token[:2] == "##":
SCREAMING_SNAKE_CASE = token[2:]
# save chinese tokens' pos
if len(A__ ) == 1 and _is_chinese_char(ord(A__ ) ):
ref_id.append(A__ )
ref_ids.append(A__ )
assert len(A__ ) == len(A__ )
return ref_ids
def __a ( A__ : List[str] ):
with open(args.file_name , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = f.readlines()
SCREAMING_SNAKE_CASE = [line.strip() for line in data if len(A__ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
SCREAMING_SNAKE_CASE = LTP(args.ltp ) # faster in GPU device
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained(args.bert )
SCREAMING_SNAKE_CASE = prepare_ref(A__ , A__ , A__ )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = [json.dumps(A__ ) + "\n" for ref in ref_ids]
f.writelines(A__ )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
required=False,
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp',
required=False,
type=str,
default='./resources/ltp',
help='resources for LTP tokenizer, usually a path',
)
parser.add_argument(
'--bert',
required=False,
type=str,
default='./resources/robert',
help='resources for Bert tokenizer',
)
parser.add_argument(
'--save_path',
required=False,
type=str,
default='./resources/ref.txt',
help='path to save res',
)
__A : Optional[int] = parser.parse_args()
main(args) | 706 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "imagegpt"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , __lowerCamelCase : Any=512 + 1 , __lowerCamelCase : str=32 * 32 , __lowerCamelCase : Any=512 , __lowerCamelCase : Optional[int]=24 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]="quick_gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[Any]=1e-5 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : int=False , __lowerCamelCase : int=False , **__lowerCamelCase : Union[str, Any] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scale_attn_weights
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE = tie_word_embeddings
super().__init__(tie_word_embeddings=__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def _snake_case ( self : Optional[int] , __lowerCamelCase : "FeatureExtractionMixin" , __lowerCamelCase : int = 1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , ):
SCREAMING_SNAKE_CASE = self._generate_dummy_images(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dict(preprocessor(images=__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return inputs | 698 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[Any] = logging.get_logger(__name__)
def __a ( A__ : Dict ):
SCREAMING_SNAKE_CASE = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
SCREAMING_SNAKE_CASE = 192
SCREAMING_SNAKE_CASE = 768
SCREAMING_SNAKE_CASE = 12
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = [800, 1333]
SCREAMING_SNAKE_CASE = False
elif yolos_name == "yolos_s_dWr":
SCREAMING_SNAKE_CASE = 330
SCREAMING_SNAKE_CASE = 14
SCREAMING_SNAKE_CASE = 6
SCREAMING_SNAKE_CASE = 1320
elif "yolos_s" in yolos_name:
SCREAMING_SNAKE_CASE = 384
SCREAMING_SNAKE_CASE = 1536
SCREAMING_SNAKE_CASE = 12
SCREAMING_SNAKE_CASE = 6
elif "yolos_b" in yolos_name:
SCREAMING_SNAKE_CASE = [800, 1344]
SCREAMING_SNAKE_CASE = 91
SCREAMING_SNAKE_CASE = "huggingface/label-files"
SCREAMING_SNAKE_CASE = "coco-detection-id2label.json"
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE = {int(lowercase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __a ( A__ : int , A__ : Union[str, Any] , A__ : List[Any] = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
SCREAMING_SNAKE_CASE = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE = in_proj_weight[: config.hidden_size, :]
SCREAMING_SNAKE_CASE = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE = in_proj_weight[-config.hidden_size :, :]
SCREAMING_SNAKE_CASE = in_proj_bias[-config.hidden_size :]
def __a ( A__ : Optional[Any] ):
if "backbone" in name:
SCREAMING_SNAKE_CASE = name.replace("backbone" , "vit" )
if "cls_token" in name:
SCREAMING_SNAKE_CASE = name.replace("cls_token" , "embeddings.cls_token" )
if "det_token" in name:
SCREAMING_SNAKE_CASE = name.replace("det_token" , "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
SCREAMING_SNAKE_CASE = name.replace("mid_pos_embed" , "encoder.mid_position_embeddings" )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "blocks" in name:
SCREAMING_SNAKE_CASE = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("attn" , "attention.self" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("mlp.fc2" , "output.dense" )
if "class_embed" in name:
SCREAMING_SNAKE_CASE = name.replace("class_embed" , "class_labels_classifier" )
if "bbox_embed" in name:
SCREAMING_SNAKE_CASE = name.replace("bbox_embed" , "bbox_predictor" )
if "vit.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("vit.norm" , "vit.layernorm" )
return name
def __a ( A__ : Optional[Any] , A__ : Union[str, Any] ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split("." )
SCREAMING_SNAKE_CASE = int(key_split[2] )
SCREAMING_SNAKE_CASE = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __a ( ):
SCREAMING_SNAKE_CASE = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def __a ( A__ : Optional[int] , A__ : Union[str, Any] , A__ : Dict , A__ : Dict = False ):
SCREAMING_SNAKE_CASE = get_yolos_config(lowercase_ )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(lowercase_ , map_location="cpu" )["model"]
# load 🤗 model
SCREAMING_SNAKE_CASE = YolosForObjectDetection(lowercase_ )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
# Check outputs on an image, prepared by YolosImageProcessor
SCREAMING_SNAKE_CASE = 800 if yolos_name != "yolos_ti" else 512
SCREAMING_SNAKE_CASE = YolosImageProcessor(format="coco_detection" , size=lowercase_ )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="pt" )
SCREAMING_SNAKE_CASE = model(**lowercase_ )
SCREAMING_SNAKE_CASE = outputs.logits, outputs.pred_boxes
SCREAMING_SNAKE_CASE = None, None
if yolos_name == "yolos_ti":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
elif yolos_name == "yolos_s_200_pre":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
elif yolos_name == "yolos_s_300_pre":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
elif yolos_name == "yolos_s_dWr":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
elif yolos_name == "yolos_base":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
else:
raise ValueError(F"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , lowercase_ , atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , lowercase_ , atol=1E-4 )
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(lowercase_ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowercase_ )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
SCREAMING_SNAKE_CASE = model_mapping[yolos_name]
image_processor.push_to_hub(lowercase_ , organization="hustvl" )
model.push_to_hub(lowercase_ , organization="hustvl" )
if __name__ == "__main__":
__A : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--yolos_name',
default='yolos_s_200_pre',
type=str,
help=(
'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
),
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
)
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.'
)
__A : Union[str, Any] = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub) | 707 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@slow
@require_torch
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE = 128
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE = 4
def _map_to_encoder_decoder_inputs(__lowerCamelCase : str ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE = tokenizer(batch["article"] , padding="max_length" , truncation=__lowerCamelCase , max_length=512 )
SCREAMING_SNAKE_CASE = tokenizer(batch["highlights"] , padding="max_length" , truncation=__lowerCamelCase , max_length=128 )
SCREAMING_SNAKE_CASE = inputs.input_ids
SCREAMING_SNAKE_CASE = inputs.attention_mask
SCREAMING_SNAKE_CASE = outputs.input_ids
SCREAMING_SNAKE_CASE = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
SCREAMING_SNAKE_CASE = outputs.attention_mask
assert all(len(__lowerCamelCase ) == 512 for x in inputs.input_ids )
assert all(len(__lowerCamelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(__lowerCamelCase : Union[str, Any] ):
SCREAMING_SNAKE_CASE = pred.label_ids
SCREAMING_SNAKE_CASE = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
SCREAMING_SNAKE_CASE = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__lowerCamelCase ) )] ) / len(__lowerCamelCase )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__lowerCamelCase , batch_size=__lowerCamelCase , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE = SeqaSeqTrainingArguments(
output_dir=__lowerCamelCase , per_device_train_batch_size=__lowerCamelCase , per_device_eval_batch_size=__lowerCamelCase , predict_with_generate=__lowerCamelCase , evaluation_strategy="steps" , do_train=__lowerCamelCase , do_eval=__lowerCamelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=__lowerCamelCase , args=__lowerCamelCase , compute_metrics=_compute_metrics , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , tokenizer=__lowerCamelCase , )
# start training
trainer.train() | 698 | 0 |
import baseaa
def __a ( A__ : List[str] ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __a ( A__ : Tuple ):
return baseaa.baadecode(__UpperCamelCase ).decode("utf-8" )
if __name__ == "__main__":
__A : Any = 'Hello World!'
__A : Optional[Any] = baseaa_encode(test)
print(encoded)
__A : Optional[Any] = baseaa_decode(encoded)
print(decoded) | 708 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "num_attention_heads" ) )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Dict=3 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : int=2 , __lowerCamelCase : List[Any]=1 , __lowerCamelCase : Optional[Any]=16 , __lowerCamelCase : Tuple=[128, 256, 384] , __lowerCamelCase : int=[4, 6, 8] , __lowerCamelCase : Tuple=[2, 3, 4] , __lowerCamelCase : List[str]=[16, 16, 16] , __lowerCamelCase : int=0 , __lowerCamelCase : List[Any]=[2, 2, 2] , __lowerCamelCase : List[str]=[2, 2, 2] , __lowerCamelCase : List[str]=0.02 , __lowerCamelCase : Any=True , __lowerCamelCase : Dict=True , __lowerCamelCase : int=2 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = kernel_size
SCREAMING_SNAKE_CASE = stride
SCREAMING_SNAKE_CASE = padding
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : List[str] ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _snake_case ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : str , __lowerCamelCase : Any ):
SCREAMING_SNAKE_CASE = LevitModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
SCREAMING_SNAKE_CASE = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _snake_case ( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = LevitForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = LevitModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Any ):
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 : Any ):
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def _snake_case ( self : Tuple ):
pass
@unittest.skip(reason="Levit does not output attentions" )
def _snake_case ( self : Tuple ):
pass
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : List[Any] ):
def check_hidden_states_output(__lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = len(self.model_tester.depths ) + 1
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = (self.model_tester.image_size, self.model_tester.image_size)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
SCREAMING_SNAKE_CASE = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : int ):
pass
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : int=False ):
SCREAMING_SNAKE_CASE = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _snake_case ( self : List[Any] ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowerCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__lowerCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}" ):
SCREAMING_SNAKE_CASE = problem_type["title"]
SCREAMING_SNAKE_CASE = problem_type["num_labels"]
SCREAMING_SNAKE_CASE = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.train()
SCREAMING_SNAKE_CASE = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
SCREAMING_SNAKE_CASE = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def _snake_case ( self : List[Any] ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = LevitModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Dict ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**__lowerCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([1.0_448, -0.3_745, -1.8_317] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1e-4 ) ) | 698 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
__A : List[Any] = True
from torch.cuda.amp import autocast
__A : Tuple = logging.getLogger(__name__)
def __a ( A__ : Tuple=None , A__ : Tuple=None ):
return field(default_factory=lambda: default , metadata=A__ )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
lowerCamelCase__ = field(
default=0.1 , metadata={"help": "The dropout ratio for the attention probabilities."} )
lowerCamelCase__ = field(
default=0.1 , metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
lowerCamelCase__ = field(
default=0.1 , metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
} , )
lowerCamelCase__ = field(
default=0.1 , metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."} , )
lowerCamelCase__ = field(
default=0.05 , metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
} , )
lowerCamelCase__ = field(default=0.0 , metadata={"help": "The LayerDrop probability."} )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowerCamelCase__ = field(
default="train+validation" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
} , )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowerCamelCase__ = field(
default=_lowerCAmelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
} , )
lowerCamelCase__ = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "\'", "\"", "�"] , metadata={"help": "A list of characters to remove from the transcripts."} , )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = 42
lowerCamelCase__ = True
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
def __call__( self : Tuple , __lowerCamelCase : List[Dict[str, Union[List[int], torch.Tensor]]] ):
SCREAMING_SNAKE_CASE = [{"input_values": feature["input_values"]} for feature in features]
SCREAMING_SNAKE_CASE = [{"input_ids": feature["labels"]} for feature in features]
SCREAMING_SNAKE_CASE = self.processor.pad(
_lowerCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
SCREAMING_SNAKE_CASE = self.processor.pad(
labels=_lowerCAmelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors="pt" , )
# replace padding with -100 to ignore loss correctly
SCREAMING_SNAKE_CASE = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
SCREAMING_SNAKE_CASE = labels
return batch
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
'''simple docstring'''
def _snake_case ( self : List[Any] , __lowerCamelCase : nn.Module , __lowerCamelCase : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
SCREAMING_SNAKE_CASE = self._prepare_inputs(_lowerCAmelCase )
if self.use_amp:
with autocast():
SCREAMING_SNAKE_CASE = self.compute_loss(_lowerCAmelCase , _lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE = self.compute_loss(_lowerCAmelCase , _lowerCAmelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
SCREAMING_SNAKE_CASE = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
SCREAMING_SNAKE_CASE = loss.sum() / (inputs["labels"] >= 0).sum()
else:
raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']" )
if self.args.gradient_accumulation_steps > 1:
SCREAMING_SNAKE_CASE = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_lowerCAmelCase ).backward()
elif self.use_apex:
with amp.scale_loss(_lowerCAmelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_lowerCAmelCase )
else:
loss.backward()
return loss.detach()
def __a ( ):
SCREAMING_SNAKE_CASE = 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.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE = 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:
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." )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , A__ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
SCREAMING_SNAKE_CASE = datasets.load_dataset(
"common_voice" , data_args.dataset_config_name , split=data_args.train_split_name )
SCREAMING_SNAKE_CASE = datasets.load_dataset("common_voice" , data_args.dataset_config_name , split="test" )
# Create and save tokenizer
SCREAMING_SNAKE_CASE = F"[{''.join(data_args.chars_to_ignore )}]"
def remove_special_characters(A__ : Dict ):
SCREAMING_SNAKE_CASE = re.sub(A__ , "" , batch["sentence"] ).lower() + " "
return batch
SCREAMING_SNAKE_CASE = train_dataset.map(A__ , remove_columns=["sentence"] )
SCREAMING_SNAKE_CASE = eval_dataset.map(A__ , remove_columns=["sentence"] )
def extract_all_chars(A__ : int ):
SCREAMING_SNAKE_CASE = " ".join(batch["text"] )
SCREAMING_SNAKE_CASE = list(set(A__ ) )
return {"vocab": [vocab], "all_text": [all_text]}
SCREAMING_SNAKE_CASE = train_dataset.map(
A__ , batched=A__ , batch_size=-1 , keep_in_memory=A__ , remove_columns=train_dataset.column_names , )
SCREAMING_SNAKE_CASE = train_dataset.map(
A__ , batched=A__ , batch_size=-1 , keep_in_memory=A__ , remove_columns=eval_dataset.column_names , )
SCREAMING_SNAKE_CASE = list(set(vocab_train["vocab"][0] ) | set(vocab_test["vocab"][0] ) )
SCREAMING_SNAKE_CASE = {v: k for k, v in enumerate(A__ )}
SCREAMING_SNAKE_CASE = vocab_dict[" "]
del vocab_dict[" "]
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = len(A__ )
with open("vocab.json" , "w" ) as vocab_file:
json.dump(A__ , A__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer(
"vocab.json" , unk_token="[UNK]" , pad_token="[PAD]" , word_delimiter_token="|" , )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=A__ , return_attention_mask=A__ )
SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=A__ , tokenizer=A__ )
SCREAMING_SNAKE_CASE = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction="mean" , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE = min(len(A__ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE = train_dataset.select(range(A__ ) )
if data_args.max_val_samples is not None:
SCREAMING_SNAKE_CASE = eval_dataset.select(range(data_args.max_val_samples ) )
SCREAMING_SNAKE_CASE = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(A__ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torchaudio.load(batch["path"] )
SCREAMING_SNAKE_CASE = resampler(A__ ).squeeze().numpy()
SCREAMING_SNAKE_CASE = 16000
SCREAMING_SNAKE_CASE = batch["text"]
return batch
SCREAMING_SNAKE_CASE = train_dataset.map(
A__ , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
SCREAMING_SNAKE_CASE = eval_dataset.map(
A__ , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(A__ : Union[str, Any] ):
# check that all files have the correct sampling rate
assert (
len(set(batch["sampling_rate"] ) ) == 1
), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
SCREAMING_SNAKE_CASE = processor(
audio=batch["speech"] , text=batch["target_text"] , sampling_rate=batch["sampling_rate"][0] )
batch.update(A__ )
return batch
SCREAMING_SNAKE_CASE = train_dataset.map(
A__ , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=A__ , num_proc=data_args.preprocessing_num_workers , )
SCREAMING_SNAKE_CASE = eval_dataset.map(
A__ , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=A__ , num_proc=data_args.preprocessing_num_workers , )
# Metric
SCREAMING_SNAKE_CASE = datasets.load_metric("wer" )
def compute_metrics(A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = pred.predictions
SCREAMING_SNAKE_CASE = np.argmax(A__ , axis=-1 )
SCREAMING_SNAKE_CASE = processor.tokenizer.pad_token_id
SCREAMING_SNAKE_CASE = processor.batch_decode(A__ )
# we do not want to group tokens when computing the metrics
SCREAMING_SNAKE_CASE = processor.batch_decode(pred.label_ids , group_tokens=A__ )
SCREAMING_SNAKE_CASE = wer_metric.compute(predictions=A__ , references=A__ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
SCREAMING_SNAKE_CASE = DataCollatorCTCWithPadding(processor=A__ , padding=A__ )
# Initialize our Trainer
SCREAMING_SNAKE_CASE = CTCTrainer(
model=A__ , data_collator=A__ , args=A__ , compute_metrics=A__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
SCREAMING_SNAKE_CASE = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
SCREAMING_SNAKE_CASE = model_args.model_name_or_path
else:
SCREAMING_SNAKE_CASE = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
SCREAMING_SNAKE_CASE = trainer.train(resume_from_checkpoint=A__ )
trainer.save_model()
SCREAMING_SNAKE_CASE = train_result.metrics
SCREAMING_SNAKE_CASE = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(A__ )
)
SCREAMING_SNAKE_CASE = min(A__ , len(A__ ) )
trainer.log_metrics("train" , A__ )
trainer.save_metrics("train" , A__ )
trainer.save_state()
# Evaluation
SCREAMING_SNAKE_CASE = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
SCREAMING_SNAKE_CASE = trainer.evaluate()
SCREAMING_SNAKE_CASE = data_args.max_val_samples if data_args.max_val_samples is not None else len(A__ )
SCREAMING_SNAKE_CASE = min(A__ , len(A__ ) )
trainer.log_metrics("eval" , A__ )
trainer.save_metrics("eval" , A__ )
return results
if __name__ == "__main__":
main() | 709 |
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 _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Tuple , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
return None
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : int ):
return None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
@require_torch
@slow
def _snake_case ( self : Optional[int] ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(__lowerCamelCase ) )
vocab_file.flush()
SCREAMING_SNAKE_CASE = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
SCREAMING_SNAKE_CASE = BertModel(BertConfig(vocab_size=len(__lowerCamelCase ) ) )
model.save_pretrained(__lowerCamelCase )
self._test_export(__lowerCamelCase , "pt" , 12 , __lowerCamelCase )
@require_tf
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "tf" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(Path(__lowerCamelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def _snake_case ( self : Any ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE = self._test_export(__lowerCamelCase , "pt" , 12 , **__lowerCamelCase )
SCREAMING_SNAKE_CASE = quantize(__lowerCamelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__lowerCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def _snake_case ( self : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , **__lowerCamelCase : List[str] ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE = Path(__lowerCamelCase ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
return path
except Exception as e:
self.fail(__lowerCamelCase )
@require_torch
@require_tokenizers
@slow
def _snake_case ( self : Dict ):
from transformers import BertModel
SCREAMING_SNAKE_CASE = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "pt" )
@require_tf
@require_tokenizers
@slow
def _snake_case ( self : int ):
from transformers import TFBertModel
SCREAMING_SNAKE_CASE = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
SCREAMING_SNAKE_CASE = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__lowerCamelCase , __lowerCamelCase , "tf" )
def _snake_case ( self : int , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = FeatureExtractionPipeline(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = infer_shapes(__lowerCamelCase , __lowerCamelCase )
# Assert all variables are present
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __lowerCamelCase )
self.assertSequenceEqual(variable_names[3:] , __lowerCamelCase )
# 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 _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask", "token_type_ids"]
SCREAMING_SNAKE_CASE = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__lowerCamelCase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__lowerCamelCase ) , set(__lowerCamelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__lowerCamelCase , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ensure_valid_input(FuncNonContiguousArgs() , __lowerCamelCase , __lowerCamelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__lowerCamelCase ) , 1 )
self.assertEqual(len(__lowerCamelCase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() ) | 698 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
__A : Optional[int] = logging.get_logger(__name__)
set_seed(7_7_0)
__A : Any = {
'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',
}
__A : 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',
},
}
__A : Dict = os.path.dirname(os.path.abspath(__file__))
__A : Dict = os.path.join(os.path.expanduser('~'), '.cache')
__A : List[str] = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def __a ( A__ : List[str] , A__ : Any=False ):
SCREAMING_SNAKE_CASE = model_type
if use_small:
key += "_small"
return os.path.join(snake_case_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def __a ( A__ : Any , A__ : str ):
os.makedirs(snake_case_ , exist_ok=snake_case_ )
hf_hub_download(repo_id=snake_case_ , filename=snake_case_ , local_dir=snake_case_ )
def __a ( A__ : Union[str, Any] , A__ : List[str] , A__ : List[str]=False , A__ : Any="text" ):
if model_type == "text":
SCREAMING_SNAKE_CASE = BarkSemanticModel
SCREAMING_SNAKE_CASE = BarkSemanticConfig
SCREAMING_SNAKE_CASE = BarkSemanticGenerationConfig
elif model_type == "coarse":
SCREAMING_SNAKE_CASE = BarkCoarseModel
SCREAMING_SNAKE_CASE = BarkCoarseConfig
SCREAMING_SNAKE_CASE = BarkCoarseGenerationConfig
elif model_type == "fine":
SCREAMING_SNAKE_CASE = BarkFineModel
SCREAMING_SNAKE_CASE = BarkFineConfig
SCREAMING_SNAKE_CASE = BarkFineGenerationConfig
else:
raise NotImplementedError()
SCREAMING_SNAKE_CASE = F"{model_type}_small" if use_small else model_type
SCREAMING_SNAKE_CASE = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(snake_case_ ):
logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`." )
_download(model_info["repo_id"] , model_info["file_name"] )
SCREAMING_SNAKE_CASE = torch.load(snake_case_ , map_location=snake_case_ )
# this is a hack
SCREAMING_SNAKE_CASE = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
SCREAMING_SNAKE_CASE = model_args["""vocab_size"""]
SCREAMING_SNAKE_CASE = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
SCREAMING_SNAKE_CASE = model_args.pop("n_head" )
SCREAMING_SNAKE_CASE = model_args.pop("n_embd" )
SCREAMING_SNAKE_CASE = model_args.pop("n_layer" )
SCREAMING_SNAKE_CASE = ConfigClass(**checkpoint["model_args"] )
SCREAMING_SNAKE_CASE = ModelClass(config=snake_case_ )
SCREAMING_SNAKE_CASE = GenerationConfigClass()
SCREAMING_SNAKE_CASE = model_generation_config
SCREAMING_SNAKE_CASE = checkpoint["""model"""]
# fixup checkpoint
SCREAMING_SNAKE_CASE = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(snake_case_ ):
# replace part of the key with corresponding layer name in HF implementation
SCREAMING_SNAKE_CASE = k[len(snake_case_ ) :]
for old_layer_name in new_layer_name_dict:
SCREAMING_SNAKE_CASE = new_k.replace(snake_case_ , new_layer_name_dict[old_layer_name] )
SCREAMING_SNAKE_CASE = state_dict.pop(snake_case_ )
SCREAMING_SNAKE_CASE = set(state_dict.keys() ) - set(model.state_dict().keys() )
SCREAMING_SNAKE_CASE = {k for k in extra_keys if not k.endswith(".attn.bias" )}
SCREAMING_SNAKE_CASE = set(model.state_dict().keys() ) - set(state_dict.keys() )
SCREAMING_SNAKE_CASE = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(snake_case_ ) != 0:
raise ValueError(F"extra keys found: {extra_keys}" )
if len(snake_case_ ) != 0:
raise ValueError(F"missing keys: {missing_keys}" )
model.load_state_dict(snake_case_ , strict=snake_case_ )
SCREAMING_SNAKE_CASE = model.num_parameters(exclude_embeddings=snake_case_ )
SCREAMING_SNAKE_CASE = checkpoint["""best_val_loss"""].item()
logger.info(F"model loaded: {round(n_params/1E6 , 1 )}M params, {round(snake_case_ , 3 )} loss" )
model.eval()
model.to(snake_case_ )
del checkpoint, state_dict
return model
def __a ( A__ : List[Any] , A__ : Dict=False , A__ : Any="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
SCREAMING_SNAKE_CASE = """cpu""" # do conversion on cpu
SCREAMING_SNAKE_CASE = _get_ckpt_path(snake_case_ , use_small=snake_case_ )
SCREAMING_SNAKE_CASE = _load_model(snake_case_ , snake_case_ , model_type=snake_case_ , use_small=snake_case_ )
# load bark initial model
SCREAMING_SNAKE_CASE = _bark_load_model(snake_case_ , "cpu" , model_type=snake_case_ , use_small=snake_case_ )
if model_type == "text":
SCREAMING_SNAKE_CASE = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=snake_case_ ) != 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
SCREAMING_SNAKE_CASE = 5
SCREAMING_SNAKE_CASE = 10
if model_type in ["text", "coarse"]:
SCREAMING_SNAKE_CASE = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
SCREAMING_SNAKE_CASE = bark_model(snake_case_ )[0]
SCREAMING_SNAKE_CASE = model(snake_case_ )
# take last logits
SCREAMING_SNAKE_CASE = output_new_model_total.logits[:, [-1], :]
else:
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = 8
SCREAMING_SNAKE_CASE = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
SCREAMING_SNAKE_CASE = model(snake_case_ , snake_case_ )
SCREAMING_SNAKE_CASE = bark_model(snake_case_ , snake_case_ )
SCREAMING_SNAKE_CASE = 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(snake_case_ ).mkdir(exist_ok=snake_case_ )
model.save_pretrained(snake_case_ )
def __a ( A__ : List[Any] , A__ : Optional[int] , A__ : Tuple , A__ : Any , A__ : Any , A__ : Tuple , ):
SCREAMING_SNAKE_CASE = os.path.join(snake_case_ , snake_case_ )
SCREAMING_SNAKE_CASE = BarkSemanticConfig.from_pretrained(os.path.join(snake_case_ , "config.json" ) )
SCREAMING_SNAKE_CASE = BarkCoarseConfig.from_pretrained(os.path.join(snake_case_ , "config.json" ) )
SCREAMING_SNAKE_CASE = BarkFineConfig.from_pretrained(os.path.join(snake_case_ , "config.json" ) )
SCREAMING_SNAKE_CASE = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
SCREAMING_SNAKE_CASE = BarkSemanticModel.from_pretrained(snake_case_ )
SCREAMING_SNAKE_CASE = BarkCoarseModel.from_pretrained(snake_case_ )
SCREAMING_SNAKE_CASE = BarkFineModel.from_pretrained(snake_case_ )
SCREAMING_SNAKE_CASE = EncodecModel.from_pretrained("facebook/encodec_24khz" )
SCREAMING_SNAKE_CASE = BarkConfig.from_sub_model_configs(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
SCREAMING_SNAKE_CASE = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
SCREAMING_SNAKE_CASE = BarkModel(snake_case_ )
SCREAMING_SNAKE_CASE = semantic
SCREAMING_SNAKE_CASE = coarseAcoustic
SCREAMING_SNAKE_CASE = fineAcoustic
SCREAMING_SNAKE_CASE = codec
SCREAMING_SNAKE_CASE = bark_generation_config
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
bark.save_pretrained(snake_case_ , repo_id=snake_case_ , push_to_hub=snake_case_ )
if __name__ == "__main__":
__A : Dict = 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.')
__A : Optional[int] = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 710 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
rect.set_stroke(__lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__lowerCamelCase , buff=0.0 )
self.add(__lowerCamelCase )
cpu_targs.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Loaded Checkpoint" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , aligned_edge=__lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCamelCase ) , Write(__lowerCamelCase ) )
self.play(Write(__lowerCamelCase , run_time=1 ) , Create(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(__lowerCamelCase , opacity=0.7 )
target.move_to(__lowerCamelCase )
first_animations.append(GrowFromCenter(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__lowerCamelCase , run_time=1.5 ) )
self.play(*__lowerCamelCase )
self.play(*__lowerCamelCase )
self.wait() | 698 | 0 |
import functools
def __a ( A__ : str , A__ : str ):
SCREAMING_SNAKE_CASE = len(__UpperCamelCase )
SCREAMING_SNAKE_CASE = len(__UpperCamelCase )
@functools.cache
def min_distance(A__ : int , A__ : int ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
SCREAMING_SNAKE_CASE = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , __UpperCamelCase ) , 1 + min_distance(__UpperCamelCase , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 711 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Any , **__lowerCamelCase : Any ):
pass
@is_pipeline_test
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCamelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@require_tf
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
[
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
{"score": 0.333, "label": ANY(__lowerCamelCase )},
],
] , )
@slow
@require_torch
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
SCREAMING_SNAKE_CASE = image_classifier(__lowerCamelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
SCREAMING_SNAKE_CASE = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , ) | 698 | 0 |
__A : List[str] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A : Optional[int] = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A : Dict = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 712 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.6_0217_6634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def __a ( A__ : str , A__ : str , A__ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE = (
F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
F"Valid values are: {', '.join(A__ )}"
)
raise ValueError(A__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__A : Union[str, Any] = "scheduler_config.json"
class _SCREAMING_SNAKE_CASE ( _A ):
'''simple docstring'''
lowerCamelCase__ = 1
lowerCamelCase__ = 2
lowerCamelCase__ = 3
lowerCamelCase__ = 4
lowerCamelCase__ = 5
lowerCamelCase__ = 6
lowerCamelCase__ = 7
lowerCamelCase__ = 8
lowerCamelCase__ = 9
lowerCamelCase__ = 1_0
lowerCamelCase__ = 1_1
lowerCamelCase__ = 1_2
lowerCamelCase__ = 1_3
lowerCamelCase__ = 1_4
@dataclass
class _SCREAMING_SNAKE_CASE ( _A ):
'''simple docstring'''
lowerCamelCase__ = 4_2
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = SCHEDULER_CONFIG_NAME
lowerCamelCase__ = []
lowerCamelCase__ = True
@classmethod
def _snake_case ( cls : Tuple , __lowerCamelCase : Any = None , __lowerCamelCase : Union[str, Any] = None , __lowerCamelCase : int=False , **__lowerCamelCase : Any , ):
SCREAMING_SNAKE_CASE = cls.load_config(
pretrained_model_name_or_path=__lowerCamelCase , subfolder=__lowerCamelCase , return_unused_kwargs=__lowerCamelCase , return_commit_hash=__lowerCamelCase , **__lowerCamelCase , )
return cls.from_config(__lowerCamelCase , return_unused_kwargs=__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : Dict = False , **__lowerCamelCase : str ):
self.save_config(save_directory=__lowerCamelCase , push_to_hub=__lowerCamelCase , **__lowerCamelCase )
@property
def _snake_case ( self : Union[str, Any] ):
return self._get_compatibles()
@classmethod
def _snake_case ( cls : List[str] ):
SCREAMING_SNAKE_CASE = list(set([cls.__name__] + cls._compatibles ) )
SCREAMING_SNAKE_CASE = importlib.import_module(__name__.split("." )[0] )
SCREAMING_SNAKE_CASE = [
getattr(__lowerCamelCase , __lowerCamelCase ) for c in compatible_classes_str if hasattr(__lowerCamelCase , __lowerCamelCase )
]
return compatible_classes | 713 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.values[key]
def _snake_case ( self : Union[str, Any] ):
return (
sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Tuple=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(__lowerCamelCase , __lowerCamelCase ) | 698 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __lowercase ):
'''simple docstring'''
lowerCamelCase__ = (EulerDiscreteScheduler,)
lowerCamelCase__ = 1_0
def _snake_case ( self : List[Any] , **__lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = {
"num_train_timesteps": 1100,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**__A )
return config
def _snake_case ( self : Union[str, Any] ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__A )
def _snake_case ( self : int ):
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__A , beta_end=__A )
def _snake_case ( self : List[str] ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__A )
def _snake_case ( self : Any ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__A )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE = sample.to(__A )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__A , __A )
SCREAMING_SNAKE_CASE = model(__A , __A )
SCREAMING_SNAKE_CASE = scheduler.step(__A , __A , __A , generator=__A )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 10.0_807 ) < 1e-2
assert abs(result_mean.item() - 0.0_131 ) < 1e-3
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config(prediction_type="v_prediction" )
SCREAMING_SNAKE_CASE = scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE = sample.to(__A )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__A , __A )
SCREAMING_SNAKE_CASE = model(__A , __A )
SCREAMING_SNAKE_CASE = scheduler.step(__A , __A , __A , generator=__A )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 0.0_002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps , device=__A )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
SCREAMING_SNAKE_CASE = sample.to(__A )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__A , __A )
SCREAMING_SNAKE_CASE = model(__A , __A )
SCREAMING_SNAKE_CASE = scheduler.step(__A , __A , __A , generator=__A )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 10.0_807 ) < 1e-2
assert abs(result_mean.item() - 0.0_131 ) < 1e-3
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__A , use_karras_sigmas=__A )
scheduler.set_timesteps(self.num_inference_steps , device=__A )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
SCREAMING_SNAKE_CASE = sample.to(__A )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__A , __A )
SCREAMING_SNAKE_CASE = model(__A , __A )
SCREAMING_SNAKE_CASE = scheduler.step(__A , __A , __A , generator=__A )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1e-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1e-3 | 714 |
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
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {
'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 _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "gpt_neo"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : str , __lowerCamelCase : Dict=50257 , __lowerCamelCase : Tuple=2048 , __lowerCamelCase : Optional[Any]=2048 , __lowerCamelCase : int=24 , __lowerCamelCase : int=[[["global", "local"], 12]] , __lowerCamelCase : int=16 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]=256 , __lowerCamelCase : Tuple="gelu_new" , __lowerCamelCase : Optional[Any]=0.0 , __lowerCamelCase : str=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : List[Any]=1e-5 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Dict=True , __lowerCamelCase : Dict=50256 , __lowerCamelCase : Optional[int]=50256 , **__lowerCamelCase : Dict , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_layers
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = window_size
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_dropout
SCREAMING_SNAKE_CASE = embed_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = attention_types
SCREAMING_SNAKE_CASE = self.expand_attention_types_params(__lowerCamelCase )
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=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
@staticmethod
def _snake_case ( __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __a ( A__ : str , A__ : List[Any] , A__ : List[str] , A__ : Union[str, Any] ):
import torch
SCREAMING_SNAKE_CASE = input.size()
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = shape[dimension]
SCREAMING_SNAKE_CASE = torch.arange(0 , A__ , A__ )
SCREAMING_SNAKE_CASE = torch.div(sizedim - size , A__ , rounding_mode="floor" ) + 1
SCREAMING_SNAKE_CASE = torch.arange(A__ ) + low_indices[:min_length][:, None]
SCREAMING_SNAKE_CASE = [slice(A__ )] * rank
SCREAMING_SNAKE_CASE = indices
SCREAMING_SNAKE_CASE = input[s]
SCREAMING_SNAKE_CASE = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A__ )
def __a ( A__ : Union[str, Any] , A__ : Optional[int] ):
import torch
SCREAMING_SNAKE_CASE = torch.arange(1 , A__ )
SCREAMING_SNAKE_CASE = torch.remainder(A__ , A__ )
SCREAMING_SNAKE_CASE = remainders == 0
SCREAMING_SNAKE_CASE = candidates[divisor_indices]
SCREAMING_SNAKE_CASE = torch.max(A__ )
return largest_divisor, torch.div(A__ , A__ , rounding_mode="floor" )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(__lowerCamelCase , direction="inputs" )
SCREAMING_SNAKE_CASE = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _snake_case ( self : Optional[int] ):
return self._config.num_heads
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : PreTrainedTokenizer , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE = super(__lowerCamelCase , self ).generate_dummy_inputs(
__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE = seqlen + 2
SCREAMING_SNAKE_CASE = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE = [
(torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase , __lowerCamelCase , dtype=__lowerCamelCase )] , dim=1 )
return ordered_inputs
@property
def _snake_case ( self : Optional[int] ):
return 13 | 698 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
__A : List[Any] = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _snake_case ( cls : Dict ):
SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(__A )
@classmethod
def _snake_case ( cls : str ):
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE = FlaxBertModel(__A )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax" )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__A , 1e-3 , msg=f"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__A , repo_id="test-model-flax" , push_to_hub=__A , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax" )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__A , 1e-3 , msg=f"{key} not identical" )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE = FlaxBertModel(__A )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__A , 1e-3 , msg=f"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__A , repo_id="valid_org/test-model-flax-org" , push_to_hub=__A , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__A , 1e-3 , msg=f"{key} not identical" )
def __a ( A__ : str , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE = False
return models_are_equal
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE = FlaxBertModel(__A )
SCREAMING_SNAKE_CASE = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__A , __A ) )
with self.assertRaises(__A ):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A , subfolder=__A )
self.assertTrue(check_models_equal(__A , __A ) )
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE = FlaxBertModel(__A )
SCREAMING_SNAKE_CASE = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__A , __A ) , max_shard_size="10KB" )
with self.assertRaises(__A ):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A , subfolder=__A )
self.assertTrue(check_models_equal(__A , __A ) )
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = "bert"
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__A ):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A , subfolder=__A )
self.assertIsNotNone(__A )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = "bert"
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__A ):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A )
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(__A , subfolder=__A )
self.assertIsNotNone(__A )
| 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'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
__A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
def __a ( A__ : List[Any] , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = [1]
for i in range(2 , _UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = list(range(_UpperCAmelCase ) )
# Find permutation
while factorials:
SCREAMING_SNAKE_CASE = factorials.pop()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = divmod(_UpperCAmelCase , _UpperCAmelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod() | 716 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
SCREAMING_SNAKE_CASE = cmath.rect(A__ , A__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 698 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MobileBertTokenizer
lowerCamelCase__ = MobileBertTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = filter_non_english
lowerCamelCase__ = "google/mobilebert-uncased"
def _snake_case ( self : List[Any] ):
super().setUp()
SCREAMING_SNAKE_CASE = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE = 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] ) )
SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def _snake_case ( self : Optional[Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = 'unwanted, running'
return input_text, output_text
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(__a , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def _snake_case ( self : Optional[Any] ):
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
SCREAMING_SNAKE_CASE = tokenizer.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = tokenizer.encode(__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# With lower casing
SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=__a )
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=__a )
SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
SCREAMING_SNAKE_CASE = tokenizer.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = tokenizer.encode(__a )
SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=__a , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(__a ):
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=__a , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def _snake_case ( self : List[str] ):
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def _snake_case ( self : Any ):
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def _snake_case ( self : Union[str, Any] ):
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(__a ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
SCREAMING_SNAKE_CASE = tokenizer.encode("sequence builders" , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer.encode("multi-sequence build" , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(__a )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def _snake_case ( self : List[str] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__a , **__a )
SCREAMING_SNAKE_CASE = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
__a , return_attention_mask=__a , return_token_type_ids=__a , return_offsets_mapping=__a , add_special_tokens=__a , )
SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(__a , "do_lower_case" ) else False
SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = ['的', '人', '有']
SCREAMING_SNAKE_CASE = ''.join(__a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__a , **__a )
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__a , **__a )
SCREAMING_SNAKE_CASE = tokenizer_p.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer_r.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(__a )
SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__a , **__a )
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__a , **__a )
SCREAMING_SNAKE_CASE = tokenizer_r.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer_p.encode(__a , add_special_tokens=__a )
SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(__a )
SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that only the first Chinese character is not preceded by "##".
SCREAMING_SNAKE_CASE = [
f"##{token}" if idx != 0 else token for idx, token in enumerate(__a )
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a ) | 717 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A__ , A__ )
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(A__ , A__ , bias=A__ )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __a ( A__ : Tuple , A__ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE = {}
for old_key in state_dict.keys():
SCREAMING_SNAKE_CASE = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts.0" , F"ffn.experts.expert_{expert_idx}" )
else:
SCREAMING_SNAKE_CASE = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
SCREAMING_SNAKE_CASE = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
SCREAMING_SNAKE_CASE = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
SCREAMING_SNAKE_CASE = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
SCREAMING_SNAKE_CASE = key.replace("final_layer_norm" , "ff_layer_norm" )
SCREAMING_SNAKE_CASE = state_dict[old_key]
return new_dict
def __a ( A__ : List[str] , A__ : List[Any] , A__ : str , A__ : Union[str, Any] , A__ : str = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
os.makedirs(A__ , exist_ok=A__ )
for expert in range(A__ ):
SCREAMING_SNAKE_CASE = switch_checkpoint_path + F"-rank-{expert}.pt"
if os.path.isfile(A__ ):
SCREAMING_SNAKE_CASE = torch.load(A__ )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = os.path.join(
A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
torch.save(A__ , A__ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A__ )[0]].dtype )
# Add the last block
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{len(A__ )+1:05d}-of-???.bin" ) )
SCREAMING_SNAKE_CASE = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A__ )
SCREAMING_SNAKE_CASE = rename_fairseq_keys(A__ , A__ )
SCREAMING_SNAKE_CASE = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A__ ) == 1:
SCREAMING_SNAKE_CASE = os.path.join(A__ , A__ )
torch.save(A__ , A__ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A__ , A__ )
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(A__ ):
SCREAMING_SNAKE_CASE = weights_name.replace(".bin" , F"-{idx+1:05d}-of-{len(A__ ):05d}.bin" )
SCREAMING_SNAKE_CASE = os.path.join(A__ , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
for key in shard:
SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE = {"total_size": total_size}
SCREAMING_SNAKE_CASE = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A__ , A__ ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(A__ , indent=2 , sort_keys=A__ ) + "\n"
f.write(A__ )
return metadata, index
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : Optional[int] = parser.parse_args()
__A , __A : Union[str, Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
__A : Any = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : Any = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path) | 698 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : int = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "altclip_text_model"
def __init__( self : List[str] , __lowerCamelCase : Union[str, Any]=250002 , __lowerCamelCase : List[str]=1024 , __lowerCamelCase : int=24 , __lowerCamelCase : int=16 , __lowerCamelCase : Dict=4096 , __lowerCamelCase : List[Any]="gelu" , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : int=514 , __lowerCamelCase : Optional[Any]=1 , __lowerCamelCase : int=0.02 , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : Optional[int]=1e-05 , __lowerCamelCase : Any=1 , __lowerCamelCase : Optional[int]=0 , __lowerCamelCase : Union[str, Any]=2 , __lowerCamelCase : Dict="absolute" , __lowerCamelCase : Any=True , __lowerCamelCase : Tuple=768 , **__lowerCamelCase : Optional[int] , ):
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = initializer_factor
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = project_dim
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "altclip_vision_model"
def __init__( self : Any , __lowerCamelCase : Union[str, Any]=768 , __lowerCamelCase : List[Any]=3072 , __lowerCamelCase : str=512 , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : Optional[Any]=3 , __lowerCamelCase : Tuple=224 , __lowerCamelCase : Dict=32 , __lowerCamelCase : Tuple="quick_gelu" , __lowerCamelCase : List[str]=1e-5 , __lowerCamelCase : Tuple=0.0 , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : str=1.0 , **__lowerCamelCase : str , ):
super().__init__(**__A )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = projection_dim
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = initializer_factor
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def _snake_case ( cls : List[str] , __lowerCamelCase : Union[str, os.PathLike] , **__lowerCamelCase : Optional[int] ):
cls._set_token_in_kwargs(__A )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("model_type" ) == "altclip":
SCREAMING_SNAKE_CASE = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__A , **__A )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "altclip"
lowerCamelCase__ = True
def __init__( self : Optional[int] , __lowerCamelCase : Tuple=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Dict=768 , __lowerCamelCase : Optional[Any]=2.6_592 , **__lowerCamelCase : Dict ):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
SCREAMING_SNAKE_CASE = kwargs.pop("text_config_dict" , __A )
SCREAMING_SNAKE_CASE = kwargs.pop("vision_config_dict" , __A )
super().__init__(**__A )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
SCREAMING_SNAKE_CASE = {}
# This is the complete result when using `text_config_dict`.
SCREAMING_SNAKE_CASE = AltCLIPTextConfig(**__A ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
SCREAMING_SNAKE_CASE = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
SCREAMING_SNAKE_CASE = (
f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
f"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(__A )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
# This is the complete result when using `vision_config_dict`.
SCREAMING_SNAKE_CASE = AltCLIPVisionConfig(**__A ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
SCREAMING_SNAKE_CASE = {
str(__A ): value for key, value in _vision_config_dict["id2label"].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
SCREAMING_SNAKE_CASE = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
SCREAMING_SNAKE_CASE = (
f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
f"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(__A )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values." )
SCREAMING_SNAKE_CASE = AltCLIPTextConfig(**__A )
SCREAMING_SNAKE_CASE = AltCLIPVisionConfig(**__A )
SCREAMING_SNAKE_CASE = projection_dim
SCREAMING_SNAKE_CASE = logit_scale_init_value
SCREAMING_SNAKE_CASE = 1.0
@classmethod
def _snake_case ( cls : str , __lowerCamelCase : AltCLIPTextConfig , __lowerCamelCase : AltCLIPVisionConfig , **__lowerCamelCase : Dict ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.text_config.to_dict()
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output | 718 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCamelCase , __lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def _snake_case ( self : Dict , **__lowerCamelCase : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).tokenizer
def _snake_case ( self : List[Any] , **__lowerCamelCase : Optional[Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase ).image_processor
def _snake_case ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(__lowerCamelCase , return_tensors="np" )
SCREAMING_SNAKE_CASE = processor(images=__lowerCamelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__lowerCamelCase ):
processor()
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = "lower newer"
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=__lowerCamelCase , images=__lowerCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 698 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A : List[Any] = 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(__snake_case )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "rag"
lowerCamelCase__ = True
def __init__( self : str , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : str=True , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : str=None , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : Dict=None , __lowerCamelCase : int=" / " , __lowerCamelCase : Optional[int]=" // " , __lowerCamelCase : List[str]=5 , __lowerCamelCase : Any=300 , __lowerCamelCase : int=768 , __lowerCamelCase : Any=8 , __lowerCamelCase : str="wiki_dpr" , __lowerCamelCase : List[str]="train" , __lowerCamelCase : Dict="compressed" , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Dict=None , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[Any]=False , __lowerCamelCase : Optional[Any]=0.0 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[str]=False , __lowerCamelCase : str=False , __lowerCamelCase : Any=False , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Optional[Any]=None , **__lowerCamelCase : str , ):
super().__init__(
bos_token_id=_lowercase , pad_token_id=_lowercase , eos_token_id=_lowercase , decoder_start_token_id=_lowercase , forced_eos_token_id=_lowercase , is_encoder_decoder=_lowercase , prefix=_lowercase , vocab_size=_lowercase , **_lowercase , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
SCREAMING_SNAKE_CASE = kwargs.pop("question_encoder" )
SCREAMING_SNAKE_CASE = question_encoder_config.pop("model_type" )
SCREAMING_SNAKE_CASE = kwargs.pop("generator" )
SCREAMING_SNAKE_CASE = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
SCREAMING_SNAKE_CASE = AutoConfig.for_model(_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE = AutoConfig.for_model(_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE = reduce_loss
SCREAMING_SNAKE_CASE = label_smoothing
SCREAMING_SNAKE_CASE = exclude_bos_score
SCREAMING_SNAKE_CASE = do_marginalize
SCREAMING_SNAKE_CASE = title_sep
SCREAMING_SNAKE_CASE = doc_sep
SCREAMING_SNAKE_CASE = n_docs
SCREAMING_SNAKE_CASE = max_combined_length
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = dataset_split
SCREAMING_SNAKE_CASE = index_name
SCREAMING_SNAKE_CASE = retrieval_vector_size
SCREAMING_SNAKE_CASE = retrieval_batch_size
SCREAMING_SNAKE_CASE = passages_path
SCREAMING_SNAKE_CASE = index_path
SCREAMING_SNAKE_CASE = use_dummy_dataset
SCREAMING_SNAKE_CASE = output_retrieved
SCREAMING_SNAKE_CASE = do_deduplication
SCREAMING_SNAKE_CASE = use_cache
if self.forced_eos_token_id is None:
SCREAMING_SNAKE_CASE = getattr(self.generator , "forced_eos_token_id" , _lowercase )
@classmethod
def _snake_case ( cls : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : Optional[Any] ):
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **_lowercase )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.question_encoder.to_dict()
SCREAMING_SNAKE_CASE = self.generator.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output | 719 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def _snake_case ( *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Union[str, Any] ):
pass
def __a ( A__ : str ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A : Tuple = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model=__lowerCamelCase , tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
SCREAMING_SNAKE_CASE = "What is the placebo?"
SCREAMING_SNAKE_CASE = [
{
"image": load_image(__lowerCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def _snake_case ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = dqa_pipeline(__lowerCamelCase , top_k=2 )
self.assertEqual(
__lowerCamelCase , [
[
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
{"score": ANY(__lowerCamelCase ), "answer": ANY(__lowerCamelCase ), "start": ANY(__lowerCamelCase ), "end": ANY(__lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "How many cats are there?"
SCREAMING_SNAKE_CASE = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , __lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , words=__lowerCamelCase , boxes=__lowerCamelCase , top_k=2 )
self.assertEqual(__lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__lowerCamelCase )
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__lowerCamelCase , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE = list(zip(*apply_tesseract(load_image(__lowerCamelCase ) , __lowerCamelCase , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE = INVOICE_URL
SCREAMING_SNAKE_CASE = "What is the invoice number?"
SCREAMING_SNAKE_CASE = dqa_pipeline(image=__lowerCamelCase , question=__lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(__lowerCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def _snake_case ( self : List[Any] ):
pass | 698 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : List[str]=3 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : int=True , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : int=224 , __lowerCamelCase : List[str]=1000 , __lowerCamelCase : Optional[Any]=[3, 3, 6, 4] , __lowerCamelCase : List[Any]=[48, 56, 112, 220] , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = layer_depths
SCREAMING_SNAKE_CASE = embed_dims
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Optional[Any] ):
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="gelu" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=A__ , layer_scale_init_value=1e-5 , )
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : str , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = SwiftFormerModel(config=A__ )
model.to(A__ )
model.eval()
SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = SwiftFormerForImageClassification(A__ )
model.to(A__ )
model.eval()
SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
SCREAMING_SNAKE_CASE = SwiftFormerForImageClassification(A__ )
model.to(A__ )
model.eval()
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Tuple ):
(SCREAMING_SNAKE_CASE) = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = SwiftFormerModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(
self , config_class=A__ , has_text_modality=A__ , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def _snake_case ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason="SwiftFormer does not use inputs_embeds" )
def _snake_case ( self : List[Any] ):
pass
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(A__ )
SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , nn.Linear ) )
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(A__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A__ )
def _snake_case ( self : Dict ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def _snake_case ( self : str ):
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = SwiftFormerModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@unittest.skip(reason="SwiftFormer does not output attentions" )
def _snake_case ( self : str ):
pass
def _snake_case ( self : Optional[int] ):
def check_hidden_states_output(__lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = 8
self.assertEqual(len(A__ ) , A__ ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(A__ ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
def _snake_case ( self : List[Any] ):
def _config_zero_init(__lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = copy.deepcopy(A__ )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(A__ , A__ , 1e-10 )
if isinstance(getattr(A__ , A__ , A__ ) , A__ ):
SCREAMING_SNAKE_CASE = _config_zero_init(getattr(A__ , A__ ) )
setattr(A__ , A__ , A__ )
return configs_no_init
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = _config_zero_init(A__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(config=A__ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : Optional[int] ):
pass
def __a ( ):
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : str ):
return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs" ) if is_vision_available() else None
@slow
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs" ).to(A__ )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="pt" ).to(A__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**A__ )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , A__ )
SCREAMING_SNAKE_CASE = torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1e-4 ) ) | 720 |
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "facebook/bart-large-mnli"
lowerCamelCase__ = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
lowerCamelCase__ = "text_classifier"
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSequenceClassification
lowerCamelCase__ = ["text", ["text"]]
lowerCamelCase__ = ["text"]
def _snake_case ( self : Optional[Any] ):
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(__lowerCamelCase ) , [f"This example is {label}" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id] | 698 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
__A : List[str] = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
__A : List[Any] = 'UperNetConfig'
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , __lowerCamelCase : Dict , __lowerCamelCase : int , __lowerCamelCase : Dict , __lowerCamelCase : List[Any] = 0 , __lowerCamelCase : List[str] = False , __lowerCamelCase : int = 1 , ):
super().__init__()
SCREAMING_SNAKE_CASE = nn.Convad(
in_channels=_a , out_channels=_a , kernel_size=_a , padding=_a , bias=_a , dilation=_a , )
SCREAMING_SNAKE_CASE = nn.BatchNormad(_a )
SCREAMING_SNAKE_CASE = nn.ReLU()
def _snake_case ( self : Any , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = self.conv(_a )
SCREAMING_SNAKE_CASE = self.batch_norm(_a )
SCREAMING_SNAKE_CASE = self.activation(_a )
return output
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
super().__init__()
SCREAMING_SNAKE_CASE = [
nn.AdaptiveAvgPoolad(_a ),
UperNetConvModule(_a , _a , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_a ) , _a )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Any ):
SCREAMING_SNAKE_CASE = input
for layer in self.layers:
SCREAMING_SNAKE_CASE = layer(_a )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any , __lowerCamelCase : Optional[int] ):
super().__init__()
SCREAMING_SNAKE_CASE = pool_scales
SCREAMING_SNAKE_CASE = align_corners
SCREAMING_SNAKE_CASE = in_channels
SCREAMING_SNAKE_CASE = channels
SCREAMING_SNAKE_CASE = []
for i, pool_scale in enumerate(_a ):
SCREAMING_SNAKE_CASE = UperNetPyramidPoolingBlock(pool_scale=_a , in_channels=_a , channels=_a )
self.blocks.append(_a )
self.add_module(str(_a ) , _a )
def _snake_case ( self : Dict , __lowerCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = []
for ppm in self.blocks:
SCREAMING_SNAKE_CASE = ppm(_a )
SCREAMING_SNAKE_CASE = nn.functional.interpolate(
_a , size=x.size()[2:] , mode="bilinear" , align_corners=self.align_corners )
ppm_outs.append(_a )
return ppm_outs
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any] ):
super().__init__()
SCREAMING_SNAKE_CASE = config
SCREAMING_SNAKE_CASE = config.pool_scales # e.g. (1, 2, 3, 6)
SCREAMING_SNAKE_CASE = in_channels
SCREAMING_SNAKE_CASE = config.hidden_size
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
SCREAMING_SNAKE_CASE = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
SCREAMING_SNAKE_CASE = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
SCREAMING_SNAKE_CASE = nn.ModuleList()
SCREAMING_SNAKE_CASE = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
SCREAMING_SNAKE_CASE = UperNetConvModule(_a , self.channels , kernel_size=1 )
SCREAMING_SNAKE_CASE = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_a )
self.fpn_convs.append(_a )
SCREAMING_SNAKE_CASE = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def _snake_case ( self : Optional[Any] ):
self.apply(self._init_weights )
def _snake_case ( self : List[Any] , __lowerCamelCase : List[Any] ):
if isinstance(_a , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _snake_case ( self : str , __lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = inputs[-1]
SCREAMING_SNAKE_CASE = [x]
psp_outs.extend(self.psp_modules(_a ) )
SCREAMING_SNAKE_CASE = torch.cat(_a , dim=1 )
SCREAMING_SNAKE_CASE = self.bottleneck(_a )
return output
def _snake_case ( self : int , __lowerCamelCase : List[str] ):
# build laterals
SCREAMING_SNAKE_CASE = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_a ) )
# build top-down path
SCREAMING_SNAKE_CASE = len(_a )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE = laterals[i - 1].shape[2:]
SCREAMING_SNAKE_CASE = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_a , mode="bilinear" , align_corners=self.align_corners )
# build outputs
SCREAMING_SNAKE_CASE = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode="bilinear" , align_corners=self.align_corners )
SCREAMING_SNAKE_CASE = torch.cat(_a , dim=1 )
SCREAMING_SNAKE_CASE = self.fpn_bottleneck(_a )
SCREAMING_SNAKE_CASE = self.classifier(_a )
return output
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : int , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict = 2 , __lowerCamelCase : Optional[int] = 3 , __lowerCamelCase : int = 1 ):
super().__init__()
SCREAMING_SNAKE_CASE = config
SCREAMING_SNAKE_CASE = config.auxiliary_in_channels
SCREAMING_SNAKE_CASE = config.auxiliary_channels
SCREAMING_SNAKE_CASE = config.auxiliary_num_convs
SCREAMING_SNAKE_CASE = config.auxiliary_concat_input
SCREAMING_SNAKE_CASE = in_index
SCREAMING_SNAKE_CASE = (kernel_size // 2) * dilation
SCREAMING_SNAKE_CASE = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_a , padding=_a , dilation=_a ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_a , padding=_a , dilation=_a ) )
if self.num_convs == 0:
SCREAMING_SNAKE_CASE = nn.Identity()
else:
SCREAMING_SNAKE_CASE = nn.Sequential(*_a )
if self.concat_input:
SCREAMING_SNAKE_CASE = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_a , padding=kernel_size // 2 )
SCREAMING_SNAKE_CASE = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def _snake_case ( self : str ):
self.apply(self._init_weights )
def _snake_case ( self : List[Any] , __lowerCamelCase : Optional[Any] ):
if isinstance(_a , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def _snake_case ( self : Optional[Any] , __lowerCamelCase : Dict ):
# just take the relevant feature maps
SCREAMING_SNAKE_CASE = encoder_hidden_states[self.in_index]
SCREAMING_SNAKE_CASE = self.convs(_a )
if self.concat_input:
SCREAMING_SNAKE_CASE = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
SCREAMING_SNAKE_CASE = self.classifier(_a )
return output
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = UperNetConfig
lowerCamelCase__ = 'pixel_values'
lowerCamelCase__ = True
def _snake_case ( self : List[Any] , __lowerCamelCase : Tuple ):
if isinstance(_a , _a ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def _snake_case ( self : List[str] ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def _snake_case ( self : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Dict=False ):
if isinstance(_a , _a ):
SCREAMING_SNAKE_CASE = value
__A : Union[str, Any] = r'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__A : Dict = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." , __SCREAMING_SNAKE_CASE , )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCamelCase : int ):
super().__init__(_a )
SCREAMING_SNAKE_CASE = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
SCREAMING_SNAKE_CASE = UperNetHead(_a , in_channels=self.backbone.channels )
SCREAMING_SNAKE_CASE = UperNetFCNHead(_a ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length" ) )
@replace_return_docstrings(output_type=_a , config_class=_CONFIG_FOR_DOC )
def _snake_case ( self : Dict , __lowerCamelCase : Dict = None , __lowerCamelCase : List[str] = None , __lowerCamelCase : Dict = None , __lowerCamelCase : Optional[Any] = None , __lowerCamelCase : Union[str, Any] = None , ):
SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE = output_attentions if output_attentions is not None else self.config.output_attentions
SCREAMING_SNAKE_CASE = self.backbone.forward_with_filtered_kwargs(
_a , output_hidden_states=_a , output_attentions=_a )
SCREAMING_SNAKE_CASE = outputs.feature_maps
SCREAMING_SNAKE_CASE = self.decode_head(_a )
SCREAMING_SNAKE_CASE = nn.functional.interpolate(_a , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=_a )
SCREAMING_SNAKE_CASE = None
if self.auxiliary_head is not None:
SCREAMING_SNAKE_CASE = self.auxiliary_head(_a )
SCREAMING_SNAKE_CASE = nn.functional.interpolate(
_a , size=pixel_values.shape[2:] , mode="bilinear" , align_corners=_a )
SCREAMING_SNAKE_CASE = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one" )
else:
# compute weighted loss
SCREAMING_SNAKE_CASE = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
SCREAMING_SNAKE_CASE = loss_fct(_a , _a )
SCREAMING_SNAKE_CASE = loss_fct(_a , _a )
SCREAMING_SNAKE_CASE = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
SCREAMING_SNAKE_CASE = (logits,) + outputs[1:]
else:
SCREAMING_SNAKE_CASE = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_a , logits=_a , hidden_states=outputs.hidden_states , attentions=outputs.attentions , ) | 721 |
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __a ( A__ : str=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser("test" )
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=A__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __a ( A__ : Tuple ):
SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
SCREAMING_SNAKE_CASE = script_name
else:
SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
SCREAMING_SNAKE_CASE = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def __a ( ):
SCREAMING_SNAKE_CASE = test_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main() | 698 | 0 |
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = name
SCREAMING_SNAKE_CASE = value
SCREAMING_SNAKE_CASE = weight
def __repr__( self : Tuple ):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def _snake_case ( self : Tuple ):
return self.value
def _snake_case ( self : Any ):
return self.name
def _snake_case ( self : Optional[Any] ):
return self.weight
def _snake_case ( self : Optional[Any] ):
return self.value / self.weight
def __a ( A__ : List[str] , A__ : Tuple , A__ : Tuple ):
SCREAMING_SNAKE_CASE = []
for i in range(len(_lowerCamelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __a ( A__ : Tuple , A__ : List[str] , A__ : List[Any] ):
SCREAMING_SNAKE_CASE = sorted(_lowerCamelCase , key=_lowerCamelCase , reverse=_lowerCamelCase )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0.0, 0.0
for i in range(len(_lowerCamelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __a ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod() | 700 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[int] = logging.get_logger(__name__)
__A : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__A : Tuple = {
'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'
},
}
__A : Dict = {'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ):
SCREAMING_SNAKE_CASE = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE = bs[:]
SCREAMING_SNAKE_CASE = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE = [chr(A__ ) for n in cs]
return dict(zip(A__ , A__ ) )
def __a ( A__ : Optional[int] ):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE = char
return pairs
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any]="replace" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : Dict="</s>" , __lowerCamelCase : Any="<s>" , __lowerCamelCase : Optional[Any]="<unk>" , __lowerCamelCase : str="<pad>" , __lowerCamelCase : List[Any]="<mask>" , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Tuple , ):
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE = bytes_to_unicode()
SCREAMING_SNAKE_CASE = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _snake_case ( self : Dict ):
return len(self.encoder )
def _snake_case ( self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = bigram
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
while i < len(__lowerCamelCase ):
try:
SCREAMING_SNAKE_CASE = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE = tuple(__lowerCamelCase )
SCREAMING_SNAKE_CASE = new_word
if len(__lowerCamelCase ) == 1:
break
else:
SCREAMING_SNAKE_CASE = get_pairs(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = word
return word
def _snake_case ( self : str , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = []
for token in re.findall(self.pat , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : List[str] , __lowerCamelCase : str ):
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : Optional[int] , __lowerCamelCase : Tuple ):
return self.decoder.get(__lowerCamelCase )
def _snake_case ( self : Tuple , __lowerCamelCase : str ):
SCREAMING_SNAKE_CASE = "".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
SCREAMING_SNAKE_CASE = 0
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
SCREAMING_SNAKE_CASE = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
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 )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _snake_case ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 : Any , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=False , **__lowerCamelCase : Optional[int] ):
SCREAMING_SNAKE_CASE = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE = " " + text
return (text, kwargs)
def _snake_case ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def _snake_case ( self : Tuple , __lowerCamelCase : "Conversation" ):
SCREAMING_SNAKE_CASE = []
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(__lowerCamelCase )
SCREAMING_SNAKE_CASE = " ".join(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
SCREAMING_SNAKE_CASE = 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 | 698 | 0 |
from sklearn.metrics import recall_score
import datasets
__A : Dict = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
__A : Any = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
__A : Any = """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def _snake_case ( self : Dict ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] , )
def _snake_case ( self : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Any=1 , __lowerCamelCase : Optional[int]="binary" , __lowerCamelCase : Tuple=None , __lowerCamelCase : str="warn" , ):
SCREAMING_SNAKE_CASE = recall_score(
__lowerCamelCase , __lowerCamelCase , labels=__lowerCamelCase , pos_label=__lowerCamelCase , average=__lowerCamelCase , sample_weight=__lowerCamelCase , zero_division=__lowerCamelCase , )
return {"recall": float(__lowerCamelCase ) if score.size == 1 else score} | 701 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE_CASE = (-b + sqrt(A__ )) / (2 * a)
SCREAMING_SNAKE_CASE = (-b - sqrt(A__ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main() | 698 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = "camembert"
def __init__( self : str , __lowerCamelCase : Any=30522 , __lowerCamelCase : List[str]=768 , __lowerCamelCase : Dict=12 , __lowerCamelCase : Tuple=12 , __lowerCamelCase : Union[str, Any]=3072 , __lowerCamelCase : Optional[int]="gelu" , __lowerCamelCase : Union[str, Any]=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : str=512 , __lowerCamelCase : List[str]=2 , __lowerCamelCase : Any=0.02 , __lowerCamelCase : Tuple=1e-12 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : List[str]=0 , __lowerCamelCase : Dict=2 , __lowerCamelCase : int="absolute" , __lowerCamelCase : Dict=True , __lowerCamelCase : str=None , **__lowerCamelCase : Any , ):
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = classifier_dropout
class _SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
'''simple docstring'''
@property
def _snake_case ( self : Any ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 702 |
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Dict ):
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 _snake_case ( self : int ):
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 _snake_case ( self : List[str] ):
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 _snake_case ( self : int ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
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 _snake_case ( self : Optional[int] ):
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 | 698 | 0 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__A : Any = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = 4_2
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = _str_to_version_tuple(self.version_str )
def __repr__( self : Tuple ):
return f"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def _snake_case ( self : List[Any] ):
return self.major, self.minor, self.patch
def _snake_case ( self : int , __lowerCamelCase : str ):
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return Version(__lowerCamelCase )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return other
raise TypeError(f"{other} (type {type(__lowerCamelCase )}) cannot be compared to version." )
def __eq__( self : Optional[Any] , __lowerCamelCase : Tuple ):
try:
SCREAMING_SNAKE_CASE = self._validate_operand(__lowerCamelCase )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : Optional[int] , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = self._validate_operand(__lowerCamelCase )
return self.tuple < other.tuple
def __hash__( self : List[Any] ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def _snake_case ( cls : Optional[int] , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def _snake_case ( self : int ):
return self.version_str
def __a ( A__ : Dict ):
SCREAMING_SNAKE_CASE = _VERSION_REG.match(SCREAMING_SNAKE_CASE_ )
if not res:
raise ValueError(F"Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(SCREAMING_SNAKE_CASE_ ) for v in [res.group("major" ), res.group("minor" ), res.group("patch" )] )
def __a ( A__ : List[Any] ):
return ".".join(str(SCREAMING_SNAKE_CASE_ ) for v in version_tuple ) | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 698 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A : Dict = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
__A : int = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
__A : List[Any] = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A : Dict = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A : Dict = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A : int = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , __lowercase )
return [m.group(0 ) for m in matches]
def __a ( ):
SCREAMING_SNAKE_CASE = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE = {
config.replace("Config" , "" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(__lowercase ):
SCREAMING_SNAKE_CASE = None
if _re_tf_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = tf_models
SCREAMING_SNAKE_CASE = _re_tf_models.match(__lowercase ).groups()[0]
elif _re_flax_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = flax_models
SCREAMING_SNAKE_CASE = _re_flax_models.match(__lowercase ).groups()[0]
elif _re_pt_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = pt_models
SCREAMING_SNAKE_CASE = _re_pt_models.match(__lowercase ).groups()[0]
if lookup_dict is not None:
while len(__lowercase ) > 0:
if attr_name in model_prefix_to_model_type:
SCREAMING_SNAKE_CASE = True
break
# Try again after removing the last word in the name
SCREAMING_SNAKE_CASE = ''.join(camel_case_split(__lowercase )[:-1] )
SCREAMING_SNAKE_CASE = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
SCREAMING_SNAKE_CASE = list(__lowercase )
all_models.sort()
SCREAMING_SNAKE_CASE = {'model_type': all_models}
SCREAMING_SNAKE_CASE = [pt_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [tf_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
SCREAMING_SNAKE_CASE = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoProcessor'
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoFeatureExtractor'
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
SCREAMING_SNAKE_CASE = [processors[t] for t in all_models]
return pd.DataFrame(__lowercase )
def __a ( A__ : str ):
SCREAMING_SNAKE_CASE = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
SCREAMING_SNAKE_CASE = [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
SCREAMING_SNAKE_CASE = [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
# Loop through all three frameworks
for module, cls, mapping in zip(__lowercase , __lowercase , __lowercase ):
# The type of pipeline may not exist in this framework
if not hasattr(__lowercase , __lowercase ):
continue
# First extract all model_names
SCREAMING_SNAKE_CASE = []
for name in getattr(__lowercase , __lowercase ).values():
if isinstance(__lowercase , __lowercase ):
model_names.append(__lowercase )
else:
model_names.extend(list(__lowercase ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def __a ( A__ : Optional[int] , A__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = get_frameworks_table()
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
SCREAMING_SNAKE_CASE = hf_hub_download(
"huggingface/transformers-metadata" , "pipeline_tags.json" , repo_type="dataset" , token=__lowercase )
SCREAMING_SNAKE_CASE = Dataset.from_json(__lowercase )
SCREAMING_SNAKE_CASE = {
tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
for i in range(len(__lowercase ) )
}
SCREAMING_SNAKE_CASE = update_pipeline_and_auto_class_table(__lowercase )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
SCREAMING_SNAKE_CASE = sorted(table.keys() )
SCREAMING_SNAKE_CASE = pd.DataFrame(
{
"model_class": model_classes,
"pipeline_tag": [table[m][0] for m in model_classes],
"auto_class": [table[m][1] for m in model_classes],
} )
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(__lowercase , "frameworks.json" ) )
tags_dataset.to_json(os.path.join(__lowercase , "pipeline_tags.json" ) )
if commit_sha is not None:
SCREAMING_SNAKE_CASE = (
F"Update with commit {commit_sha}\n\nSee: "
F"https://github.com/huggingface/transformers/commit/{commit_sha}"
)
else:
SCREAMING_SNAKE_CASE = 'Update'
upload_folder(
repo_id="huggingface/transformers-metadata" , folder_path=__lowercase , repo_type="dataset" , token=__lowercase , commit_message=__lowercase , )
def __a ( ):
SCREAMING_SNAKE_CASE = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
SCREAMING_SNAKE_CASE = transformers_module.pipelines.SUPPORTED_TASKS
SCREAMING_SNAKE_CASE = []
for key in pipeline_tasks:
if key not in in_table:
SCREAMING_SNAKE_CASE = pipeline_tasks[key]['pt']
if isinstance(__lowercase , (list, tuple) ):
SCREAMING_SNAKE_CASE = model[0]
SCREAMING_SNAKE_CASE = model.__name__
if model not in in_table.values():
missing.append(__lowercase )
if len(__lowercase ) > 0:
SCREAMING_SNAKE_CASE = ', '.join(__lowercase )
raise ValueError(
"The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
F"`utils/update_metadata.py`: {msg}. Please add them!" )
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
__A : Optional[Any] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha) | 704 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 698 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 705 |
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
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "resnet"
lowerCamelCase__ = ["basic", "bottleneck"]
def __init__( self : Optional[Any] , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=64 , __lowerCamelCase : str=[256, 512, 1024, 2048] , __lowerCamelCase : str=[3, 4, 6, 3] , __lowerCamelCase : Optional[int]="bottleneck" , __lowerCamelCase : int="relu" , __lowerCamelCase : List[str]=False , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : int=None , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = layer_type
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = downsample_in_first_stage
SCREAMING_SNAKE_CASE = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = version.parse("1.11" )
@property
def _snake_case ( self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : Optional[int] ):
return 1e-3 | 698 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
default="cifar10" , metadata={"help": "Name of a dataset from the datasets package"} )
lowerCamelCase__ = field(
default=__a , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowerCamelCase__ = field(
default=__a , metadata={"help": "The column name of the images in the files."} )
lowerCamelCase__ = field(default=__a , metadata={"help": "A folder containing the training data."} )
lowerCamelCase__ = field(default=__a , metadata={"help": "A folder containing the validation data."} )
lowerCamelCase__ = field(
default=0.15 , metadata={"help": "Percent to split off of train for validation."} )
lowerCamelCase__ = field(
default=__a , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowerCamelCase__ = field(
default=__a , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = {}
if self.train_dir is not None:
SCREAMING_SNAKE_CASE = self.train_dir
if self.validation_dir is not None:
SCREAMING_SNAKE_CASE = self.validation_dir
SCREAMING_SNAKE_CASE = data_files if data_files else None
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
default=__a , metadata={
"help": (
"The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
)
} , )
lowerCamelCase__ = field(
default=__a , metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"} )
lowerCamelCase__ = field(
default=__a , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
lowerCamelCase__ = field(
default=__a , metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
lowerCamelCase__ = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowerCamelCase__ = field(default=__a , metadata={"help": "Name or path of preprocessor config."} )
lowerCamelCase__ = field(
default=__a , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
lowerCamelCase__ = field(
default=0.75 , metadata={"help": "The ratio of the number of masked tokens in the input sequence."} )
lowerCamelCase__ = field(
default=__a , metadata={"help": "Whether or not to train with normalized pixel values as target."} )
@dataclass
class _SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
lowerCamelCase__ = field(
default=1e-3 , metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."} )
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE = torch.stack([example["pixel_values"] for example in examples] )
return {"pixel_values": pixel_values}
def __a ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_mae" , lowerCAmelCase__ , lowerCAmelCase__ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE = training_args.get_process_log_level()
logger.setLevel(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.
SCREAMING_SNAKE_CASE = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE = 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." )
# Initialize our dataset.
SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
SCREAMING_SNAKE_CASE = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowerCAmelCase__ ) and data_args.train_val_split > 0.0:
SCREAMING_SNAKE_CASE = ds["train"].train_test_split(data_args.train_val_split )
SCREAMING_SNAKE_CASE = split["train"]
SCREAMING_SNAKE_CASE = split["test"]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
SCREAMING_SNAKE_CASE = ViTMAEConfig.from_pretrained(model_args.config_name , **lowerCAmelCase__ )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = ViTMAEConfig()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"mask_ratio": model_args.mask_ratio,
"norm_pix_loss": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
SCREAMING_SNAKE_CASE = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowerCAmelCase__ )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
SCREAMING_SNAKE_CASE = ViTMAEForPreTraining.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 , )
else:
logger.info("Training new model from scratch" )
SCREAMING_SNAKE_CASE = ViTMAEForPreTraining(lowerCAmelCase__ )
if training_args.do_train:
SCREAMING_SNAKE_CASE = ds["train"].column_names
else:
SCREAMING_SNAKE_CASE = ds["validation"].column_names
if data_args.image_column_name is not None:
SCREAMING_SNAKE_CASE = data_args.image_column_name
elif "image" in column_names:
SCREAMING_SNAKE_CASE = "image"
elif "img" in column_names:
SCREAMING_SNAKE_CASE = "img"
else:
SCREAMING_SNAKE_CASE = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
SCREAMING_SNAKE_CASE = image_processor.size["shortest_edge"]
else:
SCREAMING_SNAKE_CASE = (image_processor.size["height"], image_processor.size["width"])
SCREAMING_SNAKE_CASE = Compose(
[
Lambda(lambda A__ : img.convert("RGB" ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowerCAmelCase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(A__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = [transforms(lowerCAmelCase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE = ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowerCAmelCase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE = (
ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowerCAmelCase__ )
# Compute absolute learning rate
SCREAMING_SNAKE_CASE = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
SCREAMING_SNAKE_CASE = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
SCREAMING_SNAKE_CASE = Trainer(
model=lowerCAmelCase__ , args=lowerCAmelCase__ , train_dataset=ds["train"] if training_args.do_train else None , eval_dataset=ds["validation"] if training_args.do_eval else None , tokenizer=lowerCAmelCase__ , data_collator=lowerCAmelCase__ , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE = last_checkpoint
SCREAMING_SNAKE_CASE = trainer.train(resume_from_checkpoint=lowerCAmelCase__ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
SCREAMING_SNAKE_CASE = trainer.evaluate()
trainer.log_metrics("eval" , lowerCAmelCase__ )
trainer.save_metrics("eval" , lowerCAmelCase__ )
# Write model card and (optionally) push to hub
SCREAMING_SNAKE_CASE = {
"tasks": "masked-auto-encoding",
"dataset": data_args.dataset_name,
"tags": ["masked-auto-encoding"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase__ )
else:
trainer.create_model_card(**lowerCAmelCase__ )
def __a ( A__ : Union[str, Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main() | 706 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "imagegpt"
lowerCamelCase__ = ["past_key_values"]
lowerCamelCase__ = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , __lowerCamelCase : Any=512 + 1 , __lowerCamelCase : str=32 * 32 , __lowerCamelCase : Any=512 , __lowerCamelCase : Optional[int]=24 , __lowerCamelCase : Tuple=8 , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[Any]="quick_gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Optional[Any]=1e-5 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : int=False , __lowerCamelCase : int=False , **__lowerCamelCase : Union[str, Any] , ):
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = n_positions
SCREAMING_SNAKE_CASE = n_embd
SCREAMING_SNAKE_CASE = n_layer
SCREAMING_SNAKE_CASE = n_head
SCREAMING_SNAKE_CASE = n_inner
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = resid_pdrop
SCREAMING_SNAKE_CASE = embd_pdrop
SCREAMING_SNAKE_CASE = attn_pdrop
SCREAMING_SNAKE_CASE = layer_norm_epsilon
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scale_attn_weights
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE = tie_word_embeddings
super().__init__(tie_word_embeddings=__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
@property
def _snake_case ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def _snake_case ( self : Optional[int] , __lowerCamelCase : "FeatureExtractionMixin" , __lowerCamelCase : int = 1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 32 , __lowerCamelCase : int = 32 , ):
SCREAMING_SNAKE_CASE = self._generate_dummy_images(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = dict(preprocessor(images=__lowerCamelCase , return_tensors=__lowerCamelCase ) )
return inputs | 698 | 0 |
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