



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 						snake_case__							(       )		->		Any:







						"""simple docstring"""

						def filter_log(__lowercase							):
												return False if "This IS expected if you are initializing" in record.msg else True

						A__						:				Dict         =			datasets.utils.logging.get_logger("transformers.modeling_utils"							)
						logger.addFilter(__lowercase							)
						try:
												yield
						finally:
												logger.removeFilter(__lowercase							)


snake_case     :    str						=    '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

snake_case     :    Dict						=    '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case     :    Optional[int]						=    '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,				_KWARGS_DESCRIPTION    )
class 				lowerCAmelCase__	(						datasets.Metric    ):







					def 			_lowercase      (							self   :  Dict):
											return datasets.MetricInfo(
											    description=_DESCRIPTION      ,		citation=_CITATION      ,		homepage="https://github.com/Tiiiger/bert_score"      ,		inputs_description=_KWARGS_DESCRIPTION      ,		features=datasets.Features(
											        {
											            "predictions": datasets.Value("string"      ,		id="sequence"),
											            "references": datasets.Sequence(datasets.Value("string"      ,		id="sequence")      ,		id="references"),
											        })      ,		codebase_urls=["https://github.com/Tiiiger/bert_score"]      ,		reference_urls=[
											        "https://github.com/Tiiiger/bert_score",
											        "https://arxiv.org/abs/1904.09675",
											    ]      ,		)







					def 			_lowercase      (							self   :  Optional[Any]      ,		_A   :  int      ,		_A   :  str      ,		_A   :  Union[str, Any]=None      ,		_A   :  Dict=None      ,		_A   :  int=None      ,		_A   :  List[str]=False      ,		_A   :  List[str]=False      ,		_A   :  Tuple=None      ,		_A   :  Union[str, Any]=64      ,		_A   :  Optional[Any]=4      ,		_A   :  List[str]=False      ,		_A   :  int=False      ,		_A   :  Any=None      ,		_A   :  Union[str, Any]=False      ,		):
											A__						:				Tuple         =			bert_score.utils.get_hash
											A__						:				Optional[Any]         =			bert_score.BERTScorer

											if version.parse(bert_score.__version__) >= version.parse("0.3.10"):
																	A__						:				Dict         =			functools.partial(_A      ,		use_fast_tokenizer=_A)
																	A__						:				Optional[int]         =			functools.partial(_A      ,		use_fast_tokenizer=_A)
											elif use_fast_tokenizer:
																	raise ImportWarning(
																	    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
																	    "You can install it with `pip install \"bert-score>=0.3.10\"`.")

											if model_type is None:
																	assert lang is not None, "either lang or model_type should be specified"
																	A__						:				List[Any]         =			bert_score.utils.langamodel[lang.lower()]

											if num_layers is None:
																	A__						:				Optional[Any]         =			bert_score.utils.modelalayers[model_type]

											A__						:				Any         =			get_hash(
											    model=_A      ,		num_layers=_A      ,		idf=_A      ,		rescale_with_baseline=_A      ,		use_custom_baseline=baseline_path is not None      ,		)

											with filter_logging_context():
																	if not hasattr(self      ,		"cached_bertscorer") or self.cached_bertscorer.hash != hashcode:
																							A__						:				Dict         =			scorer(
																							    model_type=_A      ,		num_layers=_A      ,		batch_size=_A      ,		nthreads=_A      ,		all_layers=_A      ,		idf=_A      ,		device=_A      ,		lang=_A      ,		rescale_with_baseline=_A      ,		baseline_path=_A      ,		)

											((A__)     ,							(A__)     ,							(A__))						:				List[Any]         =			self.cached_bertscorer.score(
											    cands=_A      ,		refs=_A      ,		verbose=_A      ,		batch_size=_A      ,		)
											A__						:				Union[str, Any]         =			{
											    "precision": P.tolist(),
											    "recall": R.tolist(),
											    "f1": F.tolist(),
											    "hashcode": hashcode,
											}
											return output_dict







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Tuple=None      ,		_A   :  Union[str, Any]=None      ,		**_A   :  Dict):
											# References can be strings or lists of strings
											# Let's change strings to lists of strings with one element
											if references is not None:
																	A__						:				List[str]         =			[[ref] if isinstance(_A      ,		_A) else ref for ref in references]
											super().add_batch(predictions=_A      ,		references=_A      ,		**_A)







					def 			_lowercase      (							self   :  List[Any]      ,		_A   :  Dict=None      ,		_A   :  Optional[Any]=None      ,		**_A   :  int):
											# References can be strings or lists of strings
											# Let's change strings to lists of strings with one element
											if isinstance(_A      ,		_A):
																	A__						:				int         =			[reference]
											super().add(prediction=_A      ,		reference=_A      ,		**_A)




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)