code: 
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  
  
  
  
  
  @contextmanager
  def 							UpperCAmelCase				(     )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def filter_log(UpperCAmelCase      ):
  										return False if "This IS expected if you are initializing" in record.msg else True
  
  					SCREAMING_SNAKE_CASE_	      =       datasets.utils.logging.get_logger('''transformers.modeling_utils'''      )
  					logger.addFilter(UpperCAmelCase      )
  					try:
  										yield
  					finally:
  										logger.removeFilter(UpperCAmelCase      )
  
  
  A_														=      "\\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"
  
  A_														=      "\\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"
  
  A_														=      "\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 						snake_case			(       datasets.Metric	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     List[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								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     :     List[Any]							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     Optional[Any]							,							lowerCAmelCase_     :     int=None							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Dict=None							,							lowerCAmelCase_     :     List[Any]=False							,							lowerCAmelCase_     :     Any=False							,							lowerCAmelCase_     :     Union[str, Any]=None							,							lowerCAmelCase_     :     str=64							,							lowerCAmelCase_     :     Optional[Any]=4							,							lowerCAmelCase_     :     Dict=False							,							lowerCAmelCase_     :     List[str]=False							,							lowerCAmelCase_     :     str=None							,							lowerCAmelCase_     :     int=False							,							)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       bert_score.utils.get_hash
  								SCREAMING_SNAKE_CASE_	      =       bert_score.BERTScorer
  
  								if version.parse(bert_score.__version__					) >= version.parse('''0.3.10'''					):
  													SCREAMING_SNAKE_CASE_	      =       functools.partial(lowerCAmelCase_							,							use_fast_tokenizer=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       functools.partial(lowerCAmelCase_							,							use_fast_tokenizer=lowerCAmelCase_					)
  								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"
  													SCREAMING_SNAKE_CASE_	      =       bert_score.utils.langamodel[lang.lower()]
  
  								if num_layers is None:
  													SCREAMING_SNAKE_CASE_	      =       bert_score.utils.modelalayers[model_type]
  
  								SCREAMING_SNAKE_CASE_	      =       get_hash(
  								    model=lowerCAmelCase_							,							num_layers=lowerCAmelCase_							,							idf=lowerCAmelCase_							,							rescale_with_baseline=lowerCAmelCase_							,							use_custom_baseline=baseline_path is not None							,							)
  
  								with filter_logging_context():
  													if not hasattr(self							,							'''cached_bertscorer'''					) or self.cached_bertscorer.hash != hashcode:
  																		SCREAMING_SNAKE_CASE_	      =       scorer(
  																		    model_type=lowerCAmelCase_							,							num_layers=lowerCAmelCase_							,							batch_size=lowerCAmelCase_							,							nthreads=lowerCAmelCase_							,							all_layers=lowerCAmelCase_							,							idf=lowerCAmelCase_							,							device=lowerCAmelCase_							,							lang=lowerCAmelCase_							,							rescale_with_baseline=lowerCAmelCase_							,							baseline_path=lowerCAmelCase_							,							)
  
  								((SCREAMING_SNAKE_CASE_)      ,(SCREAMING_SNAKE_CASE_)      ,(SCREAMING_SNAKE_CASE_))	      =       self.cached_bertscorer.score(
  								    cands=lowerCAmelCase_							,							refs=lowerCAmelCase_							,							verbose=lowerCAmelCase_							,							batch_size=lowerCAmelCase_							,							)
  								SCREAMING_SNAKE_CASE_	      =       {
  								    '''precision''': P.tolist(),
  								    '''recall''': R.tolist(),
  								    '''f1''': F.tolist(),
  								    '''hashcode''': hashcode,
  								}
  								return output_dict
  
  			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     str=None							,							lowerCAmelCase_     :     Tuple=None							,							**lowerCAmelCase_     :     List[Any]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if references is not None:
  													SCREAMING_SNAKE_CASE_	      =       [[ref] if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					) else ref for ref in references]
  								super().add_batch(predictions=lowerCAmelCase_							,							references=lowerCAmelCase_							,							**lowerCAmelCase_					)
  
  
  
  
  			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Optional[int]=None							,							**lowerCAmelCase_     :     Union[str, Any]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [reference]
  								super().add(prediction=lowerCAmelCase_							,							reference=lowerCAmelCase_							,							**lowerCAmelCase_					)
  
  
  
style_context: 
  
  
  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_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  