code: 
  
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  @contextmanager
  def lowercase						(     ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			def filter_log(SCREAMING_SNAKE_CASE   ):
  						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(SCREAMING_SNAKE_CASE   )
  			try:
  						yield
  			finally:
  						logger.removeFilter(SCREAMING_SNAKE_CASE   )
  
  
  SCREAMING_SNAKE_CASE__    :    int								=					"\\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"
  
  SCREAMING_SNAKE_CASE__    :    Optional[int]								=					"\\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"
  
  SCREAMING_SNAKE_CASE__    :    Any								=					"\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 			a_					(    datasets.Metric    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				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 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=64			,    SCREAMING_SNAKE_CASE=4			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=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 	A_(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			)     ->				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 	A_(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			)     ->				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_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  