





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class 				_UpperCamelCase       (						enum.Enum  ):
       _UpperCamelCase					:       Tuple       = '''timm'''
       _UpperCamelCase					:       Union[str, Any]       = '''transformers'''




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"	)

       if out_features is not None:
              if not isinstance(UpperCamelCase_       ,		(list,)	):
                     raise ValueError(F'''out_features must be a list {type(UpperCamelCase_	)}'''	)
              if any(feat not in stage_names for feat in out_features	):
                     raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''	)

       if out_indices is not None:
              if not isinstance(UpperCamelCase_       ,		(list, tuple)	):
                     raise ValueError(F'''out_indices must be a list or tuple, got {type(UpperCamelCase_	)}'''	)
              if any(idx >= len(UpperCamelCase_	) for idx in out_indices	):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"	)

       if out_features is not None and out_indices is not None:
              if len(UpperCamelCase_	) != len(UpperCamelCase_	):
                     raise ValueError("out_features and out_indices should have the same length if both are set"	)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		int:
       if out_indices is None and out_features is None:
              UpperCamelCase_ 							=	[len(UpperCamelCase_	) - 1]
              UpperCamelCase_ 							=	[stage_names[-1]]
       elif out_indices is None and out_features is not None:
              UpperCamelCase_ 							=	[stage_names.index(UpperCamelCase_	) for layer in out_features]
       elif out_features is None and out_indices is not None:
              UpperCamelCase_ 							=	[stage_names[idx] for idx in out_indices]
       return out_features, out_indices




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	_align_output_features_output_indices(
           out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       return output_features, output_indices



class 				_UpperCamelCase       :
       _UpperCamelCase					:       Optional[BackboneType]       = None

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Dict		)							->			None:




              """simple docstring"""


              if getattr(self       , "_backbone"       , _SCREAMING_SNAKE_CASE		) is None:
                     raise ValueError("self._backbone must be set before calling _init_timm_backbone"		)

              # These will diagree with the defaults for the transformers models e.g. for resnet50
              # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
              # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
              UpperCamelCase_ 							=	[stage["module"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	[stage["num_chs"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	self._backbone.feature_info.out_indices
              UpperCamelCase_ 							=	self._backbone.feature_info.module_name()

              # We verify the out indices and out features are valid
              verify_out_features_out_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	out_features, out_indices

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "stage_names"		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_features"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_indices"       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	stage_names
              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=_SCREAMING_SNAKE_CASE		)
              # Number of channels for each stage. This is set in the transformer backbone model init
              UpperCamelCase_ 							=	None

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	config

              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "use_timm_backbone"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

              if self.backbone_type == BackboneType.TIMM:
                     self._init_timm_backbone(_SCREAMING_SNAKE_CASE		)
              elif self.backbone_type == BackboneType.TRANSFORMERS:
                     self._init_transformers_backbone(_SCREAMING_SNAKE_CASE		)
              else:
                     raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''		)

       @property
       def lowercase			(		self:			str		)							->			List[str]:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			List[str]		)							->			str:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names		)}

       @property
       def lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              return [self.out_feature_channels[name] for name in self.out_features]

       def lowercase			(		self:			Optional[int]       , *_SCREAMING_SNAKE_CASE:			Dict       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(inspect.signature(self.forward		).parameters		)
              UpperCamelCase_ 							=	{k: v for k, v in kwargs.items() if k in signature}
              return self(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , )							->			List[Any]:




              """simple docstring"""


              raise NotImplementedError("This method should be implemented by the derived class."		)



       def lowercase			(		self:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output



class 				_UpperCamelCase       :

       @property
       def lowercase			(		self:			List[str]		)							->			Any:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Any		)							->			Dict:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)



       def lowercase			(		self:			Tuple		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output







'''simple docstring'''





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





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

    _a				:							Optional[Any]							       =      datasets.utils.logging.get_logger("""transformers.modeling_utils"""						)
    logger.addFilter(UpperCAmelCase_						)
    try:
        yield
    finally:
        logger.removeFilter(UpperCAmelCase_						)


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

a		:	List[str]       =      '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

a		:	Dict       =      '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''





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



  def     a_							(			self ):







      '''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		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=64		,     __lowerCAmelCase=4		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      bert_score.utils.get_hash
      _a				:							int							       =      bert_score.BERTScorer

      if version.parse(bert_score.__version__ ) >= version.parse("""0.3.10""" ):
          _a				:							Union[str, Any]							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
          _a				:							Dict							       =      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"
          _a				:							List[str]							       =      bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          _a				:							Dict							       =      bert_score.utils.modelalayers[model_type]

      _a				:							Union[str, Any]							       =      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:
              _a				:							Optional[Any]							       =      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		,     )

      ((_a)       , (_a)       , (_a))				:							Optional[Any]							       =      self.cached_bertscorer.score(
          cands=__lowerCAmelCase		,     refs=__lowerCAmelCase		,     verbose=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     )
      _a				:							Tuple							       =      {
          """precision""": P.tolist(),
          """recall""": R.tolist(),
          """f1""": F.tolist(),
          """hashcode""": hashcode,
      }
      return output_dict



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if references is not None:
          _a				:							Dict							       =      [[ref] if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else ref for ref in references]
      super().add_batch(predictions=__lowerCAmelCase		,     references=__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [reference]
      super().add(prediction=__lowerCAmelCase		,     reference=__lowerCAmelCase		,     **__lowerCAmelCase )


