





"""simple docstring"""




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 						UpperCAmelCase__		(					):






  """simple docstring"""





  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 )


lowerCamelCase_					:						Optional[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'

lowerCamelCase_					:						Union[str, Any]      = '\\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'

lowerCamelCase_					:						List[str]      = '\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 			_UpperCAmelCase  (       datasets.Metric		):



      '''simple docstring'''

      def  lowerCamelCase_      (					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  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=False ,				snake_case_=False ,				snake_case_=None ,				snake_case_=6_4 ,				snake_case_=4 ,				snake_case_=False ,				snake_case_=False ,				snake_case_=None ,				snake_case_=False ,				):

        """simple docstring"""

        A_  :	List[Any]    						=  bert_score.utils.get_hash
        A_  :	int    						=  bert_score.BERTScorer

        if version.parse(bert_score.__version__  ) >= version.parse('0.3.10'  ):
          A_  :	Tuple    						=  functools.partial(snake_case_ ,				use_fast_tokenizer=snake_case_  )
          A_  :	Dict    						=  functools.partial(snake_case_ ,				use_fast_tokenizer=snake_case_  )
        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_  :	Dict    						=  bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
          A_  :	Union[str, Any]    						=  bert_score.utils.modelalayers[model_type]

        A_  :	Dict    						=  get_hash(
            model=snake_case_ ,				num_layers=snake_case_ ,				idf=snake_case_ ,				rescale_with_baseline=snake_case_ ,				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[int]    						=  scorer(
                model_type=snake_case_ ,				num_layers=snake_case_ ,				batch_size=snake_case_ ,				nthreads=snake_case_ ,				all_layers=snake_case_ ,				idf=snake_case_ ,				device=snake_case_ ,				lang=snake_case_ ,				rescale_with_baseline=snake_case_ ,				baseline_path=snake_case_ ,				)

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

      def  lowerCamelCase_      (					self ,				snake_case_=None ,				snake_case_=None ,				**snake_case_  ):

        """simple docstring"""

        if references is not None:
          A_  :	Optional[Any]    						=  [[ref] if isinstance(snake_case_ ,				snake_case_  ) else ref for ref in references]
        super().add_batch(predictions=snake_case_ ,				references=snake_case_ ,				**snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_=None ,				snake_case_=None ,				**snake_case_  ):

        """simple docstring"""

        if isinstance(snake_case_ ,				snake_case_  ):
          A_  :	str    						=  [reference]
        super().add(prediction=snake_case_ ,				reference=snake_case_ ,				**snake_case_  )






"""simple docstring"""




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if not inplace:
    A_  :	Optional[int]    						=  ma + ma
  else:
    ma += ma

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  -1 * len(_UpperCAmelCase )
  A_  :	List[str]    						=  list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  return t.reshape(t.shape[:-no_dims] + (-1,) )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





  A_  :	List[str]    						=  torch.linspace(_UpperCAmelCase			, _UpperCAmelCase			, no_bins - 1			, device=pts.device )
  A_  :	Optional[Any]    						=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2			, dim=-1 ) )
  return torch.bucketize(_UpperCAmelCase			, _UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  dicts[0]
  A_  :	int    						=  {}
  for k, v in first.items():
    A_  :	Dict    						=  [d[k] for d in dicts]
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	List[Any]    						=  dict_multimap(_UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Union[str, Any]    						=  fn(_UpperCAmelCase )

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  v_bins.view(((1,) * len(x.shape )) + (len(_UpperCAmelCase ),) )
  A_  :	Any    						=  x[..., None] - reshaped_bins
  A_  :	List[str]    						=  torch.argmin(torch.abs(_UpperCAmelCase )			, dim=-1 )
  return nn.functional.one_hot(_UpperCAmelCase			, num_classes=len(_UpperCAmelCase ) ).float()





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





  A_  :	List[Union[slice, torch.Tensor]]    						=  []
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    A_  :	List[Any]    						=  torch.arange(_UpperCAmelCase )
    A_  :	Dict    						=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(_UpperCAmelCase )

  A_  :	List[Union[slice, torch.Tensor]]    						=  [slice(_UpperCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
  A_  :	Optional[Any]    						=  inds
  ranges.extend(_UpperCAmelCase )
  # Matt note: Editing this to get around the behaviour of using a list as an array index changing
  # in recent Numpy versions
  return data[tuple(_UpperCAmelCase )]


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return [tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree]
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return tuple(tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return fn(_UpperCAmelCase )
  else:
    print(type(_UpperCAmelCase ) )
    raise ValueError('Not supported' )





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)