




'''simple docstring'''





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					c_float



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return sizeof(self.value )






def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_ = 0						):
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        _a				:							List[Any]							       =      fixed_dimension

    dimension -= num_token_to_add
    return dimension





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    return num_parameters * dtype.size





def UpperCAmelCase    (       UpperCAmelCase_						):
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(UpperCAmelCase_						)
    except (ValueError, OSError, KeyError):
        _a       , _a				:							Optional[int]							       =      None, None
        try:
            _a				:							int							       =      AutoTokenizer.from_pretrained(UpperCAmelCase_						)
        except (OSError, KeyError):
            pass
        try:
            _a				:							List[Any]							       =      AutoFeatureExtractor.from_pretrained(UpperCAmelCase_						)
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''						)
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor








'''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 )


