





"""simple docstring"""
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def 						_SCREAMING_SNAKE_CASE       ():
 def filter_log(_UpperCAmelCase :  List[str]       ):
  return False if "This IS expected if you are initializing" in record.msg else True

 lowerCAmelCase  							=      datasets.utils.logging.get_logger('transformers.modeling_utils'       )
 logger.addFilter(_UpperCAmelCase       )
 try:
  yield
 finally:
  logger.removeFilter(_UpperCAmelCase       )


__UpperCamelCase			:		int		    =   '''\
@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}
}
'''

__UpperCamelCase			:		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.
'''

__UpperCamelCase			:		List[Any]		    =   '''
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 	a				(					datasets.Metric   ):



 def 					UpperCamelCase__   (			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 					UpperCamelCase__   (			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=64							,    _snake_case=4							,    _snake_case=False							,    _snake_case=False							,    _snake_case=None							,    _snake_case=False							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      bert_score.utils.get_hash
  lowerCAmelCase  							=      bert_score.BERTScorer

  if version.parse(bert_score.__version__			) >= version.parse('0.3.10'			):
   lowerCAmelCase  							=      functools.partial(_snake_case							,    use_fast_tokenizer=_snake_case			)
   lowerCAmelCase  							=      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"
   lowerCAmelCase  							=      bert_score.utils.langamodel[lang.lower()]

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

  lowerCAmelCase  							=      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:
    lowerCAmelCase  							=      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							,    )

  ((lowerCAmelCase)							,(lowerCAmelCase)							,(lowerCAmelCase))  							=      self.cached_bertscorer.score(
      cands=_snake_case							,    refs=_snake_case							,    verbose=_snake_case							,    batch_size=_snake_case							,    )
  lowerCAmelCase  							=      {
      'precision': P.tolist(),
      'recall': R.tolist(),
      'f1': F.tolist(),
      'hashcode': hashcode,
  }
  return output_dict



 def 					UpperCamelCase__   (			self							,    _snake_case=None							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






  if references is not None:
   lowerCAmelCase  							=      [[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 					UpperCamelCase__   (			self							,    _snake_case=None							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






  if isinstance(_snake_case							,    _snake_case			):
   lowerCAmelCase  							=      [reference]
  super().add(prediction=_snake_case							,    reference=_snake_case							,    **_snake_case			)












"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase			:		List[Any]		    =   {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase			:		Dict		    =   [
           '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''YosoForMaskedLM''',
           '''YosoForMultipleChoice''',
           '''YosoForQuestionAnswering''',
           '''YosoForSequenceClassification''',
           '''YosoForTokenClassification''',
           '''YosoLayer''',
           '''YosoModel''',
           '''YosoPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_yoso import (
                  YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  YosoForMaskedLM,
                  YosoForMultipleChoice,
                  YosoForQuestionAnswering,
                  YosoForSequenceClassification,
                  YosoForTokenClassification,
                  YosoLayer,
                  YosoModel,
                  YosoPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase			:		Any		    =   _LazyModule(__name__, globals()['''__file__'''], _import_structure)





