"""simple docstring"""





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def     a_					(				):





       '''simple docstring'''

       def filter_log(_lowerCAmelCase  :			Dict					):
              return False if "This IS expected if you are initializing" in record.msg else True

       lowercase__      :		Optional[int]          =     datasets.utils.logging.get_logger('transformers.modeling_utils'					)
       logger.addFilter(__lowercase					)
       try:
              yield
       finally:
              logger.removeFilter(__lowercase					)


_UpperCamelCase       :						List[Any]     =   "\\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"

_UpperCamelCase       :						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"

_UpperCamelCase       :						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):


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             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		,      a		,      a		,      a=None		,      a=None		,      a=None		,      a=False		,      a=False		,      a=None		,      a=6_4		,      a=4		,      a=False		,      a=False		,      a=None		,      a=False		,      )	-> Tuple:
             lowercase__      :		Optional[Any]          =     bert_score.utils.get_hash
             lowercase__      :		Any          =     bert_score.BERTScorer

             if version.parse(bert_score.__version__							) >= version.parse('0.3.10'							):
                    lowercase__      :		Tuple          =     functools.partial(UpperCamelCase_		,      use_fast_tokenizer=UpperCamelCase_							)
                    lowercase__      :		Dict          =     functools.partial(UpperCamelCase_		,      use_fast_tokenizer=UpperCamelCase_							)
             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"
                    lowercase__      :		str          =     bert_score.utils.langamodel[lang.lower()]

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

             lowercase__      :		int          =     get_hash(
                 model=UpperCamelCase_		,      num_layers=UpperCamelCase_		,      idf=UpperCamelCase_		,      rescale_with_baseline=UpperCamelCase_		,      use_custom_baseline=baseline_path is not None		,      )

             with filter_logging_context():
                    if not hasattr(self		,      'cached_bertscorer'							) or self.cached_bertscorer.hash != hashcode:
                           lowercase__      :		str          =     scorer(
                               model_type=UpperCamelCase_		,      num_layers=UpperCamelCase_		,      batch_size=UpperCamelCase_		,      nthreads=UpperCamelCase_		,      all_layers=UpperCamelCase_		,      idf=UpperCamelCase_		,      device=UpperCamelCase_		,      lang=UpperCamelCase_		,      rescale_with_baseline=UpperCamelCase_		,      baseline_path=UpperCamelCase_		,      )

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


      def 		_UpperCAmelCase	(       self		,      a=None		,      a=None		,      **a							)	-> Any:



             if references is not None:
                    lowercase__      :		str          =     [[ref] if isinstance(UpperCamelCase_		,      UpperCamelCase_							) else ref for ref in references]
             super().add_batch(predictions=UpperCamelCase_		,      references=UpperCamelCase_		,      **UpperCamelCase_							)


      def 		_UpperCAmelCase	(       self		,      a=None		,      a=None		,      **a							)	-> Any:



             if isinstance(UpperCamelCase_		,      UpperCamelCase_							):
                    lowercase__      :		List[Any]          =     [reference]
             super().add(prediction=UpperCamelCase_		,      reference=UpperCamelCase_		,      **UpperCamelCase_							)


"""simple docstring"""





from ..utils import DummyObject, requires_backends


class 					UpperCAmelCase_     (   metaclass=_a):
      lowerCamelCase__		:   List[str]			  =  ["torch", "transformers", "k_diffusion"]


      def __init__(       self		,      *a		,      **a							)	-> Dict:
             requires_backends(self		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Tuple:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Any:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)

