import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def     lowerCamelCase_ (			):







				'''simple docstring'''







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

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


lowercase 							=							"""\
@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}
}
"""

lowercase 							=							"""\
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.
"""

lowercase 							=							"""
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      __lowercase  (       datasets.Metric    ):




				'''simple docstring'''


				def 			A_				(						self  :						Any							):
								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  :						Any	,				_a  :						int	,				_a  :						Union[str, Any]	,				_a  :						List[str]=None	,				_a  :						str=None	,				_a  :						List[str]=None	,				_a  :						Optional[int]=False	,				_a  :						Optional[int]=False	,				_a  :						List[Any]=None	,				_a  :						str=64	,				_a  :						int=4	,				_a  :						Dict=False	,				_a  :						List[Any]=False	,				_a  :						str=None	,				_a  :						List[str]=False	,				):
								UpperCamelCase__       						=							bert_score.utils.get_hash
								UpperCamelCase__       						=							bert_score.BERTScorer

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

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

								UpperCamelCase__       						=							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:
																UpperCamelCase__       						=							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__	,				)

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





				def 			A_				(						self  :						Any	,				_a  :						Dict=None	,				_a  :						Dict=None	,				**_a  :						int							):



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





				def 			A_				(						self  :						Optional[int]	,				_a  :						Union[str, Any]=None	,				_a  :						List[str]=None	,				**_a  :						List[str]							):



								if isinstance(lowerCamelCase__	,				lowerCamelCase__							):
												UpperCamelCase__       						=							[reference]
								super().add(prediction=lowerCamelCase__	,				reference=lowerCamelCase__	,				**lowerCamelCase__							)









'''simple docstring'''
def 				snake_case_  (   a__		:	int			):



    """simple docstring"""




    if not isinstance(a__    ,a__			) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""			)

    __lowercase							=						number & ~(number - 1)
    __lowercase							=						0
    while intermediate:
        intermediate >>= 1
        index += 1
    return index - 1


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)




