






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def      _SCREAMING_SNAKE_CASE  (	)  -> List[str]:





    """simple docstring"""

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

    __A             =		datasets.utils.logging.get_logger("""transformers.modeling_utils"""      )
    logger.addFilter(__lowercase      )
    try:
        yield
    finally:
        logger.removeFilter(__lowercase      )


__a :    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"

__a :    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"

__a :    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  __lowercase (		datasets.Metric					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Any			):


         """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    :     str	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Optional[Any]=None	,		UpperCamelCase_    :     Optional[Any]=None	,		UpperCamelCase_    :     Optional[Any]=None	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Optional[int]=False	,		UpperCamelCase_    :     List[str]=None	,		UpperCamelCase_    :     Optional[int]=64	,		UpperCamelCase_    :     Tuple=4	,		UpperCamelCase_    :     Optional[Any]=False	,		UpperCamelCase_    :     List[str]=False	,		UpperCamelCase_    :     Union[str, Any]=None	,		UpperCamelCase_    :     int=False	,		):


         """simple docstring"""



         __A             =		bert_score.utils.get_hash
         __A             =		bert_score.BERTScorer

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

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

         __A             =		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:
                 __A             =		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_	,		)

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




     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     Union[str, Any]=None	,		UpperCamelCase_    :     List[Any]=None	,		**UpperCamelCase_    :     List[str]			):


         """simple docstring"""



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





     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Dict=None	,		UpperCamelCase_    :     Any=None	,		**UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




