


'''simple docstring'''
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 				lowerCAmelCase__	(       ):






       """simple docstring"""





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

       snake_case__					:	Optional[Any]          =					datasets.utils.logging.get_logger("""transformers.modeling_utils"""     )
       logger.addFilter(UpperCAmelCase_     )
       try:
              yield
       finally:
              logger.removeFilter(UpperCAmelCase_     )


lowerCAmelCase__  =     '''\
@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}
}
'''

lowerCAmelCase__  =     '''\
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.
'''

lowerCAmelCase__  =     '''
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							):



   '''simple docstring'''






   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Optional[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   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Any=None					, lowerCamelCase	:					Optional[int]=None					, lowerCamelCase	:					List[str]=None					, lowerCamelCase	:					Union[str, Any]=False					, lowerCamelCase	:					Dict=False					, lowerCamelCase	:					List[Any]=None					, lowerCamelCase	:					Tuple=64					, lowerCamelCase	:					List[str]=4					, lowerCamelCase	:					Optional[Any]=False					, lowerCamelCase	:					Optional[Any]=False					, lowerCamelCase	:					Optional[int]=None					, lowerCamelCase	:					Union[str, Any]=False					, )->	int:
          snake_case__					:	Any          =					bert_score.utils.get_hash
          snake_case__					:	int          =					bert_score.BERTScorer

          if version.parse(bert_score.__version__						) >= version.parse("""0.3.10"""						):
                 snake_case__					:	Union[str, Any]          =					functools.partial(__lowerCAmelCase					, use_fast_tokenizer=__lowerCAmelCase						)
                 snake_case__					:	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"
                 snake_case__					:	List[str]          =					bert_score.utils.langamodel[lang.lower()]

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

          snake_case__					:	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:
                        snake_case__					:	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					, )

          (snake_case__)					:	Optional[Any]          =					self.cached_bertscorer.score(
              cands=__lowerCAmelCase					, refs=__lowerCAmelCase					, verbose=__lowerCAmelCase					, batch_size=__lowerCAmelCase					, )
          snake_case__					:	Tuple          =					{
              """precision""": P.tolist(),
              """recall""": R.tolist(),
              """f1""": F.tolist(),
              """hashcode""": hashcode,
          }
          return output_dict




   def   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					Tuple=None					, lowerCamelCase	:					List[str]=None					, **lowerCamelCase	:					Optional[Any]						)->	List[str]:


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




   def   __lowerCAmelCase						(	self	:					List[Any]					, lowerCamelCase	:					Union[str, Any]=None					, lowerCamelCase	:					Optional[Any]=None					, **lowerCamelCase	:					Any						)->	Tuple:


          if isinstance(__lowerCAmelCase					, __lowerCAmelCase						):
                 snake_case__					:	Optional[Any]          =					[reference]
          super().add(prediction=__lowerCAmelCase					, reference=__lowerCAmelCase					, **__lowerCAmelCase						)










'''simple docstring'''





from __future__ import annotations

# Define the Gravitational Constant G and the function
a		:	Union[str, Any]       =      6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							Dict							       =      mass_a * mass_a

    if (force, mass_a, mass_a, distance).count(0						) != 1:
        raise ValueError("""One and only one argument must be 0"""						)
    if force < 0:
        raise ValueError("""Gravitational force can not be negative"""						)
    if distance < 0:
        raise ValueError("""Distance can not be negative"""						)
    if mass_a < 0 or mass_a < 0:
        raise ValueError("""Mass can not be negative"""						)
    if force == 0:
        _a				:							Optional[int]							       =      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
        return {"force": force}
    elif mass_a == 0:
        _a				:							Any							       =      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_1": mass_a}
    elif mass_a == 0:
        _a				:							Union[str, Any]							       =      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_2": mass_a}
    elif distance == 0:
        _a				:							Optional[Any]							       =      (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
        return {"distance": distance}
    raise ValueError("""One and only one argument must be 0"""						)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()


