






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 						_A     (       ):
    def filter_log(SCREAMING_SNAKE_CASE__						:			Union[str, Any] ):
        return False if "This IS expected if you are initializing" in record.msg else True

    UpperCamelCase :str										=     datasets.utils.logging.get_logger('''transformers.modeling_utils''' )
    logger.addFilter(SCREAMING_SNAKE_CASE__ )
    try:
        yield
    finally:
        logger.removeFilter(SCREAMING_SNAKE_CASE__ )


__snake_case							      =						"""\
@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}
}
"""

__snake_case							      =						"""\
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.
"""

__snake_case							      =						"""
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     UpperCAmelCase_		(      datasets.Metric				):



      """simple docstring"""
      def 					UpperCAmelCase		(       self				)       ->   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 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=64					,       SCREAMING_SNAKE_CASE_=4					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=False					,       )       ->   Optional[int]:
          UpperCamelCase :Dict										=     bert_score.utils.get_hash
          UpperCamelCase :Union[str, Any]										=     bert_score.BERTScorer

          if version.parse(bert_score.__version__				) >= version.parse('''0.3.10'''				):
              UpperCamelCase :Tuple										=     functools.partial(SCREAMING_SNAKE_CASE_					,       use_fast_tokenizer=SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Union[str, Any]										=     functools.partial(SCREAMING_SNAKE_CASE_					,       use_fast_tokenizer=SCREAMING_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"
              UpperCamelCase :Union[str, Any]										=     bert_score.utils.langamodel[lang.lower()]

          if num_layers is None:
              UpperCamelCase :List[Any]										=     bert_score.utils.modelalayers[model_type]

          UpperCamelCase :str										=     get_hash(
              model=SCREAMING_SNAKE_CASE_					,       num_layers=SCREAMING_SNAKE_CASE_					,       idf=SCREAMING_SNAKE_CASE_					,       rescale_with_baseline=SCREAMING_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:
                  UpperCamelCase :Optional[int]										=     scorer(
                      model_type=SCREAMING_SNAKE_CASE_					,       num_layers=SCREAMING_SNAKE_CASE_					,       batch_size=SCREAMING_SNAKE_CASE_					,       nthreads=SCREAMING_SNAKE_CASE_					,       all_layers=SCREAMING_SNAKE_CASE_					,       idf=SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_					,       lang=SCREAMING_SNAKE_CASE_					,       rescale_with_baseline=SCREAMING_SNAKE_CASE_					,       baseline_path=SCREAMING_SNAKE_CASE_					,       )

          ((UpperCamelCase)							,     (UpperCamelCase)							,     (UpperCamelCase)) :int										=     self.cached_bertscorer.score(
              cands=SCREAMING_SNAKE_CASE_					,       refs=SCREAMING_SNAKE_CASE_					,       verbose=SCREAMING_SNAKE_CASE_					,       batch_size=SCREAMING_SNAKE_CASE_					,       )
          UpperCamelCase :Optional[Any]										=     {
              '''precision''': P.tolist(),
              '''recall''': R.tolist(),
              '''f1''': F.tolist(),
              '''hashcode''': hashcode,
          }
          return output_dict
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
          # References can be strings or lists of strings
          # Let's change strings to lists of strings with one element
          if references is not None:
              UpperCamelCase :Optional[Any]										=     [[ref] if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) else ref for ref in references]
          super().add_batch(predictions=SCREAMING_SNAKE_CASE_					,       references=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)




      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   int:
          # References can be strings or lists of strings
          # Let's change strings to lists of strings with one element
          if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
              UpperCamelCase :List[str]										=     [reference]
          super().add(prediction=SCREAMING_SNAKE_CASE_					,       reference=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)













from __future__ import annotations


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
    UpperCamelCase :int										=     {1}
    UpperCamelCase :Optional[int]										=     [1]
    for _ in range(2  ,						depth + 1 ):
        UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
        UpperCamelCase :List[str]										=     [0, *previous_coefficients]
        UpperCamelCase :List[str]										=     []
        for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
            coefficients.add(x + y )
            previous_coefficients.append(x + y )
    return coefficients


def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
    UpperCamelCase :Union[str, Any]										=     set()
    for number in unique_coefficients:
        UpperCamelCase :Optional[int]										=     2
        UpperCamelCase :List[Any]										=     number
        while divisor**2 <= copy_number:
            UpperCamelCase :List[Any]										=     0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                break
            divisor += 1

    return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
    UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
    return sum(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
 print(f'''{solution() = }''')





