"""simple docstring"""


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


UpperCAmelCase__			=    datasets.logging.get_logger(__name__)


UpperCAmelCase__			=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

UpperCAmelCase__			=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

UpperCAmelCase__			=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

UpperCAmelCase__			=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION )
class 							a					(		datasets.Metric ):


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      return datasets.MetricInfo(
          description=_DESCRIPTION ,					citation=_CITATION ,					homepage="""https://github.com/google-research/bleurt""" ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string""" ,					id="""sequence"""       ),
                  """references""": datasets.Value("""string""" ,					id="""sequence"""       ),
              }       ) ,					codebase_urls=["""https://github.com/google-research/bleurt"""] ,					reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] ,					)


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str       ):
      # check that config name specifies a valid BLEURT model
      if self.config_name == "default":
        logger.warning(
            """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
            """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""       )
        _UpperCAmelCase    			=   """bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
        _UpperCAmelCase    			=   self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
        _UpperCAmelCase    			=   self.config_name.upper()

      else:
        raise KeyError(
            f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''       )

      # download the model checkpoint specified by self.config_name and set up the scorer
      _UpperCAmelCase    			=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
      _UpperCAmelCase    			=   score.BleurtScorer(os.path.join(__lowerCAmelCase ,					__lowerCAmelCase       )       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.scorer.score(references=__lowerCAmelCase ,					candidates=__lowerCAmelCase       )
      return {"scores": scores}

"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
    print(F'''Perimeter {solution()} has maximum solutions''')
