

import argparse
import os
import re


_SCREAMING_SNAKE_CASE					:  str             =      '''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_SCREAMING_SNAKE_CASE					:  str             =      re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
_SCREAMING_SNAKE_CASE					:  Tuple             =      re.compile(r'''\s*\(\s*"(\S[^"]+)"''')
def     UpperCAmelCase_       (						_A				,		_A = False    ):





     '''simple docstring'''




     with open(_A				,		'''r'''				,		encoding='''utf-8'''    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.read()

     SCREAMING_SNAKE_CASE__   =   content.split('''\n'''    )
     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   0
     while line_idx < len(_A    ):
          if _re_intro_mapping.search(lines[line_idx]    ) is not None:
               SCREAMING_SNAKE_CASE__   =   len(re.search(R'''^(\s*)\S'''				,		lines[line_idx]    ).groups()[0]    ) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(''' ''' * indent + '''('''    ):
                    new_lines.append(lines[line_idx]    )
                    line_idx += 1

               SCREAMING_SNAKE_CASE__   =   []
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         SCREAMING_SNAKE_CASE__   =   line_idx
                         while not lines[line_idx].startswith(''' ''' * indent + ''')'''    ):
                              line_idx += 1
                         blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]    )    )
                    else:
                         blocks.append(lines[line_idx]    )
                    line_idx += 1

               # Sort blocks by their identifiers
               SCREAMING_SNAKE_CASE__   =   sorted(_A				,		key=lambda _A    : _re_identifier.search(_A    ).groups()[0]    )
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]    )
               line_idx += 1

     if overwrite:
          with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
               f.write('''\n'''.join(_A    )    )
     elif "\n".join(_A    ) != content:
          return True
def     UpperCAmelCase_       (						_A = False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [os.path.join(_A				,		_A    ) for f in os.listdir(_A    ) if f.endswith('''.py'''    )]
     SCREAMING_SNAKE_CASE__   =   [sort_auto_mapping(_A				,		overwrite=_A    ) for fname in fnames]

     if not overwrite and any(_A    ):
          SCREAMING_SNAKE_CASE__   =   [f for f, d in zip(_A				,		_A    ) if d]
          raise ValueError(
              F'''The following files have auto mappings that need sorting: {", ".join(_A    )}. Run `make style` to fix'''
              ''' this.'''    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  int             =      argparse.ArgumentParser()
       parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       sort_all_auto_mappings(not args.check_only)





import comet  # From: unbabel-comet
import torch

import datasets


_SCREAMING_SNAKE_CASE					:  List[str]             =      datasets.logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Any             =      '''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''






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






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://unbabel.github.io/COMET/html/index.html'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''sources''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''predictions''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''references''': datasets.Value('''string'''     ,       id='''sequence''' ),
               } )     ,       codebase_urls=['''https://github.com/Unbabel/COMET''']     ,       reference_urls=[
               '''https://github.com/Unbabel/COMET''',
               '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
               '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
           ]     ,       )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Tuple:
       if self.config_name == "default":
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
       else:
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model(self.config_name ) )







  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict=None     ,       __lowerCamelCase						:		Optional[int]=False )		->							str:
       if gpus is None:
            SCREAMING_SNAKE_CASE__   =   1 if torch.cuda.is_available() else 0
       SCREAMING_SNAKE_CASE__   =   {'''src''': sources, '''mt''': predictions, '''ref''': references}
       SCREAMING_SNAKE_CASE__   =   [dict(zip(__lowerCamelCase     ,       __lowerCamelCase ) ) for t in zip(*data.values() )]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.scorer.predict(__lowerCamelCase     ,       gpus=__lowerCamelCase     ,       progress_bar=__lowerCamelCase )
       return {"mean_score": mean_score, "scores": scores}


