





import comet  # From: unbabel-comet
import torch

import datasets


__lowerCamelCase	:					Union[str, Any]			  =					datasets.logging.get_logger(__name__)

__lowerCamelCase	:					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\",
}
"""

__lowerCamelCase	:					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.
"""

__lowerCamelCase	:					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  A__       (	datasets.Metric      ):





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       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 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.config_name == "default":
        UpperCamelCase     : Union[str, Any]	     =       comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"       )       )
       else:
        UpperCamelCase     : Optional[Any]	     =       comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_=None	,							A_=False       ):
       '''simple docstring'''




       if gpus is None:
        UpperCamelCase     : Union[str, Any]	     =       1 if torch.cuda.is_available() else 0
       UpperCamelCase     : Tuple	     =       {"src": sources, "mt": predictions, "ref": references}
       UpperCamelCase     : Dict	     =       [dict(zip(A_	,							A_       )       ) for t in zip(*data.values()       )]
       UpperCamelCase     ,   UpperCamelCase     : Any	     =       self.scorer.predict(A_	,							gpus=A_	,							progress_bar=A_       )
       return {"mean_score": mean_score, "scores": scores}







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
