





import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE_		     = datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = """\
@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_		     = """\
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_		     = """
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        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''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        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Optional[int] )  ->	Any:




           '''simple docstring'''


           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        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					int=None		,lowerCamelCase__		:					List[str]=False )  ->	Any:




           '''simple docstring'''


           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}









import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      for line in lines:
            SCREAMING_SNAKE_CASE	=		re.sub(r"""#.*"""				,		""""""				,		_SCREAMING_SNAKE_CASE			)  # remove comments
            if line:
                  filtered_lines.append(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		"""\n""".join(_SCREAMING_SNAKE_CASE			)

      # Make a hash from all this code
      SCREAMING_SNAKE_CASE	=		full_str.encode("""utf-8"""			)
      return shaaaa(_SCREAMING_SNAKE_CASE			).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_		     = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_		     = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_		     = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_		     = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


