


"""simple docstring"""





import comet  # From: unbabel-comet
import torch

import datasets


snake_case__						:   Tuple			 = datasets.logging.get_logger(__name__)

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",
}
'''

snake_case__						:   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.
'''

snake_case__						:   Dict			 = '''
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 						snake_case_(				datasets.Metric	):






     def    lowerCamelCase__   (		self     :    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    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    Tuple ):
           if self.config_name == "default":
                 lowerCAmelCase    :		Tuple				=      comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
           else:
                 lowerCAmelCase    :		Tuple				=      comet.load_from_checkpoint(comet.download_model(self.config_name ) )






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







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






    def __init__(       self		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


