

from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Optional[int]             =      logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE					:  List[Any]             =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_SCREAMING_SNAKE_CASE					:  List[Any]             =      1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_SCREAMING_SNAKE_CASE					:  int             =      0

_SCREAMING_SNAKE_CASE					:  str             =      0xE_0_0_0
_SCREAMING_SNAKE_CASE					:  Optional[int]             =      0xE_0_0_1
_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      0xE_0_0_2
_SCREAMING_SNAKE_CASE					:  str             =      0xE_0_0_3
_SCREAMING_SNAKE_CASE					:  Optional[int]             =      0xE_0_0_4

# Maps special codepoints to human-readable names.
_SCREAMING_SNAKE_CASE					:  Dict[int, str]             =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_SCREAMING_SNAKE_CASE					:  Dict[str, int]             =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(							self						:		int     ,       __lowerCamelCase						:		str=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		Optional[Any]=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		List[Any]=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		Tuple=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		Optional[int]=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		Tuple=chr(__lowerCamelCase )     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Union[str, Any]=2048     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else bos_token
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else eos_token
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else sep_token
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else cls_token
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__   =   AddedToken(__lowerCamelCase     ,       lstrip=__lowerCamelCase     ,       rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,       __lowerCamelCase ) else mask_token

       super().__init__(
           bos_token=__lowerCamelCase     ,       eos_token=__lowerCamelCase     ,       sep_token=__lowerCamelCase     ,       cls_token=__lowerCamelCase     ,       pad_token=__lowerCamelCase     ,       mask_token=__lowerCamelCase     ,       add_prefix_space=__lowerCamelCase     ,       model_max_length=__lowerCamelCase     ,       **__lowerCamelCase     ,       )

       # Creates a mapping for looking up the IDs of special symbols.
       SCREAMING_SNAKE_CASE__   =   {}
       for codepoint, name in SPECIAL_CODEPOINTS.items():
            SCREAMING_SNAKE_CASE__   =   codepoint

       # Creates a mapping for looking up the string forms of special symbol IDs.
       SCREAMING_SNAKE_CASE__   =   {
           codepoint: name for name, codepoint in self._special_codepoints.items()
       }

       SCREAMING_SNAKE_CASE__   =   UNICODE_VOCAB_SIZE
       SCREAMING_SNAKE_CASE__   =   len(self._special_codepoints )






  @property
  def 				lowercase_		(							self						:		Dict )		->							int:
       return self._unicode_vocab_size






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		str )		->							List[str]:
       return list(__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		str )		->							int:
       try:
            return ord(__lowerCamelCase )
       except TypeError:
            raise ValueError(f'''invalid token: \'{token}\'''' )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		int )		->							str:
       try:
            if index in SPECIAL_CODEPOINTS:
                 return SPECIAL_CODEPOINTS[index]
            return chr(__lowerCamelCase )
       except TypeError:
            raise ValueError(f'''invalid id: {index}''' )






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Tuple )		->							int:
       return "".join(__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		Optional[List[int]] = None )		->							List[int]:
       SCREAMING_SNAKE_CASE__   =   [self.sep_token_id]
       SCREAMING_SNAKE_CASE__   =   [self.cls_token_id]

       SCREAMING_SNAKE_CASE__   =   cls + token_ids_a + sep
       if token_ids_a is not None:
            result += token_ids_a + sep
       return result






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		Optional[List[int]] = None     ,       __lowerCamelCase						:		bool = False )		->							List[int]:
       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowerCamelCase     ,       token_ids_a=__lowerCamelCase     ,       already_has_special_tokens=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   [1] + ([0] * len(__lowerCamelCase )) + [1]
       if token_ids_a is not None:
            result += ([0] * len(__lowerCamelCase )) + [1]
       return result






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		Optional[List[int]] = None )		->							List[int]:
       SCREAMING_SNAKE_CASE__   =   [self.sep_token_id]
       SCREAMING_SNAKE_CASE__   =   [self.cls_token_id]

       SCREAMING_SNAKE_CASE__   =   len(cls + token_ids_a + sep ) * [0]
       if token_ids_a is not None:
            result += len(token_ids_a + sep ) * [1]
       return result







  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Optional[str] = None )		->							Union[str, Any]:
       return ()





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}


