






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_camembert import CamembertTokenizer
else:
	a__       =     None


a__       =     logging.get_logger(__name__)

a__       =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a__       =     {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

a__       =     {
    """camembert-base""": 5_1_2,
}

a__       =     """▁"""


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   int      =  VOCAB_FILES_NAMES
				_lowercase				:   str      =  PRETRAINED_VOCAB_FILES_MAP
				_lowercase				:   Any      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase				:   Optional[Any]      =  ['''input_ids''', '''attention_mask''']
				_lowercase				:   str      =  CamembertTokenizer



				def __init__(      self    :    List[Any]			,		UpperCamelCase__    :    Union[str, Any]=None			,		UpperCamelCase__    :    Optional[Any]=None			,		UpperCamelCase__    :    Union[str, Any]="<s>"			,		UpperCamelCase__    :    Dict="</s>"			,		UpperCamelCase__    :    Optional[int]="</s>"			,		UpperCamelCase__    :    str="<s>"			,		UpperCamelCase__    :    List[Any]="<unk>"			,		UpperCamelCase__    :    Any="<pad>"			,		UpperCamelCase__    :    List[Any]="<mask>"			,		UpperCamelCase__    :    Dict=["<s>NOTUSED", "</s>NOTUSED"]			,		**UpperCamelCase__    :    Dict			,		):






											'''simple docstring'''







											snake_case__	  =       AddedToken(UpperCamelCase__			,		lstrip=UpperCamelCase__			,		rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__			,		UpperCamelCase__) else mask_token

											super().__init__(
											    UpperCamelCase__			,		tokenizer_file=UpperCamelCase__			,		bos_token=UpperCamelCase__			,		eos_token=UpperCamelCase__			,		sep_token=UpperCamelCase__			,		cls_token=UpperCamelCase__			,		unk_token=UpperCamelCase__			,		pad_token=UpperCamelCase__			,		mask_token=UpperCamelCase__			,		additional_special_tokens=UpperCamelCase__			,		**UpperCamelCase__			,		)

											snake_case__	  =       vocab_file
											snake_case__	  =       False if not self.vocab_file else True



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None):






											'''simple docstring'''







											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											snake_case__	  =       [self.cls_token_id]
											snake_case__	  =       [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None):






											'''simple docstring'''







											snake_case__	  =       [self.sep_token_id]
											snake_case__	  =       [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																		    """tokenizer.""")

											if not os.path.isdir(UpperCamelCase__):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
																		return
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
																		copyfile(self.vocab_file			,		UpperCamelCase__)

											return (out_vocab_file,)








import comet  # From: unbabel-comet
import torch

import datasets


a__       =     datasets.logging.get_logger(__name__)

a__       =     """\
@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\",
}
"""

a__       =     """\
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.
"""

a__       =     """
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    _lowerCAmelCase			(		datasets.Metric      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Optional[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 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											if self.config_name == "default":
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""))
											else:
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model(self.config_name))



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    List[Any]=False):






											'''simple docstring'''







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