





import comet  # From: unbabel-comet
import torch

import datasets


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

A__:    int  	=	'''\
@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__:    List[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.
'''

A__:    Tuple  	=	'''
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):

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			int					):
          '''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:			Tuple	,    __lowerCamelCase:			Tuple					):
          '''simple docstring'''


          if self.config_name == "default":
              UpperCamelCase__:	List[str]					=     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:			Dict	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int=None	,    __lowerCamelCase:			int=False					):
          '''simple docstring'''


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








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A__:    List[Any]  	=	logging.get_logger(__name__)

A__:    str  	=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__:    List[Any]  	=	{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

A__:    str  	=	{
    '''junnyu/roformer_chinese_small''': 1536,
    '''junnyu/roformer_chinese_base''': 1536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


A__:    int  	=	{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       VOCAB_FILES_NAMES
      UpperCamelCase__  		=       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__  		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__  		=       PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__  		=       RoFormerTokenizer






      def __init__(		self:			int	,    __lowerCamelCase:			Optional[Any]=None	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			str=True	,    __lowerCamelCase:			Any="[UNK]"	,    __lowerCamelCase:			int="[SEP]"	,    __lowerCamelCase:			Optional[int]="[PAD]"	,    __lowerCamelCase:			Optional[int]="[CLS]"	,    __lowerCamelCase:			Tuple="[MASK]"	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			List[Any]=None	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(
              __lowerCamelCase	,    tokenizer_file=__lowerCamelCase	,    do_lower_case=__lowerCamelCase	,    unk_token=__lowerCamelCase	,    sep_token=__lowerCamelCase	,    pad_token=__lowerCamelCase	,    cls_token=__lowerCamelCase	,    mask_token=__lowerCamelCase	,    tokenize_chinese_chars=__lowerCamelCase	,    strip_accents=__lowerCamelCase	,    **__lowerCamelCase	,    )

          UpperCamelCase__:	int					=     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
          if (
              pre_tok_state.get("lowercase"	,    __lowerCamelCase					) != do_lower_case
              or pre_tok_state.get("strip_accents"	,    __lowerCamelCase					) != strip_accents
          ):
              UpperCamelCase__:	int					=     getattr(__lowerCamelCase	,    pre_tok_state.pop("type"					)					)
              UpperCamelCase__:	Any					=     do_lower_case
              UpperCamelCase__:	Optional[int]					=     strip_accents
              UpperCamelCase__:	Any					=     pre_tok_class(**__lowerCamelCase					)

          UpperCamelCase__:	Tuple					=     do_lower_case






      def __getstate__(		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     self.__dict__.copy()
          UpperCamelCase__:	Dict					=     BertPreTokenizer()
          return state






      def __setstate__(		self:			Dict	,    __lowerCamelCase:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     d
          UpperCamelCase__:	List[Any]					=     self.__dict__["_tokenizer"].get_vocab()
          UpperCamelCase__:	str					=     PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase					)					)






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			Dict=None					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     [self.sep_token_id]
          UpperCamelCase__:	str					=     [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					) * [0] + len(token_ids_a + sep					) * [1]






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[str] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self._tokenizer.model.save(__lowerCamelCase	,    name=__lowerCamelCase					)
          return tuple(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			List[str]=False	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     BertPreTokenizer()
          return super().save_pretrained(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    **__lowerCamelCase					)

