





import comet  # From: unbabel-comet
import torch

import datasets


_lowerCamelCase      							=   datasets.logging.get_logger(__name__)

_lowerCamelCase      							=   '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

_lowerCamelCase      							=   '\\nCrosslingual 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).\nWith 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.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

_lowerCamelCase      							=   '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION	)
class       __A							(   datasets.Metric	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """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 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if self.config_name == "default":
                    _lowerCamelCase				:  List[Any]						=		comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''))
             else:
                    _lowerCamelCase				:  int						=		comet.load_from_checkpoint(comet.download_model(self.config_name))



      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__=None   ,				a__=False):



             """simple docstring"""





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








import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   '▁'

_lowerCamelCase      							=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

_lowerCamelCase      							=   {
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

_lowerCamelCase      							=   {
    'facebook/s2t-small-librispeech-asr': 1024,
}

_lowerCamelCase      							=   ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

_lowerCamelCase      							=   {'mustc': MUSTC_LANGS}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   MAX_MODEL_INPUT_SIZES
      UpperCAmelCase__							=   ["""input_ids""", """attention_mask"""]

      UpperCAmelCase__							=   []


      def __init__(		self   ,				a__   ,				a__   ,				a__="<s>"   ,				a__="</s>"   ,				a__="<pad>"   ,				a__="<unk>"   ,				a__=False   ,				a__=False   ,				a__=None   ,				a__=None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=a__   ,				eos_token=a__   ,				unk_token=a__   ,				pad_token=a__   ,				do_upper_case=a__   ,				do_lower_case=a__   ,				tgt_lang=a__   ,				lang_codes=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		do_upper_case
             _lowerCamelCase				:  Optional[Any]						=		do_lower_case

             _lowerCamelCase				:  Tuple						=		load_json(a__)
             _lowerCamelCase				:  Union[str, Any]						=		{v: k for k, v in self.encoder.items()}
             _lowerCamelCase				:  Tuple						=		spm_file
             _lowerCamelCase				:  Any						=		load_spm(a__   ,				self.sp_model_kwargs)

             if lang_codes is not None:
                    _lowerCamelCase				:  List[Any]						=		lang_codes
                    _lowerCamelCase				:  List[str]						=		LANGUAGES[lang_codes]
                    _lowerCamelCase				:  Any						=		[F"""<lang:{lang}>""" for lang in self.langs]
                    _lowerCamelCase				:  Optional[Any]						=		{lang: self.sp_model.PieceToId(F"""<lang:{lang}>""") for lang in self.langs}

                    _lowerCamelCase				:  List[str]						=		self.lang_tokens
                    _lowerCamelCase				:  str						=		tgt_lang if tgt_lang is not None else self.langs[0]

                    self.set_tgt_lang_special_tokens(self._tgt_lang)
             else:
                    _lowerCamelCase				:  Any						=		{}


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.encoder)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._tgt_lang


      @tgt_lang.setter
      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		new_tgt_lang
             self.set_tgt_lang_special_tokens(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.lang_code_to_id[tgt_lang]
             _lowerCamelCase				:  Any						=		[lang_code_id]


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.encode(a__   ,				out_type=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.encoder.get(a__   ,				self.encoder[self.unk_token])


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.decoder.get(a__   ,				self.unk_token)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		[]
             _lowerCamelCase				:  List[str]						=		''''''
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           _lowerCamelCase				:  List[Any]						=		self.sp_model.decode(a__)
                           out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                           _lowerCamelCase				:  Optional[int]						=		[]
                    else:
                           current_sub_tokens.append(a__)
             _lowerCamelCase				:  Tuple						=		self.sp_model.decode(a__)
             out_string += decoded.upper() if self.do_upper_case else decoded
             return out_string.strip()


      def 					__snake_case      (		self   ,				a__   ,				a__=None):



             """simple docstring"""





             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = False):



             """simple docstring"""





             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a__   ,				token_ids_a=a__   ,				already_has_special_tokens=a__)

             _lowerCamelCase				:  Tuple						=		[1] * len(self.prefix_tokens)
             _lowerCamelCase				:  Tuple						=		[1]
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(a__)) + suffix_ones
             return prefix_ones + ([0] * len(a__)) + ([0] * len(a__)) + suffix_ones


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.encoder.copy()
             vocab.update(self.added_tokens_encoder)
             return vocab


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.__dict__.copy()
             _lowerCamelCase				:  str						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  List[Any]						=		{}

             _lowerCamelCase				:  Dict						=		load_spm(self.spm_file   ,				self.sp_model_kwargs)



      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  str						=		Path(a__)
             assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
             _lowerCamelCase				:  Any						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
             )
             _lowerCamelCase				:  Optional[Any]						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
             )

             save_json(self.encoder   ,				a__)

             if os.path.abspath(self.spm_file) != os.path.abspath(a__) and os.path.isfile(self.spm_file):
                    copyfile(self.spm_file   ,				a__)
             elif not os.path.isfile(self.spm_file):
                    with open(a__   ,				'''wb''') as fi:
                           _lowerCamelCase				:  List[str]						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (str(a__), str(a__))




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[Any]						=		sentencepiece.SentencePieceProcessor(**lowercase_   )
       spm.Load(str(lowercase_   )   )
       return spm




def  __UpperCAmelCase(   lowercase_   ):
       with open(lowercase_			,     '''r'''   ) as f:
              return json.load(lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       with open(lowercase_			,     '''w'''   ) as f:
              json.dump(lowercase_			,     lowercase_			,     indent=2   )

