import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


UpperCamelCase__     :	int				=			'\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCamelCase__     :	Union[str, Any]				=			'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

UpperCamelCase__     :	List[str]				=			'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION   )
class 				_lowercase	(      datasets.Metric   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         if version.parse(scb.__version__							) < version.parse('''1.4.12'''							):
            raise ImportWarning(
                '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''							)
         return datasets.MetricInfo(
             description=_DESCRIPTION					,citation=_CITATION					,homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''					,inputs_description=_KWARGS_DESCRIPTION					,features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''					,id='''sequence'''							),
                     '''references''': datasets.Sequence(datasets.Value('''string'''					,id='''sequence'''							)					,id='''references'''							),
                 }							)					,codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']					,reference_urls=[
                 '''https://github.com/m-popovic/chrF''',
             ]					,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = CHRF.CHAR_ORDER					,lowerCamelCase_ = CHRF.WORD_ORDER					,lowerCamelCase_ = CHRF.BETA					,lowerCamelCase_ = False					,lowerCamelCase_ = False					,lowerCamelCase_ = False					,)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  len(references[0]							)
         if any(len(lowerCamelCase_							) != references_per_prediction for refs in references							):
            raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
         UpperCAmelCase__							:				Tuple   				=  [[refs[i] for refs in references] for i in range(lowerCamelCase_							)]

         UpperCAmelCase__							:				List[Any]   				=  CHRF(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  sb_chrf.corpus_score(lowerCamelCase_					,lowerCamelCase_							)

         return {
             "score": output.score,
             "char_order": output.char_order,
             "word_order": output.word_order,
             "beta": output.beta,
         }





'''simple docstring'''

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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			'▁'

UpperCamelCase__     :	Any				=			{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase__     :	Union[str, Any]				=			{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

UpperCamelCase__     :	Dict				=			{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
UpperCamelCase__     :	Optional[int]				=			{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Optional[int]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      Optional[int]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Optional[int]      							=   ['''input_ids''', '''attention_mask''']

      UpperCAmelCase_      :      List[int]      							=   []
      UpperCAmelCase_      :      List[int]      							=   []




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="m2m100"					,lowerCamelCase_ = None					,lowerCamelCase_=8					,**lowerCamelCase_					,)			->			None:



         '''simple docstring'''



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

         UpperCAmelCase__							:				Dict   				=  language_codes
         UpperCAmelCase__							:				Union[str, Any]   				=  FAIRSEQ_LANGUAGE_CODES[language_codes]
         UpperCAmelCase__							:				Union[str, Any]   				=  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

         UpperCAmelCase__							:				Any   				=  kwargs.get('''additional_special_tokens'''					,[]							)
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(lowerCamelCase_							)
             for lang_code in fairseq_language_code
             if self.get_lang_token(lowerCamelCase_							) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=lowerCamelCase_					,tgt_lang=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,language_codes=lowerCamelCase_					,sp_model_kwargs=self.sp_model_kwargs					,num_madeup_words=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Optional[int]   				=  vocab_file
         UpperCAmelCase__							:				Optional[Any]   				=  load_json(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.encoder.items()}
         UpperCAmelCase__							:				List[Any]   				=  spm_file
         UpperCAmelCase__							:				Any   				=  load_spm(lowerCamelCase_					,self.sp_model_kwargs							)

         UpperCAmelCase__							:				int   				=  len(self.encoder							)

         UpperCAmelCase__							:				Optional[int]   				=  {
             self.get_lang_token(lowerCamelCase_							): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)
         }
         UpperCAmelCase__							:				List[Any]   				=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)}
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.lang_token_to_id.items()}

         UpperCAmelCase__							:				Optional[int]   				=  src_lang if src_lang is not None else '''en'''
         UpperCAmelCase__							:				int   				=  tgt_lang
         UpperCAmelCase__							:				int   				=  self.get_lang_id(self._src_lang							)
         self.set_src_lang_special_tokens(self._src_lang							)

         UpperCAmelCase__							:				Optional[int]   				=  num_madeup_words




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return len(self.encoder							) + len(self.lang_token_to_id							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         return self._src_lang




      @src_lang.setter
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
         return self.encoder.get(lowerCamelCase_					,self.encoder[self.unk_token]							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
         return self.decoder.get(lowerCamelCase_					,self.unk_token							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  []
         UpperCAmelCase__							:				str   				=  ''''''
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(lowerCamelCase_							) + token
               UpperCAmelCase__							:				str   				=  []
            else:
               current_sub_tokens.append(lowerCamelCase_							)
         out_string += self.sp_model.decode(lowerCamelCase_							)
         return out_string.strip()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



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

         UpperCAmelCase__							:				Dict   				=  [1] * len(self.prefix_tokens							)
         UpperCAmelCase__							:				Optional[Any]   				=  [1] * len(self.suffix_tokens							)
         if token_ids_a is None:
            return prefix_ones + ([0] * len(lowerCamelCase_							)) + suffix_ones
         return prefix_ones + ([0] * len(lowerCamelCase_							)) + ([0] * len(lowerCamelCase_							)) + suffix_ones




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # 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.suffix_tokens




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def __getstate__(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				str   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  d

         # for backward compatibility
         if not hasattr(self					,'''sp_model_kwargs'''							):
            UpperCAmelCase__							:				Dict   				=  {}

         UpperCAmelCase__							:				int   				=  load_spm(self.spm_file					,self.sp_model_kwargs							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  Path(lowerCamelCase_							)
         if not save_dir.is_dir():
            raise OSError(f'''{save_directory} should be a directory'''							)
         UpperCAmelCase__							:				Optional[int]   				=  save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
         )
         UpperCAmelCase__							:				str   				=  save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
         )

         save_json(self.encoder					,lowerCamelCase_							)

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

         return (str(lowerCamelCase_							), str(lowerCamelCase_							))




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = "en"					,lowerCamelCase_ = None					,lowerCamelCase_ = "ro"					,**lowerCamelCase_					,)			->			BatchEncoding:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Tuple   				=  tgt_lang
         self.set_src_lang_special_tokens(self.src_lang							)
         return super().prepare_seqaseq_batch(lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         if src_lang is None or tgt_lang is None:
            raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
         UpperCAmelCase__							:				List[str]   				=  src_lang
         UpperCAmelCase__							:				List[str]   				=  self(lowerCamelCase_					,add_special_tokens=lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_lang_id(lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  tgt_lang_id
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         self.set_src_lang_special_tokens(self.src_lang							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         self.set_tgt_lang_special_tokens(self.tgt_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Union[str, Any]   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				Dict   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Tuple   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				str   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.lang_code_to_token[lang]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_lang_token(lowerCamelCase_							)
         return self.lang_token_to_id[lang_token]







def __UpperCamelCase(   _A					:				str  ,			_A					:				Dict[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  sentencepiece.SentencePieceProcessor(**_A    )
   spm.Load(str(_A    )    )
   return spm







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''r'''    ) as f:
      return json.load(_A    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''w'''    ) as f:
      json.dump(_A  ,			_A  ,			indent=2    )




