

'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_UpperCAmelCase  :		str  =		logging.get_logger(__name__)


_UpperCAmelCase  :		Any  =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
_UpperCAmelCase  :		Optional[int]  =		{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

_UpperCAmelCase  :		Union[str, Any]  =		{
    '''facebook/bart-base''': 10_24,
    '''facebook/bart-large''': 10_24,
    '''facebook/bart-large-mnli''': 10_24,
    '''facebook/bart-large-cnn''': 10_24,
    '''facebook/bart-large-xsum''': 10_24,
    '''yjernite/bart_eli5''': 10_24,
}




@lru_cache()
def 	UpperCamelCase   (       )		->	int:




  '''simple docstring'''



  lowercase									=(
      list(range(ord('''!'''	)     ,					ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)     ,					ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)     ,					ord('''ÿ'''	) + 1	)	)
  )
  lowercase									=bs[:]
  lowercase									=0
  for b in range(2**8	):
    if b not in bs:
      bs.append(lowercase_	)
      cs.append(2**8 + n	)
      n += 1
  lowercase									=[chr(lowercase_	) for n in cs]
  return dict(zip(lowercase_     ,					lowercase_	)	)




def 	UpperCamelCase   (       lowercase_						:    int	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=set()
  lowercase									=word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    lowercase									=char
  return pairs




class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       VOCAB_FILES_NAMES
 UpperCamelCase__     =       PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase__     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase__     =       ['input_ids', 'attention_mask']







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_="replace"  , snake_case_="<s>"  , snake_case_="</s>"  , snake_case_="</s>"  , snake_case_="<s>"  , snake_case_="<unk>"  , snake_case_="<pad>"  , snake_case_="<mask>"  , snake_case_=False  , **snake_case_  , ):
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else bos_token
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else eos_token
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else sep_token
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else cls_token
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else unk_token
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else pad_token

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

   super().__init__(
       errors=snake_case_  , bos_token=snake_case_  , eos_token=snake_case_  , unk_token=snake_case_  , sep_token=snake_case_  , cls_token=snake_case_  , pad_token=snake_case_  , mask_token=snake_case_  , add_prefix_space=snake_case_  , **snake_case_  , )

   with open(snake_case_  , encoding='''utf-8'''   ) as vocab_handle:
     lowercase									=json.load(snake_case_   )
   lowercase									={v: k for k, v in self.encoder.items()}
   lowercase									=errors  # how to handle errors in decoding
   lowercase									=bytes_to_unicode()
   lowercase									={v: k for k, v in self.byte_encoder.items()}
   with open(snake_case_  , encoding='''utf-8'''   ) as merges_handle:
     lowercase									=merges_handle.read().split('''\n'''   )[1:-1]
   lowercase									=[tuple(merge.split()   ) for merge in bpe_merges]
   lowercase									=dict(zip(snake_case_  , range(len(snake_case_   )   )   )   )
   lowercase									={}
   lowercase									=add_prefix_space

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   lowercase									=re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''   )







 @property
 def 						_A(       self   ):
   return len(self.encoder   )







 def 						_A(       self   ):
   return dict(self.encoder  , **self.added_tokens_encoder   )







 def 						_A(       self  , snake_case_   ):
   if token in self.cache:
     return self.cache[token]
   lowercase									=tuple(snake_case_   )
   lowercase									=get_pairs(snake_case_   )

   if not pairs:
     return token

   while True:
     lowercase									=min(snake_case_  , key=lambda snake_case_   : self.bpe_ranks.get(snake_case_  , float('''inf'''   )   )   )
     if bigram not in self.bpe_ranks:
       break
     lowercase							, lowercase									=bigram
     lowercase									=[]
     lowercase									=0
     while i < len(snake_case_   ):
       try:
         lowercase									=word.index(snake_case_  , snake_case_   )
       except ValueError:
         new_word.extend(word[i:]   )
         break
       else:
         new_word.extend(word[i:j]   )
         lowercase									=j

       if word[i] == first and i < len(snake_case_   ) - 1 and word[i + 1] == second:
         new_word.append(first + second   )
         i += 2
       else:
         new_word.append(word[i]   )
         i += 1
     lowercase									=tuple(snake_case_   )
     lowercase									=new_word
     if len(snake_case_   ) == 1:
       break
     else:
       lowercase									=get_pairs(snake_case_   )
   lowercase									=''' '''.join(snake_case_   )
   lowercase									=word
   return word







 def 						_A(       self  , snake_case_   ):
   lowercase									=[]
   for token in re.findall(self.pat  , snake_case_   ):
     lowercase									=''''''.join(
         self.byte_encoder[b] for b in token.encode('''utf-8'''   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_   ).split(''' '''   )   )
   return bpe_tokens







 def 						_A(       self  , snake_case_   ):
   return self.encoder.get(snake_case_  , self.encoder.get(self.unk_token   )   )







 def 						_A(       self  , snake_case_   ):
   return self.decoder.get(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=''''''.join(snake_case_   )
   lowercase									=bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8'''  , errors=self.errors   )
   return text







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   if not os.path.isdir(snake_case_   ):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
     return
   lowercase									=os.path.join(
       snake_case_  , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
   lowercase									=os.path.join(
       snake_case_  , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

   with open(snake_case_  , '''w'''  , encoding='''utf-8'''   ) as f:
     f.write(json.dumps(self.encoder  , indent=2  , sort_keys=snake_case_  , ensure_ascii=snake_case_   ) + '''\n'''   )

   lowercase									=0
   with open(snake_case_  , '''w'''  , encoding='''utf-8'''   ) as writer:
     writer.write('''#version: 0.2\n'''   )
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  , key=lambda snake_case_   : kv[1]   ):
       if index != token_index:
         logger.warning(
             f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
             ''' Please check that the tokenizer is not corrupted!'''   )
         lowercase									=token_index
       writer.write(''' '''.join(snake_case_   ) + '''\n'''   )
       index += 1

   return vocab_file, merge_file







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowercase									=[self.cls_token_id]
   lowercase									=[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep







 def 						_A(       self  , snake_case_  , snake_case_ = None  , snake_case_ = False   ):
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_  , token_ids_a=snake_case_  , already_has_special_tokens=snake_case_   )

   if token_ids_a is None:
     return [1] + ([0] * len(snake_case_   )) + [1]
   return [1] + ([0] * len(snake_case_   )) + [1, 1] + ([0] * len(snake_case_   )) + [1]







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   lowercase									=[self.sep_token_id]
   lowercase									=[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 						_A(       self  , snake_case_  , snake_case_=False  , **snake_case_   ):
   lowercase									=kwargs.pop('''add_prefix_space'''  , self.add_prefix_space   )
   if (is_split_into_words or add_prefix_space) and (len(snake_case_   ) > 0 and not text[0].isspace()):
     lowercase									=''' ''' + text
   return (text, kwargs)








'''simple docstring'''



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

import datasets


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

__UpperCamelCase   :							Union[str, Any]         =   """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

__UpperCamelCase   :							str         =   """
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> 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.\"]
        >>> 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.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION  )
class 				__SCREAMING_SNAKE_CASE							(				datasets.Metric  ):




      def 			__UpperCamelCase				(	self   )				->Union[str, 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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = CHRF.CHAR_ORDER			, lowerCamelCase = CHRF.WORD_ORDER			, lowerCamelCase = CHRF.BETA			, lowerCamelCase = False			, lowerCamelCase = False			, lowerCamelCase = False			, )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     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'   )
         __a					  =     [[refs[i] for refs in references] for i in range(lowerCamelCase   )]

         __a					  =     CHRF(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )
         __a					  =     sb_chrf.corpus_score(lowerCamelCase			, lowerCamelCase   )

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