





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


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

__lowerCamelCase       :     Optional[int]   =     """▁"""

__lowerCamelCase       :     List[str]   =     {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

__lowerCamelCase       :     Tuple   =     {
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

__lowerCamelCase       :     Optional[Any]   =     {
    """vocab_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
    },
    """sentencepiece_model_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
    },
}

__lowerCamelCase       :     int   =     {
    """ernie-m-base""": 514,
    """ernie-m-large""": 514,
}

__lowerCamelCase       :     Union[str, Any]   =     {
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   ["input_ids"]

      _UpperCAmelCase	:Any						    =   VOCAB_FILES_NAMES
      _UpperCAmelCase	:Optional[int]						    =   PRETRAINED_INIT_CONFIGURATION
      _UpperCAmelCase	:List[Any]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase	:List[str]						    =   PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase	:Tuple						    =   RESOURCE_FILES_NAMES





      def __init__( self       ,							A_       ,							A_=None       ,							A_=False       ,							A_="utf8"       ,							A_="[UNK]"       ,							A_="[SEP]"       ,							A_="[PAD]"       ,							A_="[CLS]"       ,							A_="[MASK]"       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   {} if sp_model_kwargs is None else sp_model_kwargs
       super().__init__(
           do_lower_case=A_       ,							unk_token=A_       ,							sep_token=A_       ,							pad_token=A_       ,							cls_token=A_       ,							mask_token=A_       ,							vocab_file=A_       ,							encoding=A_       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**A_       ,							)
       UpperCamelCase	:					List[str]       	=   do_lower_case
       UpperCamelCase	:					List[Any]       	=   sentencepiece_model_ckpt
       UpperCamelCase	:					Union[str, Any]       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(A_   )

       # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
       if vocab_file is not None:
        UpperCamelCase	:					List[Any]       	=   self.load_vocab(filepath=A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   {self.sp_model.id_to_piece(A_   ): id for id in range(self.sp_model.get_piece_size()   )}
       UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in self.vocab.items()}





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if text is None:
        return None

       UpperCamelCase	:					Optional[Any]       	=   self.tokenize(A_   )
       UpperCamelCase	,    UpperCamelCase	:					Any       	=   "", []

       for i, ch in enumerate(A_   ):
        if ch in self.SP_CHAR_MAPPING:
         UpperCamelCase	:					int       	=   self.SP_CHAR_MAPPING.get(A_   )
        else:
         UpperCamelCase	:					int       	=   unicodedata.normalize("NFKC"       ,							A_   )
        if self.is_whitespace(A_   ):
         continue
        normalized_text += ch
        char_mapping.extend([i] * len(A_   )   )

       UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					str       	=   normalized_text, [], 0

       if self.do_lower_case:
        UpperCamelCase	:					List[str]       	=   text.lower()

       for token in split_tokens:
        if token[:1] == "▁":
         UpperCamelCase	:					List[Any]       	=   token[1:]
        UpperCamelCase	:					Tuple       	=   text[offset:].index(A_   ) + offset
        UpperCamelCase	:					List[Any]       	=   start + len(A_   )

        token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)   )
        UpperCamelCase	:					Dict       	=   end
       return token_mapping





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return len(self.vocab   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return dict(self.vocab       ,							**self.added_tokens_encoder   )





      def __getstate__( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.__dict__.copy()
       UpperCamelCase	:					Dict       	=   None
       return state





      def __setstate__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   d

       # for backward compatibility
       if not hasattr(self       ,							"sp_model_kwargs"   ):
        UpperCamelCase	:					Optional[int]       	=   {}

       UpperCamelCase	:					Optional[Any]       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.sentencepiece_model_ckpt   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return "".join((self.SP_CHAR_MAPPING.get(A_       ,							A_   ) for c in text)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_=False       ,							A_=64       ,							A_=0.1   ):
       '''simple docstring'''




       if self.sp_model_kwargs.get("enable_sampling"   ) is True:
        UpperCamelCase	:					Any       	=   True
       if self.sp_model_kwargs.get("alpha"   ) is not None:
        UpperCamelCase	:					Union[str, Any]       	=   self.sp_model_kwargs.get("alpha"   )
       if self.sp_model_kwargs.get("nbest_size"   ) is not None:
        UpperCamelCase	:					Dict       	=   self.sp_model_kwargs.get("nbest_size"   )

       if not enable_sampling:
        UpperCamelCase	:					Optional[int]       	=   self.sp_model.EncodeAsPieces(A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self.sp_model.SampleEncodeAsPieces(A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Tuple       	=   []
       for pi, piece in enumerate(A_   ):
        if piece == SPIECE_UNDERLINE:
         if not pieces[pi + 1].startswith(A_   ) and pi != 0:
          new_pieces.append(A_   )
          continue
         else:
          continue
        UpperCamelCase	:					str       	=   0
        for i, chunk in enumerate(A_   ):
         if chunk == SPIECE_UNDERLINE:
          continue
         if self.is_ch_char(A_   ) or self.is_punct(A_   ):
          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
           new_pieces.append(piece[lst_i:i]   )
          new_pieces.append(A_   )
          UpperCamelCase	:					List[str]       	=   i + 1
         elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
           new_pieces.append(piece[lst_i:i]   )
          UpperCamelCase	:					Any       	=   i
         elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
           new_pieces.append(piece[lst_i:i]   )
          UpperCamelCase	:					Dict       	=   i
        if len(A_   ) > lst_i:
         new_pieces.append(piece[lst_i:]   )
       return new_pieces





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   "".join(A_   ).replace(A_       ,							" "   ).strip()
       return out_string





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.convert_ids_to_tokens(A_   )
       UpperCamelCase	:					Optional[Any]       	=   "".join(A_   ).replace(A_       ,							" "   ).strip()
       return out_string





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.vocab.get(A_       ,							self.vocab.get(self.unk_token   )   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.reverse_vocab.get(A_       ,							self.unk_token   )





      def        __UpperCamelCase( self       ,							A_       ,							A_=None   ):
       '''simple docstring'''




       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase	:					int       	=   [self.cls_token_id]
       UpperCamelCase	:					Optional[int]       	=   [self.sep_token_id]
       return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep





      def        __UpperCamelCase( self       ,							A_       ,							A_=None   ):
       '''simple docstring'''




       if offset_mapping_a is None:
        return [(0, 0)] + offset_mapping_a + [(0, 0)]

       return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]





      def        __UpperCamelCase( self       ,							A_       ,							A_=None       ,							A_=False   ):
       '''simple docstring'''




       if already_has_special_tokens:
        if token_ids_a is not None:
         raise ValueError(
             "You should not supply a second sequence if the provided sequence of "
             "ids is already formatted with special tokens for the model."   )
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
        return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]
       return [1] + ([0] * len(A_   )) + [1]





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       if token_ids_a is None:
        # [CLS] X [SEP]
        return (len(A_   ) + 2) * [0]

       # [CLS] A [SEP] [SEP] B [SEP]
       return [0] * (len(A_   ) + 1) + [1] * (len(A_   ) + 3)





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if "\u4e00" <= char <= "\u9fff":
        return True
       return False





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if ("a" <= char <= "z") or ("A" <= char <= "Z"):
        return True
       return False





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if char in ",;:.?!~，；：。？！《》【】":
        return True
       return False





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if char == " " or char == "\t" or char == "\n" or char == "\r":
        return True
       if len(A_   ) == 1:
        UpperCamelCase	:					Optional[Any]       	=   unicodedata.category(A_   )
        if cat == "Zs":
         return True
       return False





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   {}
       with io.open(A_       ,							"r"       ,							encoding="utf-8"   ) as f:
        for index, line in enumerate(A_   ):
         UpperCamelCase	:					List[Any]       	=   line.rstrip("\n"   )
         UpperCamelCase	:					Any       	=   int(A_   )

       return token_to_idx





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   0
       if os.path.isdir(A_   ):
        UpperCamelCase	:					Optional[int]       	=   os.path.join(
            A_       ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
       else:
        UpperCamelCase	:					List[Any]       	=   (filename_prefix + "-" if filename_prefix else "") + save_directory
       with open(A_       ,							"w"       ,							encoding="utf-8"   ) as writer:
        for token, token_index in sorted(self.vocab.items()       ,							key=lambda A_   : kv[1]   ):
         if index != token_index:
          logger.warning(
              F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
              " Please check that the vocabulary is not corrupted!"   )
          UpperCamelCase	:					Any       	=   token_index
         writer.write(token + "\n"   )
         index += 1

       UpperCamelCase	:					Any       	=   os.path.join(A_       ,							"sentencepiece.bpe.model"   )
       with open(A_       ,							"wb"   ) as fi:
        UpperCamelCase	:					int       	=   self.sp_model.serialized_model_proto()
        fi.write(A_   )

       return (vocab_file,)







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase       :     List[Any]   =     """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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\",
}
"""

__lowerCamelCase       :     Optional[int]   =     """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase       :     str   =     """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""



def 		A_				(     _lowerCAmelCase     )						-> str:

 def remove_articles(_lowerCAmelCase     ):
  UpperCamelCase	:					Tuple       	=   re.compile(r"\b(a|an|the)\b"							,   re.UNICODE     )
  return re.sub(_lowerCAmelCase							,   " "							,   _lowerCAmelCase     )

 def white_space_fix(_lowerCAmelCase     ):
  return " ".join(text.split()     )

 def remove_punc(_lowerCAmelCase     ):
  UpperCamelCase	:					int       	=   set(string.punctuation     )
  return "".join(ch for ch in text if ch not in exclude     )

 def lower(_lowerCAmelCase     ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 return int(normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   [any(compute_exact(_lowerCAmelCase							,   _lowerCAmelCase     ) for ref in refs     ) for pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase     )]
 return (sum(_lowerCAmelCase     ) / len(_lowerCAmelCase     )) * 100



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					Union[str, Any]       	=   [rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[int]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					List[Any]       	=   Counter()
 for sgram, scount in sgramcounter.items():
  UpperCamelCase	:					Tuple       	=   scount * numref

 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCamelCase	:					Dict       	=   ccount * numref

 # KEEP
 UpperCamelCase	:					List[Any]       	=   sgramcounter_rep & cgramcounter_rep
 UpperCamelCase	:					Union[str, Any]       	=   keepgramcounter_rep & rgramcounter
 UpperCamelCase	:					Dict       	=   sgramcounter_rep & rgramcounter

 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Tuple       	=   0
 for keepgram in keepgramcountergood_rep:
  keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
  # Fix an alleged bug [2] in the keep score computation.
  # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
  keeptmpscorea += keepgramcountergood_rep[keepgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 #      a target exactly.
 UpperCamelCase	:					Any       	=   1
 UpperCamelCase	:					Any       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   keeptmpscorea / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  UpperCamelCase	:					Union[str, Any]       	=   keeptmpscorea / sum(keepgramcounterall_rep.values()     )
 UpperCamelCase	:					Any       	=   0
 if keepscore_precision > 0 or keepscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 UpperCamelCase	:					Any       	=   sgramcounter_rep - cgramcounter_rep
 UpperCamelCase	:					str       	=   delgramcounter_rep - rgramcounter
 UpperCamelCase	:					Any       	=   sgramcounter_rep - rgramcounter
 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Union[str, Any]       	=   0
 for delgram in delgramcountergood_rep:
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Dict       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					str       	=   deltmpscorea / len(_lowerCAmelCase     )

 # ADDITION
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) & set(_lowerCAmelCase     )
 UpperCamelCase	:					Dict       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   0
 for addgram in addgramcountergood:
  addtmpscore += 1

 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Tuple       	=   1
 UpperCamelCase	:					Tuple       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   addtmpscore / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Tuple       	=   addtmpscore / len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   0
 if addscore_precision > 0 or addscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   ssent.split(" "     )
 UpperCamelCase	:					Dict       	=   csent.split(" "     )
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					str       	=   []

 UpperCamelCase	:					Dict       	=   []
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Optional[Any]       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for rsent in rsents:
  UpperCamelCase	:					List[Any]       	=   rsent.split(" "     )
  UpperCamelCase	:					List[str]       	=   []
  UpperCamelCase	:					int       	=   []
  UpperCamelCase	:					Tuple       	=   []
  ragramslist.append(_lowerCAmelCase     )
  for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
   if i < len(_lowerCAmelCase     ) - 1:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 2:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 3:
    UpperCamelCase	:					List[Any]       	=   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
    ragrams.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					List[str]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Optional[int]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
   sagrams.append(_lowerCAmelCase     )

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					Optional[Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(_lowerCAmelCase     )

 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[Any]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					str       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
 UpperCamelCase	:					str       	=   sum([delascore, delascore, delascore, delascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   sum([addascore, addascore, addascore, addascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   (avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase = True							,   _lowerCAmelCase = "13a"							,   _lowerCAmelCase = True     )						-> Optional[Any]:
 # Normalization is requried for the ASSET dataset (one of the primary
 # datasets in sentence simplification) to allow using space
 # to split the sentence. Even though Wiki-Auto and TURK datasets,
 # do not require normalization, we do it for consistency.
 # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
 # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

 if lowercase:
  UpperCamelCase	:					Dict       	=   sentence.lower()

 if tokenizer in ["13a", "intl"]:
  if version.parse(sacrebleu.__version__     ).major >= 2:
   UpperCamelCase	:					str       	=   sacrebleu.metrics.bleu._get_tokenizer(_lowerCAmelCase     )()(_lowerCAmelCase     )
  else:
   UpperCamelCase	:					Dict       	=   sacrebleu.TOKENIZERS[tokenizer]()(_lowerCAmelCase     )
 elif tokenizer == "moses":
  UpperCamelCase	:					Union[str, Any]       	=   sacremoses.MosesTokenizer().tokenize(_lowerCAmelCase							,   return_str=_lowerCAmelCase							,   escape=_lowerCAmelCase     )
 elif tokenizer == "penn":
  UpperCamelCase	:					str       	=   sacremoses.MosesTokenizer().penn_tokenize(_lowerCAmelCase							,   return_str=_lowerCAmelCase     )
 else:
  UpperCamelCase	:					Union[str, Any]       	=   sentence

 if not return_str:
  UpperCamelCase	:					Tuple       	=   normalized_sent.split()

 return normalized_sent



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 if not (len(_lowerCAmelCase     ) == len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )):
  raise ValueError("Sources length must match predictions and references lengths."     )
 UpperCamelCase	:					Optional[Any]       	=   0
 for src, pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     ):
  sari_score += SARIsent(normalize(_lowerCAmelCase     )							,   normalize(_lowerCAmelCase     )							,   [normalize(_lowerCAmelCase     ) for sent in refs]     )
 UpperCamelCase	:					Optional[int]       	=   sari_score / len(_lowerCAmelCase     )
 return 100 * sari_score



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase="exp"							,   _lowerCAmelCase=None							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   )						-> List[str]:
 UpperCamelCase	:					Optional[Any]       	=   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	:					Optional[int]       	=   [[refs[i] for refs in references] for i in range(_lowerCAmelCase     )]
 UpperCamelCase	:					Tuple       	=   sacrebleu.corpus_bleu(
     _lowerCAmelCase							,   _lowerCAmelCase							,   smooth_method=_lowerCAmelCase							,   smooth_value=_lowerCAmelCase							,   force=_lowerCAmelCase							,   lowercase=_lowerCAmelCase							,   use_effective_order=_lowerCAmelCase							,   )
 return output.score



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION       ,							citation=_CITATION       ,							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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
               "https://github.com/cocoxu/simplification/blob/master/SARI.py",
               "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
               "https://github.com/mjpost/sacreBLEU",
           ]       ,							reference_urls=[
               "https://www.aclweb.org/anthology/Q16-1029.pdf",
               "https://github.com/mjpost/sacreBLEU",
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {}
       result.update({"sari": compute_sari(sources=A_       ,							predictions=A_       ,							references=A_   )}   )
       result.update({"sacrebleu": compute_sacrebleu(predictions=A_       ,							references=A_   )}   )
       result.update({"exact": compute_em(predictions=A_       ,							references=A_   )}   )
       return result
