




from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
     import importlib_metadata
else:
     import importlib.metadata as importlib_metadata


_UpperCAmelCase		:  Dict	=   ""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):




     class       __lowerCAmelCase				(							tr.AbstractTransform):






            def __init__(      self:    List[str]				,    _lowerCAmelCase:    str = " " ):
              lowercase			:Any							=  sentence_delimiter






            def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str ):
              return list(_lowerCAmelCase )






            def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    List[str] ):
              lowercase			:int							=  []
              for sent_idx, sentence in enumerate(_lowerCAmelCase ):
                chars.extend(self.process_string(_lowerCAmelCase ) )
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_lowerCAmelCase ) - 1:
                  chars.append(self.sentence_delimiter )
              return chars







     _UpperCAmelCase		:  Any	=   tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     _UpperCAmelCase		:  Optional[Any]	=   tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


_UpperCAmelCase		:  Optional[int]	=   "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

_UpperCAmelCase		:  Any	=   "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

_UpperCAmelCase		:  Optional[Any]	=   "\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




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






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         return datasets.MetricInfo(
             description=_DESCRIPTION				,    citation=_CITATION				,    inputs_description=_KWARGS_DESCRIPTION				,    features=datasets.Features(
                 {
                     "predictions": datasets.Value("string"				,    id="sequence" ),
                     "references": datasets.Value("string"				,    id="sequence" ),
                 } )				,    codebase_urls=["https://github.com/jitsi/jiwer/"]				,    reference_urls=[
                 "https://en.wikipedia.org/wiki/Word_error_rate",
                 "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
             ]				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    Optional[int]=False ):
         if concatenate_texts:
           return jiwer.compute_measures(
               _lowerCAmelCase				,    _lowerCAmelCase				,    truth_transform=_lowerCAmelCase				,    hypothesis_transform=_lowerCAmelCase				,    )["wer"]

         lowercase			:int							=  0
         lowercase			:Optional[int]							=  0
         for prediction, reference in zip(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:int							=  jiwer.compute_measures(
               _lowerCAmelCase				,    _lowerCAmelCase				,    truth_transform=_lowerCAmelCase				,    hypothesis_transform=_lowerCAmelCase				,    )
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

         return incorrect / total









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


_UpperCAmelCase		:  Dict	=   logging.get_logger(__name__)

_UpperCAmelCase		:  Optional[int]	=   "▁"

_UpperCAmelCase		:  int	=   {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

_UpperCAmelCase		:  List[Any]	=   {
    "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"
        )
    },
}

_UpperCAmelCase		:  str	=   {
    "facebook/s2t-small-librispeech-asr": 1024,
}

_UpperCAmelCase		:  List[str]	=   ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

_UpperCAmelCase		:  Tuple	=   {"mustc": MUSTC_LANGS}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  MAX_MODEL_INPUT_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']

       _a           =  []






       def __init__(      self:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    List[Any]="<s>"				,    _lowerCAmelCase:    List[str]="</s>"				,    _lowerCAmelCase:    Optional[int]="<pad>"				,    _lowerCAmelCase:    List[str]="<unk>"				,    _lowerCAmelCase:    Dict=False				,    _lowerCAmelCase:    Optional[Any]=False				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Optional[Dict[str, Any]] = None				,    **_lowerCAmelCase:    Tuple				,    ):
         lowercase			:str							=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase				,    eos_token=_lowerCAmelCase				,    unk_token=_lowerCAmelCase				,    pad_token=_lowerCAmelCase				,    do_upper_case=_lowerCAmelCase				,    do_lower_case=_lowerCAmelCase				,    tgt_lang=_lowerCAmelCase				,    lang_codes=_lowerCAmelCase				,    sp_model_kwargs=self.sp_model_kwargs				,    **_lowerCAmelCase				,    )
         lowercase			:List[str]							=  do_upper_case
         lowercase			:List[str]							=  do_lower_case

         lowercase			:str							=  load_json(_lowerCAmelCase )
         lowercase			:List[Any]							=  {v: k for k, v in self.encoder.items()}
         lowercase			:str							=  spm_file
         lowercase			:Any							=  load_spm(_lowerCAmelCase				,    self.sp_model_kwargs )

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

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

           self.set_tgt_lang_special_tokens(self._tgt_lang )
         else:
           lowercase			:Tuple							=  {}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         return len(self.encoder )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         return self._tgt_lang






       @tgt_lang.setter
       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any] ):
         lowercase			:Any							=  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str ):
         lowercase			:Any							=  self.lang_code_to_id[tgt_lang]
         lowercase			:List[Any]							=  [lang_code_id]






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    str ):
         return self.sp_model.encode(_lowerCAmelCase				,    out_type=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    Tuple ):
         return self.encoder.get(_lowerCAmelCase				,    self.encoder[self.unk_token] )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    int ):
         return self.decoder.get(_lowerCAmelCase				,    self.unk_token )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str] ):
         lowercase			:int							=  []
         lowercase			:Any							=  ""
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             lowercase			:Dict							=  self.sp_model.decode(_lowerCAmelCase )
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             lowercase			:Any							=  []
           else:
             current_sub_tokens.append(_lowerCAmelCase )
         lowercase			:Dict							=  self.sp_model.decode(_lowerCAmelCase )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()






       def     SCREAMING_SNAKE_CASE			(      self:    Any				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    str=None ):
         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     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None				,    _lowerCAmelCase:    bool = False ):

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

         lowercase			:int							=  [1] * len(self.prefix_tokens )
         lowercase			:List[str]							=  [1]
         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     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:int							=  self.encoder.copy()
         vocab.update(self.added_tokens_encoder )
         return vocab






       def __getstate__(      self:    Optional[Any] ):
         lowercase			:Union[str, Any]							=  self.__dict__.copy()
         lowercase			:Dict							=  None
         return state






       def __setstate__(      self:    Union[str, Any]				,    _lowerCAmelCase:    Dict ):
         lowercase			:List[Any]							=  d

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

         lowercase			:Union[str, Any]							=  load_spm(self.spm_file				,    self.sp_model_kwargs )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         lowercase			:List[str]							=  Path(_lowerCAmelCase )
         assert save_dir.is_dir(), F"{save_directory} should be a directory"
         lowercase			:List[Any]							=  save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         lowercase			:Any							=  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:
             lowercase			:int							=  self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase )

         return (str(_lowerCAmelCase ), str(_lowerCAmelCase ))







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Tuple							=  sentencepiece.SentencePieceProcessor(**lowerCamelCase      )
  spm.Load(str(lowerCamelCase      )      )
  return spm



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  with open(lowerCamelCase,	"r"      ) as f:
    return json.load(lowerCamelCase      )







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  with open(lowerCamelCase,	"w"      ) as f:
    json.dump(lowerCamelCase,	lowerCamelCase,	indent=2      )



