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  							=						''''''


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



      class   snake_case_       (		tr.AbstractTransform       ):






         def __init__(      self				:     int	,						lowercase_				:     str = " "  )					->						Optional[Any]:
             lowercase__ :   Optional[int]    					=				sentence_delimiter






         def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     str  )					->						List[str]:
             return list(lowercase_  )







         def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     List[str]  )					->						Union[str, Any]:
             lowercase__ :   str    					=				[]
             for sent_idx, sentence in enumerate(lowercase_  ):
                 chars.extend(self.process_string(lowercase_  )  )
                 if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase_  ) - 1:
                     chars.append(self.sentence_delimiter  )
             return chars



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


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

UpperCamelCase  							=						'''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'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
performance of the ASR system with a CER of 0 being a perfect score.
'''

UpperCamelCase  							=						'''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''



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






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Any:
       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       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     Any	,						lowercase_				:     List[str]	,						lowercase_				:     Union[str, Any]=False  )					->						Union[str, Any]:
       if concatenate_texts:
           return jiwer.compute_measures(
               lowercase_	,						lowercase_	,						truth_transform=lowercase_	,						hypothesis_transform=lowercase_	,						)["wer"]

       lowercase__ :   List[str]    					=				0
       lowercase__ :   Dict    					=				0
       for prediction, reference in zip(lowercase_	,						lowercase_  ):
           lowercase__ :   Union[str, Any]    					=				jiwer.compute_measures(
               lowercase_	,						lowercase_	,						truth_transform=lowercase_	,						hypothesis_transform=lowercase_	,						)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total







"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_rembert import RemBertTokenizer
else:
  __snake_case   			=    None

__snake_case   			=    logging.get_logger(__name__)
__snake_case   			=    {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

__snake_case   			=    {
    """google/rembert""": 256,
}

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  RemBertTokenizer







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=False						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="[SEP]"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="[CLS]"						,	UpperCamelCase__="[MASK]"						,	**UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else mask_token

       super().__init__(
           UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	do_lower_case=UpperCamelCase__						,	remove_space=UpperCamelCase__						,	keep_accents=UpperCamelCase__						,	bos_token=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	sep_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	cls_token=UpperCamelCase__						,	mask_token=UpperCamelCase__						,	**UpperCamelCase__						,	)

       snake_case    :      int      					=    do_lower_case
       snake_case    :      Union[str, Any]      					=    remove_space
       snake_case    :      Optional[int]      					=    keep_accents
       snake_case    :      Any      					=    vocab_file
       snake_case    :      Any      					=    False if not self.vocab_file else True







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [self.cls_token_id]
       if token_ids_a is None:
           return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						)	->		List[int]:

       '''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(UpperCamelCase__						)) + [1] + ([0] * len(UpperCamelCase__						)) + [1]
       return [1] + ([0] * len(UpperCamelCase__						)) + [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [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						) * [0] + len(token_ids_a + sep						) * [1]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__						):
           logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__						)						)
           return
       snake_case    :      str      					=    os.path.join(
           UpperCamelCase__						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCamelCase__						):
           copyfile(self.vocab_file						,	UpperCamelCase__						)

       return (out_vocab_file,)





