

from typing import List

from packaging import version
from sklearn.metrics import fa_score

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


# TODO(Patrick/Anton)
_SCREAMING_SNAKE_CASE							 =			'''\
'''

_SCREAMING_SNAKE_CASE							 =			'''\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
'''

_SCREAMING_SNAKE_CASE							 =			'''
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.
        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',
        \'tokenize\', \'use_effective_order\'.
    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.
        Keywords include \'concatenate_texts\'.
Returns: depending on the XTREME-S task, one or several of:
    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'
    "f1": F1 score - for \'minds14\'
    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'
Examples:

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'
    >>> references = ["it is sunny here", "paper and pen are essentials"]
    >>> predictions = ["it\'s sunny", "paper pen are essential"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'wer\': 0.56, \'cer\': 0.27}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')
    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]
    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'bleu\': 31.65}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'accuracy\': 0.6}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'f1\': 0.58, \'accuracy\': 0.6}
'''

_SCREAMING_SNAKE_CASE							 =			['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
_SCREAMING_SNAKE_CASE							 =			''''''

try:
      from jiwer import transforms as tr

      _SCREAMING_SNAKE_CASE							 =			True
except ImportError:
      _SCREAMING_SNAKE_CASE							 =			False

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


      class      lowerCamelCase_ (			tr.AbstractTransform):




       def __init__(			self  ,  _UpperCAmelCase = " "							):
         __UpperCAmelCase	:  str		=     sentence_delimiter




       def     a       (			self  ,  _UpperCAmelCase							):
         return list(_UpperCAmelCase							)




       def     a       (			self  ,  _UpperCAmelCase							):
         __UpperCAmelCase	:  Any		=     []
         for sent_idx, sentence in enumerate(_UpperCAmelCase							):
           chars.extend(self.process_string(_UpperCAmelCase							)							)
           if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_UpperCAmelCase							) - 1:
             chars.append(self.sentence_delimiter							)
         return chars



      _SCREAMING_SNAKE_CASE							 =			tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      _SCREAMING_SNAKE_CASE							 =			tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      _SCREAMING_SNAKE_CASE							 =			None





def  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     Dict  )							->		int:
  return float((preds == labels).mean()  )





def  _snake_case		(snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[Any]  )							->		List[Any]:
  return {
      "f1": float(fa_score(y_true=snake_case_    ,       y_pred=snake_case_    ,       average='macro'  )  ),
      "accuracy": simple_accuracy(snake_case_    ,       snake_case_  ),
  }





def  _snake_case		(snake_case_					:     str    ,       snake_case_					:     int    ,       snake_case_					:     Any="exp"    ,       snake_case_					:     Tuple=None    ,       snake_case_					:     Tuple=False    ,       snake_case_					:     Tuple=False    ,       snake_case_					:     List[Any]=None    ,       snake_case_					:     Optional[Any]=False    ,       )							->		Dict:
  # xtreme-s can only have one label
  __UpperCAmelCase	:  Dict		=     [[label] for label in labels]
  __UpperCAmelCase	:  Tuple		=     list(snake_case_  )
  try:
    import sacrebleu as scb
  except ImportError:
    raise ValueError(
        'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
        'You can install it via `pip install sacrebleu`.'  )

  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"`.'  )

  __UpperCAmelCase	:  Optional[int]		=     len(labels[0]  )
  if any(len(snake_case_  ) != references_per_prediction for refs in labels  ):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'  )
  __UpperCAmelCase	:  int		=     [[refs[i] for refs in labels] for i in range(snake_case_  )]
  __UpperCAmelCase	:  Any		=     scb.corpus_bleu(
      snake_case_    ,       snake_case_    ,       smooth_method=snake_case_    ,       smooth_value=snake_case_    ,       force=snake_case_    ,       lowercase=snake_case_    ,       use_effective_order=snake_case_    ,       **({'tokenize': tokenize} if tokenize else {})    ,       )
  return {"bleu": output.score}





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     int    ,       snake_case_					:     Any    ,       snake_case_					:     List[str]  )							->		str:
  try:
    from jiwer import compute_measures
  except ImportError:
    raise ValueError(
        F"jiwer has to be installed in order to apply the wer metric for {config_name}."
        'You can install it via `pip install jiwer`.'  )

  if concatenate_texts:
    __UpperCAmelCase	:  Union[str, Any]		=     compute_measures(snake_case_    ,       snake_case_  )['wer']

    __UpperCAmelCase	:  Dict		=     compute_measures(snake_case_    ,       snake_case_    ,       truth_transform=snake_case_    ,       hypothesis_transform=snake_case_  )['wer']
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     List[Any]="wer"  ):
      __UpperCAmelCase	:  List[str]		=     0
      __UpperCAmelCase	:  Optional[int]		=     0
      for prediction, reference in zip(snake_case_    ,       snake_case_  ):
        if score_type == "wer":
          __UpperCAmelCase	:  str		=     compute_measures(snake_case_    ,       snake_case_  )
        elif score_type == "cer":
          __UpperCAmelCase	:  List[Any]		=     compute_measures(
              snake_case_    ,       snake_case_    ,       truth_transform=snake_case_    ,       hypothesis_transform=snake_case_  )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total

    return {"wer": compute_score(snake_case_    ,       snake_case_    ,       'wer'  ), "cer": compute_score(snake_case_    ,       snake_case_    ,       'cer'  )}







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




 def     a       (			self							):
   if self.config_name not in _CONFIG_NAMES:
     raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"							)

   __UpperCAmelCase	:  Any		=     'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

   return datasets.MetricInfo(
       description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
           {'predictions': datasets.Value(_UpperCAmelCase							), 'references': datasets.Value(_UpperCAmelCase							)}							)  ,  codebase_urls=[]  ,  reference_urls=[]  ,  format='numpy'  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None							):
   __UpperCAmelCase	:  Any		=     bleu_kwargs if bleu_kwargs is not None else {}
   __UpperCAmelCase	:  Any		=     wer_kwargs if wer_kwargs is not None else {}

   if self.config_name == "fleurs-lang_id":
     return {"accuracy": simple_accuracy(_UpperCAmelCase  ,  _UpperCAmelCase							)}
   elif self.config_name == "minds14":
     return fa_and_simple_accuracy(_UpperCAmelCase  ,  _UpperCAmelCase							)
   elif self.config_name == "covost2":
     __UpperCAmelCase	:  Tuple		=     bleu_kwargs.pop('smooth_method'  ,  'exp'							)
     __UpperCAmelCase	:  int		=     bleu_kwargs.pop('smooth_value'  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  List[Any]		=     bleu_kwargs.pop('force'  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     bleu_kwargs.pop('lowercase'  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     bleu_kwargs.pop('tokenize'  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  List[str]		=     bleu_kwargs.pop('use_effective_order'  ,  _UpperCAmelCase							)
     return bleu(
         preds=_UpperCAmelCase  ,  labels=_UpperCAmelCase  ,  smooth_method=_UpperCAmelCase  ,  smooth_value=_UpperCAmelCase  ,  force=_UpperCAmelCase  ,  lowercase=_UpperCAmelCase  ,  tokenize=_UpperCAmelCase  ,  use_effective_order=_UpperCAmelCase  ,  )
   elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
     __UpperCAmelCase	:  Tuple		=     wer_kwargs.pop('concatenate_texts'  ,  _UpperCAmelCase							)
     return wer_and_cer(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  self.config_name							)
   else:
     raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"							)







_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



