
'''simple docstring'''





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        UpperCAmelCase_       (						tr.AbstractTransform							):

       '''simple docstring'''







       def __init__(				self		:     Optional[int] ,							snake_case__		:     str = " "   ):



         '''simple docstring'''




         UpperCAmelCase__			:				Tuple												=       sentence_delimiter







       def        UpperCamelCase     (				self		:     int ,							snake_case__		:     str   ):



         '''simple docstring'''




         return list(snake_case__   )







       def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     List[str]   ):



         '''simple docstring'''




         UpperCAmelCase__			:				Optional[int]												=       []
         for sent_idx, sentence in enumerate(snake_case__   ):
           chars.extend(self.process_string(snake_case__   )   )
           if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case__   ) - 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_    (				lowercase__      ,					lowercase__		):
  return float((preds == labels).mean()		)







def snake_case_    (				lowercase__      ,					lowercase__		):
  return {
      "f1": float(fa_score(y_true=lowercase__      ,					y_pred=lowercase__      ,					average="macro"		)		),
      "accuracy": simple_accuracy(lowercase__      ,					lowercase__		),
  }







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__="exp"      ,					lowercase__=None      ,					lowercase__=False      ,					lowercase__=False      ,					lowercase__=None      ,					lowercase__=False      ,					):
  # xtreme-s can only have one label
  UpperCAmelCase__			:				str												=       [[label] for label in labels]
  UpperCAmelCase__			:				Any												=       list(lowercase__		)
  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__			:				Dict												=       len(labels[0]		)
  if any(len(lowercase__		) != references_per_prediction for refs in labels		):
    raise ValueError("Sacrebleu requires the same number of references for each prediction"		)
  UpperCAmelCase__			:				Dict												=       [[refs[i] for refs in labels] for i in range(lowercase__		)]
  UpperCAmelCase__			:				str												=       scb.corpus_bleu(
      lowercase__      ,					lowercase__      ,					smooth_method=lowercase__      ,					smooth_value=lowercase__      ,					force=lowercase__      ,					lowercase=lowercase__      ,					use_effective_order=lowercase__      ,					**({"tokenize": tokenize} if tokenize else {})      ,					)
  return {"bleu": output.score}







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__		):
  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(lowercase__      ,					lowercase__		)["wer"]

    UpperCAmelCase__			:				Any												=       compute_measures(lowercase__      ,					lowercase__      ,					truth_transform=lowercase__      ,					hypothesis_transform=lowercase__		)["wer"]
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(lowercase__      ,					lowercase__      ,					lowercase__="wer"		):
      UpperCAmelCase__			:				List[str]												=       0
      UpperCAmelCase__			:				List[str]												=       0
      for prediction, reference in zip(lowercase__      ,					lowercase__		):
        if score_type == "wer":
          UpperCAmelCase__			:				Dict												=       compute_measures(lowercase__      ,					lowercase__		)
        elif score_type == "cer":
          UpperCAmelCase__			:				Optional[int]												=       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

    return {"wer": compute_score(lowercase__      ,					lowercase__      ,					"wer"		), "cer": compute_score(lowercase__      ,					lowercase__      ,					"cer"		)}






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

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




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

   UpperCAmelCase__			:				List[str]												=       "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(snake_case__   ), "references": datasets.Value(snake_case__   )}   ) ,							codebase_urls=[] ,							reference_urls=[] ,							format="numpy" ,							)







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Union[str, Any] ,							snake_case__		:     str=None ,							snake_case__		:     Union[str, Any]=None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       bleu_kwargs if bleu_kwargs is not None else {}
   UpperCAmelCase__			:				List[str]												=       wer_kwargs if wer_kwargs is not None else {}

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





'''simple docstring'''





def snake_case_    (				lowercase__      ,					lowercase__ = 1_3		):
  UpperCAmelCase__			:				Optional[Any]												=       ""
  for c in s:
    if "A" <= c <= "Z":
      out += chr(ord("A"		) + (ord(lowercase__		) - ord("A"		) + n) % 2_6		)
    elif "a" <= c <= "z":
      out += chr(ord("a"		) + (ord(lowercase__		) - ord("a"		) + n) % 2_6		)
    else:
      out += c
  return out







def snake_case_    (				):
  UpperCAmelCase__			:				Dict												=       input("Enter message: "		)

  UpperCAmelCase__			:				Optional[int]												=       dencrypt(lowercase__      ,					1_3		)
  print("Encryption:"      ,					lowercase__		)

  UpperCAmelCase__			:				Union[str, Any]												=       dencrypt(lowercase__      ,					1_3		)
  print("Decryption: "      ,					lowercase__		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



