

"""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)
_a					 =				'\\n'

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

_a					 =				'\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

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

try:
   from jiwer import transforms as tr

   _a					 =				True
except ImportError:
   _a					 =				False

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




   class    _lowerCAmelCase    (      tr.AbstractTransform  ):







    """simple docstring"""







    def __init__(     self				:     int,   UpperCAmelCase__				:     str = " "      ):
       __lowercase								=		sentence_delimiter


    def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     str      ):
       return list(UpperCAmelCase__      )



    def    _lowercase					(     self				:     int,   UpperCAmelCase__				:     List[str]      ):
       __lowercase								=		[]
       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







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




def     _A    (    UpperCamelCase_	:			Dict,     UpperCamelCase_	:			Optional[int])      ->		Union[str, Any]:
   '''simple docstring'''


   return float((preds == labels).mean())




def     _A    (    UpperCamelCase_	:			Union[str, Any],     UpperCamelCase_	:			str)      ->		str:
   '''simple docstring'''


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




def     _A    (    UpperCamelCase_	:			Dict,     UpperCamelCase_	:			Any,     UpperCamelCase_	:			Optional[Any]="exp",     UpperCamelCase_	:			Optional[Any]=None,     UpperCamelCase_	:			Dict=False,     UpperCamelCase_	:			Any=False,     UpperCamelCase_	:			Any=None,     UpperCamelCase_	:			Optional[int]=False,     )      ->		Any:
   '''simple docstring'''


   __lowercase								=		[[label] for label in labels]
   __lowercase								=		list(UpperCamelCase_)
   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\"`.")

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




def     _A    (    UpperCamelCase_	:			Dict,     UpperCamelCase_	:			List[Any],     UpperCamelCase_	:			Dict,     UpperCamelCase_	:			Tuple)      ->		Tuple:
   '''simple docstring'''


   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:
      __lowercase								=		compute_measures(UpperCamelCase_,     UpperCamelCase_)["wer"]

      __lowercase								=		compute_measures(UpperCamelCase_,     UpperCamelCase_,     truth_transform=UpperCamelCase_,     hypothesis_transform=UpperCamelCase_)["wer"]
      return {"wer": wer, "cer": cer}
   else:

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

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




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







 """simple docstring"""







 def    _lowercase					(     self				:     Union[str, Any]      ):
    if self.config_name not in _CONFIG_NAMES:
       raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""      )

    __lowercase								=		"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    _lowercase					(     self				:     Dict,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     List[str]=None,   UpperCAmelCase__				:     List[str]=None      ):
    __lowercase								=		bleu_kwargs if bleu_kwargs is not None else {}
    __lowercase								=		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":
       __lowercase								=		bleu_kwargs.pop("smooth_method",   "exp"      )
       __lowercase								=		bleu_kwargs.pop("smooth_value",   UpperCAmelCase__      )
       __lowercase								=		bleu_kwargs.pop("force",   UpperCAmelCase__      )
       __lowercase								=		bleu_kwargs.pop("lowercase",   UpperCAmelCase__      )
       __lowercase								=		bleu_kwargs.pop("tokenize",   UpperCAmelCase__      )
       __lowercase								=		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"]:
       __lowercase								=		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}"""      )







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			list)      ->		list:
   '''simple docstring'''


   for i, _ in enumerate(UpperCamelCase_):
      if (i % 2 == 1) == (nums[i - 1] > nums[i]):
         __lowercase						,__lowercase								=		nums[i], nums[i - 1]

   return nums


if __name__ == "__main__":
   print('Enter the array elements:')
   _a					 =				list(map(int, input().split()))
   print('The unsorted array is:')
   print(array)
   print('Array after Wiggle sort:')
   print(wiggle_sort(array))




