




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)
__magic_name__     :     int  							=					"""\
"""

__magic_name__     :     str  							=					"""\
    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.
"""

__magic_name__     :     List[str]  							=					"""
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}
"""

__magic_name__     :     List[str]  							=					["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
__magic_name__     :     int  							=					""""""

try:
      from jiwer import transforms as tr

      __magic_name__     :     List[Any]  							=					True
except ImportError:
      __magic_name__     :     int  							=					False

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





      class 						SCREAMING_SNAKE_CASE__		(tr.AbstractTransform ):
           def __init__(	self  :		List[str]					,	__lowerCamelCase  :		str = " "	):



             """simple docstring"""
             lowerCAmelCase__								=			sentence_delimiter
           def 					A__			(	self  :		Any					,	__lowerCamelCase  :		str	):



             """simple docstring"""
             return list(__lowerCamelCase	)



           def 					A__			(	self  :		int					,	__lowerCamelCase  :		List[str]	):



             """simple docstring"""
             lowerCAmelCase__								=			[]
             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



      __magic_name__     :     List[str]  							=					tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      __magic_name__     :     Tuple  							=					tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      __magic_name__     :     str  							=					None
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):

  '''simple docstring'''





  return float((preds == labels).mean()							)
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):

  '''simple docstring'''





  return {
      "f1": float(fa_score(y_true=__lowerCAmelCase      ,   y_pred=__lowerCAmelCase      ,   average='''macro'''							)							),
      "accuracy": simple_accuracy(__lowerCAmelCase      ,   __lowerCAmelCase							),
  }
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase="exp"      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=False      ,   __lowerCAmelCase=False      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=False      ,   ):

  '''simple docstring'''





  lowerCAmelCase__								=			[[label] for label in labels]
  lowerCAmelCase__								=			list(__lowerCAmelCase							)
  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"`.'''							)

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

  '''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:
    lowerCAmelCase__								=			compute_measures(__lowerCAmelCase      ,   __lowerCAmelCase							)['''wer''']

    lowerCAmelCase__								=			compute_measures(__lowerCAmelCase      ,   __lowerCAmelCase      ,   truth_transform=__lowerCAmelCase      ,   hypothesis_transform=__lowerCAmelCase							)['''wer''']
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(__lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase="wer"							):
      lowerCAmelCase__								=			0
      lowerCAmelCase__								=			0
      for prediction, reference in zip(__lowerCAmelCase      ,   __lowerCAmelCase							):
        if score_type == "wer":
          lowerCAmelCase__								=			compute_measures(__lowerCAmelCase      ,   __lowerCAmelCase							)
        elif score_type == "cer":
          lowerCAmelCase__								=			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

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





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION )
class 						SCREAMING_SNAKE_CASE__		(datasets.Metric ):
     def 					A__			(	self  :		List[str]	):



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

       lowerCAmelCase__								=			'''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(__lowerCamelCase	), '''references''': datasets.Value(__lowerCamelCase	)}	)					,	codebase_urls=[]					,	reference_urls=[]					,	format='''numpy'''					,	)



     def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		int=None					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			bleu_kwargs if bleu_kwargs is not None else {}
       lowerCAmelCase__								=			wer_kwargs if wer_kwargs is not None else {}

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







def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_a={set_a}] is not a set"""
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_b={set_b}] is not a set"""

  return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
      from doctest import testmod

      testmod()

