




'''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)
UpperCAmelCase_				:     Optional[Any]     			=						'''\
'''

UpperCAmelCase_				:     Any     			=						'''\
    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.
'''

UpperCAmelCase_				:     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}
'''

UpperCAmelCase_				:     Any     			=						['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
UpperCAmelCase_				:     Dict     			=						''''''

try:
     from jiwer import transforms as tr

     UpperCAmelCase_				:     Union[str, Any]     			=						True
except ImportError:
     UpperCAmelCase_				:     List[Any]     			=						False

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





     class 			lowerCAmelCase      (       tr.AbstractTransform):


            def __init__(      self			,     __SCREAMING_SNAKE_CASE = " "     )					->     Optional[Any]:



                 '''simple docstring'''




                 __snake_case			    =     sentence_delimiter


            def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Optional[int]:



                 '''simple docstring'''




                 return list(__SCREAMING_SNAKE_CASE     )




            def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



                 '''simple docstring'''




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





     UpperCAmelCase_				:     Dict     			=						tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
elif _jiwer_available:
     UpperCAmelCase_				:     str     			=						tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )
else:
     UpperCAmelCase_				:     Optional[Any]     			=						None







def 	_UpperCamelCase       (_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   Union[str, Any]		)->      Optional[Any]:




     '''simple docstring'''





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







def 	_UpperCamelCase       (_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   Optional[Any]		)->      List[str]:




     '''simple docstring'''





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







def 	_UpperCamelCase       (_lowerCamelCase							:   List[Any]      ,		_lowerCamelCase							:   int      ,		_lowerCamelCase							:   int="exp"      ,		_lowerCamelCase							:   str=None      ,		_lowerCamelCase							:   Union[str, Any]=False      ,		_lowerCamelCase							:   str=False      ,		_lowerCamelCase							:   List[Any]=None      ,		_lowerCamelCase							:   Optional[int]=False      ,		)->      int:




     '''simple docstring'''





     __snake_case			    =     [[label] for label in labels]
     __snake_case			    =     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"`.'''		)

     __snake_case			    =     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'''		)
     __snake_case			    =     [[refs[i] for refs in labels] for i in range(_lowerCamelCase		)]
     __snake_case			    =     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 	_UpperCamelCase       (_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   int      ,		_lowerCamelCase							:   Any      ,		_lowerCamelCase							:   Optional[int]		)->      Optional[Any]:




     '''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:
          __snake_case			    =     compute_measures(_lowerCamelCase      ,		_lowerCamelCase		)['''wer''']

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

          def compute_score(_lowerCamelCase							:   Optional[int]      ,		_lowerCamelCase							:   List[Any]      ,		_lowerCamelCase							:   Dict="wer"		):
               __snake_case			    =     0
               __snake_case			    =     0
               for prediction, reference in zip(_lowerCamelCase      ,		_lowerCamelCase		):
                    if score_type == "wer":
                         __snake_case			    =     compute_measures(_lowerCamelCase      ,		_lowerCamelCase		)
                    elif score_type == "cer":
                         __snake_case			    =     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 			lowerCAmelCase      (       datasets.Metric):


       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




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

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




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None     )					->     int:



            '''simple docstring'''




            __snake_case			    =     bleu_kwargs if bleu_kwargs is not None else {}
            __snake_case			    =     wer_kwargs if wer_kwargs is not None else {}

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












'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase_				:     List[Any]     			=						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase_				:     Union[str, Any]     			=						'''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''





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


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                        '''references''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                    }
                    if self.config_name == '''multilabel'''
                    else {
                        '''predictions''': datasets.Value('''int32'''     ),
                        '''references''': datasets.Value('''int32'''     ),
                    }     )			,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']			,     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






