





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)
lowerCAmelCase_												=       '''\
'''

lowerCAmelCase_												=       '''\
    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.
'''

lowerCAmelCase_												=       '''
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}
'''

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

try:
 from jiwer import transforms as tr

 lowerCAmelCase_												=       True
except ImportError:
 lowerCAmelCase_												=       False

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



 class       __lowerCAmelCase (    tr.AbstractTransform  ):






    def __init__(self      ,							__magic_name__ = " "					)  ->     List[str]:


        '''simple docstring'''



        snake_case_		:					Dict			    =						sentence_delimiter






    def     lowerCamelCase						(self      ,							__magic_name__					)  ->     int:


        '''simple docstring'''



        return list(__magic_name__					)






    def     lowerCamelCase						(self      ,							__magic_name__					)  ->     str:


        '''simple docstring'''



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




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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Any:






    """simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Tuple:






    """simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase="exp"				,       _UpperCamelCase=None				,       _UpperCamelCase=False				,       _UpperCamelCase=False				,       _UpperCamelCase=None				,       _UpperCamelCase=False				,       )	->		Any:






    """simple docstring"""


    snake_case_		:					Any			    =						[[label] for label in labels]
    snake_case_		:					int			    =						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"`.'''				)

    snake_case_		:					Optional[int]			    =						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'''				)
    snake_case_		:					Optional[Any]			    =						[[refs[i] for refs in labels] for i in range(_UpperCamelCase				)]
    snake_case_		:					List[Any]			    =						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   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)	->		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:
        snake_case_		:					Dict			    =						compute_measures(_UpperCamelCase				,       _UpperCamelCase				)['''wer''']

        snake_case_		:					Optional[int]			    =						compute_measures(_UpperCamelCase				,       _UpperCamelCase				,       truth_transform=_UpperCamelCase				,       hypothesis_transform=_UpperCamelCase				)['''wer''']
        return {"wer": wer, "cer": cer}
    else:

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






   def     lowerCamelCase						(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}'''					)

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






   def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__=None      ,							__magic_name__=None					)  ->     int:


       '''simple docstring'''



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

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







class       __lowerCAmelCase :






			def __init__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						[]
							snake_case_		:					Any			    =						0
							snake_case_		:					Optional[int]			    =						0






			def __str__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					int			    =						'''<''' + str(self.entries					)[1:-1] + '''>'''
							return printed






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							self.entries.append(__magic_name__					)
							snake_case_		:					List[str]			    =						self.length + 1






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.length - 1
							snake_case_		:					Dict			    =						self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							snake_case_		:					List[str]			    =						self.entries[1:]
							return dequeued






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							for _ in range(__magic_name__					):
											self.put(self.get()					)






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


							'''simple docstring'''



							return self.entries[0]






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


							'''simple docstring'''



							return self.length
