


'''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    				=							'''\
'''

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

try:
		from jiwer import transforms as tr

		__UpperCAmelCase    				=							True
except ImportError:
		__UpperCAmelCase    				=							False

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




		class 		a__				(							tr.AbstractTransform  ):





									'''simple docstring'''






									def __init__(		self					,    lowerCamelCase_ = " "   )       ->							Tuple:
																lowerCAmelCase__          =	sentence_delimiter







									def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							int:
																return list(lowerCamelCase_   )







									def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Any:
																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

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


def 					_snake_case   (     A ,    A       )						->    Optional[int]:
							return float((preds == labels).mean()       )


def 					_snake_case   (     A ,    A       )						->    Tuple:
							return {
							    "f1": float(fa_score(y_true=A ,    y_pred=A ,    average='''macro'''       )       ),
							    "accuracy": simple_accuracy(A ,    A       ),
							}


def 					_snake_case   (     A ,    A ,    A="exp" ,    A=None ,    A=False ,    A=False ,    A=None ,    A=False ,    )						->    Optional[int]:
							# xtreme-s can only have one label
							lowerCAmelCase__          =	[[label] for label in labels]
							lowerCAmelCase__          =	list(A       )
							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(A       ) != 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(A       )]
							lowerCAmelCase__          =	scb.corpus_bleu(
							    A ,    A ,    smooth_method=A ,    smooth_value=A ,    force=A ,    lowercase=A ,    use_effective_order=A ,    **({'''tokenize''': tokenize} if tokenize else {}) ,    )
							return {"bleu": output.score}


def 					_snake_case   (     A ,    A ,    A ,    A       )						->    List[str]:
							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(A ,    A       )['''wer''']

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

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

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






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





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_=None					,    lowerCamelCase_=None   )       ->							List[str]:
														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}"""   )



'''simple docstring'''






def 					_snake_case   (     A ,    A       )						->    float:
							return a + b


if __name__ == "__main__":
		__UpperCAmelCase    				=							5
		__UpperCAmelCase    				=							6
		print(f"""The sum of {a} + {b} is {add(a, b)}""")