

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_						:     Union[str, Any]			  =				'\\n'

A_						:     List[str]			  =				'\\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_						:     Union[str, Any]			  =				'\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_						:     Optional[Any]			  =				['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
A_						:     List[Any]			  =				''

try:
							from jiwer import transforms as tr

							A_						:     Tuple			  =				True
except ImportError:
							A_						:     List[Any]			  =				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       , lowercase__ = " "					)       -> Optional[int]:
															__UpperCAmelCase			   =     sentence_delimiter


											def        lowerCAmelCase_  (self       , lowercase__					)       -> Tuple:
															return list(lowercase__					)






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






							A_						:     List[Any]			  =				tr.Compose(
							    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
							)
elif _jiwer_available:
							A_						:     Union[str, Any]			  =				tr.Compose(
							    [
							        tr.RemoveMultipleSpaces(),
							        tr.Strip(),
							        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
							        tr.ReduceToListOfListOfChars(),
							    ]
							)
else:
							A_						:     Union[str, Any]			  =				None


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






				'''simple docstring'''
				return float((preds == labels).mean() )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE="exp"    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=False    ,				SCREAMING_SNAKE_CASE=False    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=False    ,				) ->       List[Any]:






				'''simple docstring'''
				__UpperCAmelCase			   =     [[label] for label in labels]
				__UpperCAmelCase			   =     list(SCREAMING_SNAKE_CASE )
				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"`.''' )

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






				'''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:
								__UpperCAmelCase			   =     compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )['''wer''']

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

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

								return {"wer": compute_score(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				'''wer''' ), "cer": compute_score(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				'''cer''' )}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,       _KWARGS_DESCRIPTION  )
class    A_				( datasets.Metric  ):


				'''simple docstring'''






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

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






				def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__=None       , lowercase__=None					)       -> Union[str, Any]:
								__UpperCAmelCase			   =     bleu_kwargs if bleu_kwargs is not None else {}
								__UpperCAmelCase			   =     wer_kwargs if wer_kwargs is not None else {}

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







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




