




"""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)
_lowercase							=			"""\
"""

_lowercase							=			"""\
    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.
"""

_lowercase							=			"""
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}
"""

_lowercase							=			["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
_lowercase							=			""""""

try:
							from jiwer import transforms as tr

							_lowercase							=			True
except ImportError:
							_lowercase							=			False

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

							class 					__lowercase				(	tr.AbstractTransform		):


											'''simple docstring'''


											def __init__(      self      :   Optional[Any]					,SCREAMING_SNAKE_CASE_      :   Optional[Any] = " "					):
																	snake_case__				:    Tuple								=    sentence_delimiter

											def 					_lowerCamelCase    (      self      :   str					,SCREAMING_SNAKE_CASE_      :   Union[str, Any]					):
																	return list(SCREAMING_SNAKE_CASE_					)





											def 					_lowerCamelCase    (      self      :   str					,SCREAMING_SNAKE_CASE_      :   List[Any]					):
																	snake_case__				:    List[str]								=    []
																	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







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

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase						):
						'''simple docstring'''


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

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase						):
						'''simple docstring'''


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

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase="exp"					,	lowerCamelCase=None					,	lowerCamelCase=False					,	lowerCamelCase=False					,	lowerCamelCase=None					,	lowerCamelCase=False					,	):
						'''simple docstring'''


						snake_case__				:    List[Any]								=    [[label] for label in labels]
						snake_case__				:    Any								=    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__				:    Tuple								=    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__				:    int								=    [[refs[i] for refs in labels] for i in range(__lowerCamelCase						)]
						snake_case__				:    List[str]								=    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 						lowerCAmelCase							(       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:
												snake_case__				:    Any								=    compute_measures(__lowerCamelCase					,	__lowerCamelCase						)['''wer''']

												snake_case__				:    Any								=    compute_measures(__lowerCamelCase					,	__lowerCamelCase					,	truth_transform=__lowerCamelCase					,	hypothesis_transform=__lowerCamelCase						)['''wer''']
												return {"wer": wer, "cer": cer}
						else:

												def compute_score(lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase="wer"						):
																		snake_case__				:    List[str]								=    0
																		snake_case__				:    List[Any]								=    0
																		for prediction, reference in zip(__lowerCamelCase					,	__lowerCamelCase						):
																								if score_type == "wer":
																														snake_case__				:    Dict								=    compute_measures(__lowerCamelCase					,	__lowerCamelCase						)
																								elif score_type == "cer":
																														snake_case__				:    Optional[Any]								=    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 					__lowercase				(	datasets.Metric		):


				'''simple docstring'''


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

										snake_case__				:    Tuple								=    '''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      :   str					,SCREAMING_SNAKE_CASE_      :   int					,SCREAMING_SNAKE_CASE_      :   List[Any]					,SCREAMING_SNAKE_CASE_      :   Optional[Any]=None					,SCREAMING_SNAKE_CASE_      :   int=None					):
										snake_case__				:    Any								=    bleu_kwargs if bleu_kwargs is not None else {}
										snake_case__				:    Dict								=    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__				:    Any								=    bleu_kwargs.pop('''smooth_method'''					,'''exp'''					)
																snake_case__				:    List[Any]								=    bleu_kwargs.pop('''smooth_value'''					,SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Tuple								=    bleu_kwargs.pop('''force'''					,SCREAMING_SNAKE_CASE_					)
																snake_case__				:    List[Any]								=    bleu_kwargs.pop('''lowercase'''					,SCREAMING_SNAKE_CASE_					)
																snake_case__				:    int								=    bleu_kwargs.pop('''tokenize'''					,SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Dict								=    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__				:    Optional[Any]								=    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}'''					)



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_				:		List[str]       						=       """\
"""

lowerCamelCase_				:		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.
"""

lowerCamelCase_				:		Tuple       						=       """
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_				:		Dict       						=       ["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
lowerCamelCase_				:		Optional[Any]       						=       """"""

try:
	from jiwer import transforms as tr

	lowerCamelCase_				:		str       						=       True
except ImportError:
	lowerCamelCase_				:		List[Any]       						=       False

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


	class 						a__							(   tr.AbstractTransform		):



				def __init__(						self      ,						UpperCAmelCase = " "    ) ->    List[str]:
									__a							=   sentence_delimiter



				def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
									return list(UpperCAmelCase    )







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






	lowerCamelCase_				:		Any       						=       tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
elif _jiwer_available:
	lowerCamelCase_				:		Optional[Any]       						=       tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)
else:
	lowerCamelCase_				:		Tuple       						=       None
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return float((preds == labels).mean()						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return {
					    "f1": float(fa_score(y_true=__lowerCamelCase      ,				y_pred=__lowerCamelCase      ,				average='macro'						)						),
					    "accuracy": simple_accuracy(__lowerCamelCase      ,				__lowerCamelCase						),
					}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="exp"      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				__lowerCamelCase=False      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				):
					# xtreme-s can only have one label
					__a							=   [[label] for label in labels]
					__a							=   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"`.'						)

					__a							=   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'						)
					__a							=   [[refs[i] for refs in labels] for i in range(__lowerCamelCase						)]
					__a							=   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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					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:
										__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)['wer']

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

										def compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="wer"						):
															__a							=   0
															__a							=   0
															for prediction, reference in zip(__lowerCamelCase      ,				__lowerCamelCase						):
																				if score_type == "wer":
																									__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)
																				elif score_type == "cer":
																									__a							=   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 						a__							(   datasets.Metric		):



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

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







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None    ) ->    Optional[Any]:
								__a							=   bleu_kwargs if bleu_kwargs is not None else {}
								__a							=   wer_kwargs if wer_kwargs is not None else {}

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




