



"""simple docstring"""







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase:  str			     =				logging.get_logger(__name__)





@dataclass
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )				,indent=2   ) + "\n"







@dataclass(frozen=snake_case       )
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[int]
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Union[int, float]]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )   ) + "\n"







class   UpperCamelCase	:




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if len(self.get_labels()   ) > 1:
					_lowercase				: Optional[int]     =   self.get_labels()[int(example.label   )]
			return example






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			with open(UpperCAmelCase_				,"""r"""				,encoding="""utf-8-sig"""   ) as f:
					return list(csv.reader(UpperCAmelCase_				,delimiter="""\t"""				,quotechar=UpperCAmelCase_   )   )







class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_="classification"				,UpperCAmelCase_=False   ):
			_lowercase				: int     =   [] if labels is None else labels
			_lowercase				: int     =   [] if examples is None else examples
			_lowercase				: Union[str, Any]     =   mode
			_lowercase				: Optional[Any]     =   verbose






	def __len__(     self   ):
			return len(self.examples   )






	def __getitem__(     self				,UpperCAmelCase_   ):
			if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
					return SingleSentenceClassificationProcessor(labels=self.labels				,examples=self.examples[idx]   )
			return self.examples[idx]






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples_from_csv(
			    UpperCAmelCase_				,split_name=UpperCAmelCase_				,column_label=UpperCAmelCase_				,column_text=UpperCAmelCase_				,column_id=UpperCAmelCase_				,skip_first_row=UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_				,)
			return processor






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples(UpperCAmelCase_				,labels=UpperCAmelCase_   )
			return processor






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,):
			_lowercase				: Dict     =   self._read_tsv(UpperCAmelCase_   )
			if skip_first_row:
					_lowercase				: Union[str, Any]     =   lines[1:]
			_lowercase				: int     =   []
			_lowercase				: Optional[int]     =   []
			_lowercase				: int     =   []
			for i, line in enumerate(UpperCAmelCase_   ):
					texts.append(line[column_text]   )
					labels.append(line[column_label]   )
					if column_id is not None:
							ids.append(line[column_id]   )
					else:
							_lowercase				: Tuple     =   f"""{split_name}-{i}""" if split_name else str(UpperCAmelCase_   )
							ids.append(UpperCAmelCase_   )

			return self.add_examples(
			    UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False   ):
			if labels is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(
					    f"""Text and labels have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(f"""Text and ids have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is None:
					_lowercase				: Any     =   [None] * len(UpperCAmelCase_   )
			if labels is None:
					_lowercase				: List[str]     =   [None] * len(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   []
			_lowercase				: int     =   set()
			for text_or_text_and_label, label, guid in zip(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
					if isinstance(UpperCAmelCase_				,(tuple, list)   ) and label is None:
							_lowercase     ,  _lowercase				: Dict     =   text_or_text_and_label
					else:
							_lowercase				: Optional[int]     =   text_or_text_and_label
					added_labels.add(UpperCAmelCase_   )
					examples.append(InputExample(guid=UpperCAmelCase_				,text_a=UpperCAmelCase_				,text_b=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			# Update examples
			if overwrite_examples:
					_lowercase				: Any     =   examples
			else:
					self.examples.extend(UpperCAmelCase_   )

			# Update labels
			if overwrite_labels:
					_lowercase				: List[str]     =   list(UpperCAmelCase_   )
			else:
					_lowercase				: Optional[int]     =   list(set(self.labels   ).union(UpperCAmelCase_   )   )

			return self.examples






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=0				,UpperCAmelCase_=True				,UpperCAmelCase_=None				,):
			if max_length is None:
					_lowercase				: Any     =   tokenizer.max_len

			_lowercase				: List[Any]     =   {label: i for i, label in enumerate(self.labels   )}

			_lowercase				: Union[str, Any]     =   []
			for ex_index, example in enumerate(self.examples   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Tokenizing example {ex_index}"""   )

					_lowercase				: Optional[int]     =   tokenizer.encode(
					    example.text_a				,add_special_tokens=UpperCAmelCase_				,max_length=min(UpperCAmelCase_				,tokenizer.max_len   )				,)
					all_input_ids.append(UpperCAmelCase_   )

			_lowercase				: str     =   max(len(UpperCAmelCase_   ) for input_ids in all_input_ids   )

			_lowercase				: Optional[Any]     =   []
			for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase_				,self.examples   )   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Writing example {ex_index}/{len(self.examples   )}"""   )
					# The mask has 1 for real tokens and 0 for padding tokens. Only real
					# tokens are attended to.
					_lowercase				: Any     =   [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_   )

					# Zero-pad up to the sequence length.
					_lowercase				: Dict     =   batch_length - len(UpperCAmelCase_   )
					if pad_on_left:
							_lowercase				: Dict     =   ([pad_token] * padding_length) + input_ids
							_lowercase				: Any     =   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
					else:
							_lowercase				: Optional[Any]     =   input_ids + ([pad_token] * padding_length)
							_lowercase				: int     =   attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )
					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )

					if self.mode == "classification":
							_lowercase				: Dict     =   label_map[example.label]
					elif self.mode == "regression":
							_lowercase				: Optional[int]     =   float(example.label   )
					else:
							raise ValueError(self.mode   )

					if ex_index < 5 and self.verbose:
							logger.info("""*** Example ***"""   )
							logger.info(f"""guid: {example.guid}"""   )
							logger.info(f"""input_ids: {" ".join([str(UpperCAmelCase_   ) for x in input_ids]   )}"""   )
							logger.info(f"""attention_mask: {" ".join([str(UpperCAmelCase_   ) for x in attention_mask]   )}"""   )
							logger.info(f"""label: {example.label} (id = {label})"""   )

					features.append(InputFeatures(input_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			if return_tensors is None:
					return features
			elif return_tensors == "tf":
					if not is_tf_available():
							raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""   )
					import tensorflow as tf

					def gen():
							for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

					_lowercase				: Tuple     =   tf.data.Dataset.from_generator(
					    UpperCAmelCase_				,({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)				,({"""input_ids""": tf.TensorShape([None]   ), """attention_mask""": tf.TensorShape([None]   )}, tf.TensorShape([]   ))				,)
					return dataset
			elif return_tensors == "pt":
					if not is_torch_available():
							raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""   )
					import torch
					from torch.utils.data import TensorDataset

					_lowercase				: int     =   torch.tensor([f.input_ids for f in features]				,dtype=torch.long   )
					_lowercase				: str     =   torch.tensor([f.attention_mask for f in features]				,dtype=torch.long   )
					if self.mode == "classification":
							_lowercase				: Tuple     =   torch.tensor([f.label for f in features]				,dtype=torch.long   )
					elif self.mode == "regression":
							_lowercase				: Any     =   torch.tensor([f.label for f in features]				,dtype=torch.float   )

					_lowercase				: Any     =   TensorDataset(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
					return dataset
			else:
					raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""   )











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_     =			'\\n'

lowerCAmelCase_     =			'\\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'

lowerCAmelCase_     =			'\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'

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   UpperCamelCase			(					tr.AbstractTransform			):







							"""simple docstring"""



							def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							str = " "		)     -> Dict:


											'''simple docstring'''
											A									=   sentence_delimiter
							def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> int:


											'''simple docstring'''
											return list(_SCREAMING_SNAKE_CASE		)







							def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Tuple:


											'''simple docstring'''
											A									=   []
											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


					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   snake_case		(				UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Tuple ):
				return float((preds == labels).mean() )







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							Tuple ):
				return {
				    "f1": float(fa_score(y_true=UpperCAmelCase,    y_pred=UpperCAmelCase,    average='macro' ) ),
				    "accuracy": simple_accuracy(UpperCAmelCase,    UpperCAmelCase ),
				}







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[str],    UpperCAmelCase						:							List[Any]="exp",    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							int=False,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							str=False,    ):
				# xtreme-s can only have one label
				A									=   [[label] for label in labels]
				A									=   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"`.' )

				A									=   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' )
				A									=   [[refs[i] for refs in labels] for i in range(UpperCAmelCase )]
				A									=   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   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							Optional[int] ):
				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(UpperCAmelCase,    UpperCAmelCase )['wer']

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

								def compute_score(UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[Any]="wer" ):
												A									=   0
												A									=   0
												for prediction, reference in zip(UpperCAmelCase,    UpperCAmelCase ):
																if score_type == "wer":
																				A									=   compute_measures(UpperCAmelCase,    UpperCAmelCase )
																elif score_type == "cer":
																				A									=   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   UpperCamelCase			(					datasets.Metric			):







		"""simple docstring"""



		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		), 'references': datasets.Value(_SCREAMING_SNAKE_CASE		)}		)    ,codebase_urls=[]    ,reference_urls=[]    ,format='numpy'    ,)







		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Dict=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None		)     -> Tuple:


						'''simple docstring'''
						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(_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":
										A									=   bleu_kwargs.pop('smooth_method'    ,'exp'		)
										A									=   bleu_kwargs.pop('smooth_value'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('force'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('lowercase'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('tokenize'    ,_SCREAMING_SNAKE_CASE		)
										A									=   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"]:
										A									=   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}'		)





