code: 
  
  
  
  
  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)
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					"\\n"
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					"\\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"
  
  SCREAMING_SNAKE_CASE__    :    Optional[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"
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
  SCREAMING_SNAKE_CASE__    :    int								=					""
  
  try:
  				from jiwer import transforms as tr
  
  				SCREAMING_SNAKE_CASE__    :    List[str]								=					True
  except ImportError:
  				SCREAMING_SNAKE_CASE__    :    Dict								=					False
  
  if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
  
  
  
  				class 			a_					(    tr.AbstractTransform    ):
  
  
  
  
  
  					def __init__(       self			,    SCREAMING_SNAKE_CASE = " "			)     ->				Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  								SCREAMING_SNAKE_CASE_			=	sentence_delimiter
  
  
  
  
  
  					def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  								return list(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  					def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  								SCREAMING_SNAKE_CASE_			=	[]
  								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
  
  
  
  
  				SCREAMING_SNAKE_CASE__    :    str								=					tr.Compose(
  				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  				)
  elif _jiwer_available:
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					tr.Compose(
  				    [
  				        tr.RemoveMultipleSpaces(),
  				        tr.Strip(),
  				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
  				        tr.ReduceToListOfListOfChars(),
  				    ]
  				)
  else:
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					None
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return float((preds == labels).mean()   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''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 lowercase						(     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'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[label] for label in labels]
  			SCREAMING_SNAKE_CASE_			=	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"`.'   )
  
  			SCREAMING_SNAKE_CASE_			=	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'   )
  			SCREAMING_SNAKE_CASE_			=	[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE   )]
  			SCREAMING_SNAKE_CASE_			=	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 lowercase						(     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:
  						SCREAMING_SNAKE_CASE_			=	compute_measures(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )['wer']
  
  						SCREAMING_SNAKE_CASE_			=	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"   ):
  									SCREAMING_SNAKE_CASE_			=	0
  									SCREAMING_SNAKE_CASE_			=	0
  									for prediction, reference in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ):
  												if score_type == "wer":
  															SCREAMING_SNAKE_CASE_			=	compute_measures(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  												elif score_type == "cer":
  															SCREAMING_SNAKE_CASE_			=	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    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.config_name not in _CONFIG_NAMES:
  							raise KeyError(f'You should supply a configuration name selected in {_CONFIG_NAMES}'			)
  
  				SCREAMING_SNAKE_CASE_			=	'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			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	bleu_kwargs if bleu_kwargs is not None else {}
  				SCREAMING_SNAKE_CASE_			=	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":
  							SCREAMING_SNAKE_CASE_			=	bleu_kwargs.pop('smooth_method'			,    'exp'			)
  							SCREAMING_SNAKE_CASE_			=	bleu_kwargs.pop('smooth_value'			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	bleu_kwargs.pop('force'			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	bleu_kwargs.pop('lowercase'			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	bleu_kwargs.pop('tokenize'			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	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"]:
  							SCREAMING_SNAKE_CASE_			=	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}'			)
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  