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)
  __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        lowerCAmelCase_	(						tr.AbstractTransform							):
             def __init__(    self,    SCREAMING_SNAKE_CASE_ = " " )  ->	int:
                UpperCamelCase					:						Optional[Any]					=			sentence_delimiter
             def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	str:
                return list(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
             def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	List[Any]:
                UpperCamelCase					:						int					=			[]
                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
  
  
  
  
  
  
      __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 	UpperCamelCase			(     snake_case__ :					Optional[Any] ,  snake_case__ :					Dict			)			->  Optional[Any]:
     return float((preds == labels).mean()			)
  def 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					Optional[int]			)			->  List[Any]:
     return {
         "f1": float(fa_score(y_true=snake_case__ ,  y_pred=snake_case__ ,  average='macro'			)			),
         "accuracy": simple_accuracy(snake_case__ ,  snake_case__			),
     }
  def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					Dict ,  snake_case__ :					List[str]="exp" ,  snake_case__ :					Optional[int]=None ,  snake_case__ :					Dict=False ,  snake_case__ :					List[str]=False ,  snake_case__ :					Any=None ,  snake_case__ :					List[Any]=False ,  )			->  str:
     # xtreme-s can only have one label
     UpperCamelCase					:						List[Any]					=			[[label] for label in labels]
     UpperCamelCase					:						List[Any]					=			list(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					:						str					=			len(labels[0]			)
     if any(len(snake_case__			) != references_per_prediction for refs in labels			):
        raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
     UpperCamelCase					:						Any					=			[[refs[i] for refs in labels] for i in range(snake_case__			)]
     UpperCamelCase					:						Tuple					=			scb.corpus_bleu(
         snake_case__ ,  snake_case__ ,  smooth_method=snake_case__ ,  smooth_value=snake_case__ ,  force=snake_case__ ,  lowercase=snake_case__ ,  use_effective_order=snake_case__ ,  **({'tokenize': tokenize} if tokenize else {}) ,  )
     return {"bleu": output.score}
  def 	UpperCamelCase			(     snake_case__ :					Tuple ,  snake_case__ :					List[str] ,  snake_case__ :					Optional[int] ,  snake_case__ :					Any			)			->  Dict:
     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					:						Dict					=			compute_measures(snake_case__ ,  snake_case__			)['wer']
  
        UpperCamelCase					:						Union[str, Any]					=			compute_measures(snake_case__ ,  snake_case__ ,  truth_transform=snake_case__ ,  hypothesis_transform=snake_case__			)['wer']
        return {"wer": wer, "cer": cer}
     else:
  
        def compute_score(snake_case__ :					Optional[int] ,  snake_case__ :					Union[str, Any] ,  snake_case__ :					Any="wer"			):
           UpperCamelCase					:						Union[str, Any]					=			0
           UpperCamelCase					:						Optional[int]					=			0
           for prediction, reference in zip(snake_case__ ,  snake_case__			):
              if score_type == "wer":
                 UpperCamelCase					:						Optional[Any]					=			compute_measures(snake_case__ ,  snake_case__			)
              elif score_type == "cer":
                 UpperCamelCase					:						List[Any]					=			compute_measures(
                     snake_case__ ,  snake_case__ ,  truth_transform=snake_case__ ,  hypothesis_transform=snake_case__			)
              incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
              total += measures["substitutions"] + measures["deletions"] + measures["hits"]
           return incorrect / total
  
        return {"wer": compute_score(snake_case__ ,  snake_case__ ,  'wer'			), "cer": compute_score(snake_case__ ,  snake_case__ ,  'cer'			)}
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,					_KWARGS_DESCRIPTION							)
  class        lowerCAmelCase_	(						datasets.Metric							):
         def       snake_case_ (    self )  ->	Dict:
            if self.config_name not in _CONFIG_NAMES:
               raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}""" )
  
            UpperCamelCase					:						str					=			'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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None )  ->	Union[str, Any]:
            UpperCamelCase					:						Optional[Any]					=			bleu_kwargs if bleu_kwargs is not None else {}
            UpperCamelCase					:						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":
               UpperCamelCase					:						List[Any]					=			bleu_kwargs.pop('smooth_method',    'exp' )
               UpperCamelCase					:						str					=			bleu_kwargs.pop('smooth_value',    SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						Optional[int]					=			bleu_kwargs.pop('force',    SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						str					=			bleu_kwargs.pop('lowercase',    SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						Optional[Any]					=			bleu_kwargs.pop('tokenize',    SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						List[str]					=			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"]:
               UpperCamelCase					:						Optional[int]					=			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: 
  
  
  
  import json
  import logging
  import os
  import subprocess
  from argparse import ArgumentParser
  
  
  __UpperCAmelCase      = logging.getLogger(__name__)
  def 	UpperCamelCase			(     )			->  List[str]:
     UpperCamelCase					:						Union[str, Any]					=			ArgumentParser()
     UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			parser.parse_known_args()
     for arg in unknown:
        if arg.startswith(('-', '--')			):
           parser.add_argument(arg.split('='			)[0]			)
  
     return parser.parse_args()
  def 	UpperCamelCase			(     )			->  Optional[Any]:
     UpperCamelCase					:						int					=			parse_args()
     UpperCamelCase					:						Dict					=			8888
     UpperCamelCase					:						Union[str, Any]					=			int(os.environ['SM_NUM_GPUS']			)
     UpperCamelCase					:						Optional[Any]					=			json.loads(os.environ['SM_HOSTS']			)
     UpperCamelCase					:						Dict					=			len(snake_case__			)
     UpperCamelCase					:						int					=			os.environ['SM_CURRENT_HOST']
     UpperCamelCase					:						List[str]					=			hosts.index(snake_case__			)
     UpperCamelCase					:						Optional[Any]					=			'INFO'
  
     if num_nodes > 1:
        UpperCamelCase					:						List[Any]					=			F"""python -m torch.distributed.launch \
                  --nnodes={num_nodes}  \
                  --node_rank={rank}  \
                  --nproc_per_node={num_gpus}  \
                  --master_addr={hosts[0]}  \
                  --master_port={port} \
                  ./run_glue.py \
                  {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     else:
        UpperCamelCase					:						Any					=			F"""python -m torch.distributed.launch \
              --nproc_per_node={num_gpus}  \
              ./run_glue.py \
              {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     try:
        subprocess.run(snake_case__ ,  shell=snake_case__			)
     except Exception as e:
        logger.info(snake_case__			)
  
  
  if __name__ == "__main__":
      main()
  
  
  
  