



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

A_		:  str										=  '''\
    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.
'''

A_		:  Union[str, Any]										=  '''
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}
'''

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

try:
     from jiwer import transforms as tr

     A_		:  List[str]										=  True
except ImportError:
     A_		:  Dict										=  False

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



     class 		_lowercase		(							tr.AbstractTransform						):

        def __init__(   self      :							List[Any]				, __lowerCAmelCase      :							str = " "				)							->					int:


         """simple docstring"""






         a							=		sentence_delimiter

        def 		A				(   self      :							Any				, __lowerCAmelCase      :							str				)							->					int:


         """simple docstring"""






         return list(__lowerCAmelCase				)




        def 		A				(   self      :							int				, __lowerCAmelCase      :							List[str]				)							->					Dict:


         """simple docstring"""






         a							=		[]
         for sent_idx, sentence in enumerate(__lowerCAmelCase				):
          chars.extend(self.process_string(__lowerCAmelCase				)				)
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCAmelCase				) - 1:
           chars.append(self.sentence_delimiter				)
         return chars

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


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:List[Any]       ):






 '''simple docstring'''

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


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Dict			,       UpperCAmelCase__			:List[str]       ):






 '''simple docstring'''

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


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:List[str]="exp"			,       UpperCAmelCase__			:Dict=None			,       UpperCAmelCase__			:List[str]=False			,       UpperCAmelCase__			:int=False			,       UpperCAmelCase__			:List[Any]=None			,       UpperCAmelCase__			:str=False			,       ):






 '''simple docstring'''

 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 						UpperCAmelCase__   (   UpperCAmelCase__			:Dict			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			: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:
  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__			:str			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:Optional[int]="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 		_lowercase		(							datasets.Metric						):

   def 		A				(   self      :							Union[str, Any]				)							->					Any:


    """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(__lowerCAmelCase				), "references": datasets.Value(__lowerCAmelCase				)}				)				, codebase_urls=[]				, reference_urls=[]				, format="numpy"				, )




   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Optional[Any]=None				, __lowerCAmelCase      :							Dict=None				)							->					Optional[int]:


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_		:  Any										=  {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:  List[Any]										=  [
         '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VideoMAEForPreTraining''',
         '''VideoMAEModel''',
         '''VideoMAEPreTrainedModel''',
         '''VideoMAEForVideoClassification''',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:  str										=  ['''VideoMAEFeatureExtractor''']
     A_		:  Union[str, Any]										=  ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
     from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_videomae import (
              VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              VideoMAEForPreTraining,
              VideoMAEForVideoClassification,
              VideoMAEModel,
              VideoMAEPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_videomae import VideoMAEFeatureExtractor
          from .image_processing_videomae import VideoMAEImageProcessor

else:
     import sys

     A_		:  Dict										=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

