






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			=   {
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCamelCase__			=   [
          "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "UniSpeechSatForAudioFrameClassification",
          "UniSpeechSatForCTC",
          "UniSpeechSatForPreTraining",
          "UniSpeechSatForSequenceClassification",
          "UniSpeechSatForXVector",
          "UniSpeechSatModel",
          "UniSpeechSatPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_unispeech_sat import (
                UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                UniSpeechSatForAudioFrameClassification,
                UniSpeechSatForCTC,
                UniSpeechSatForPreTraining,
                UniSpeechSatForSequenceClassification,
                UniSpeechSatForXVector,
                UniSpeechSatModel,
                UniSpeechSatPreTrainedModel,
            )

else:
      import sys

      lowerCamelCase__			=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












"""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)
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):






          def __init__(self						,      lowerCamelCase__ = " "   ):


            """simple docstring"""
            A__											=							sentence_delimiter






          def  A    (self						,      lowerCamelCase__   ):


            """simple docstring"""
            return list(lowerCamelCase__   )






          def  A    (self						,      lowerCamelCase__   ):


            """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
      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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  return float((preds == labels).mean()    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	str    ):
  return {
      "f1": float(fa_score(y_true=UpperCamelCase		,				y_pred=UpperCamelCase		,				average="""macro"""    )    ),
      "accuracy": simple_accuracy(UpperCamelCase		,				UpperCamelCase    ),
  }






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






    def  A    (self   ):


      """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						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None   ):


      """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}"""   )




