






"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCamelCase__			=   logging.get_logger(__name__)







class 					_UpperCamelCase		( lowerCAmelCase_):
				__lowerCamelCase         =  ["input_values", "attention_mask"]






				def __init__(self						,      lowerCamelCase__=1						,      lowerCamelCase__=1_6_0_0_0						,      lowerCamelCase__=1_2_8						,      lowerCamelCase__=1_0_2_4						,      lowerCamelCase__=0.0						,      lowerCamelCase__=True						,      lowerCamelCase__=-4.2_6_7_7_3_9_3						,      lowerCamelCase__=4.5_6_8_9_9_7_4						,      lowerCamelCase__=False						,      **lowerCamelCase__						,      ):


						"""simple docstring"""
						super().__init__(feature_size=__a						,      sampling_rate=__a						,      padding_value=__a						,      **__a   )
						A__											=							num_mel_bins
						A__											=							max_length
						A__											=							do_normalize
						A__											=							mean
						A__											=							std
						A__											=							return_attention_mask






				def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


						"""simple docstring"""
						A__											=							torch.from_numpy(__a   ).unsqueeze(0   )
						A__											=							ta_kaldi.fbank(
						    __a						,      htk_compat=__a						,      sample_frequency=self.sampling_rate						,      use_energy=__a						,      window_type="""hanning"""						,      num_mel_bins=self.num_mel_bins						,      dither=0.0						,      frame_shift=1_0						,      )

						A__											=							fbank.shape[0]
						A__											=							max_length - n_frames

						# pad or truncate, depending on difference
						if difference > 0:
								A__											=							torch.nn.ZeroPadad((0, 0, 0, difference)   )
								A__											=							pad_module(__a   )
						elif difference < 0:
								A__											=							fbank[0:max_length, :]

						A__											=							fbank.numpy()

						return fbank






				def  A    (self						,      lowerCamelCase__   ):


						"""simple docstring"""
						return (input_values - (self.mean)) / (self.std * 2)






				def __call__(self						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      **lowerCamelCase__						,      ):


						"""simple docstring"""





						if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
										    F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
										    F""" {self.sampling_rate} and not {sampling_rate}."""   )
						else:
								logger.warning(
								    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
								    """Failing to do so can result in silent errors that might be hard to debug."""   )

						A__											=							isinstance(__a						,      np.ndarray   ) and len(raw_speech.shape   ) > 1
						if is_batched_numpy and len(raw_speech.shape   ) > 2:
								raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
						A__											=							is_batched_numpy or (
						    isinstance(__a						,      (list, tuple)   ) and (isinstance(raw_speech[0]						,      (np.ndarray, tuple, list)   ))
						)

						if is_batched:
								A__											=							[np.asarray(__a						,      dtype=np.floataa   ) for speech in raw_speech]
						elif not is_batched and not isinstance(__a						,      np.ndarray   ):
								A__											=							np.asarray(__a						,      dtype=np.floataa   )
						elif isinstance(__a						,      np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
								A__											=							raw_speech.astype(np.floataa   )

						# always return batch
						if not is_batched:
								A__											=							[raw_speech]

						# extract fbank features and pad/truncate to max_length
						A__											=							[self._extract_fbank_features(__a						,      max_length=self.max_length   ) for waveform in raw_speech]

						# convert into BatchFeature
						A__											=							BatchFeature({"""input_values""": features}   )

						# make sure list is in array format
						A__											=							padded_inputs.get("""input_values"""   )
						if isinstance(input_values[0]						,      __a   ):
								A__											=							[np.asarray(__a						,      dtype=np.floataa   ) for feature in input_values]

						# normalization
						if self.do_normalize:
								A__											=							[self.normalize(__a   ) for feature in input_values]

						if return_tensors is not None:
								A__											=							padded_inputs.convert_to_tensors(__a   )

						return padded_inputs












"""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}"""   )




