

'''simple docstring'''
from ...processing_utils import ProcessorMixin


class        a__  (							a_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'SpeechT5FeatureExtractor'
   _SCREAMING_SNAKE_CASE   :					int     	=							'SpeechT5Tokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(lowercase_      ,    lowercase_  )


   def __call__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Any      					=       kwargs.pop("audio"      ,    lowercase_  )
      _lowercase      :				Tuple      					=       kwargs.pop("text"      ,    lowercase_  )
      _lowercase      :				Optional[int]      					=       kwargs.pop("text_target"      ,    lowercase_  )
      _lowercase      :				Tuple      					=       kwargs.pop("audio_target"      ,    lowercase_  )
      _lowercase      :				List[Any]      					=       kwargs.pop("sampling_rate"      ,    lowercase_  )

      if audio is not None and text is not None:
         raise ValueError(
             "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
      if audio_target is not None and text_target is not None:
         raise ValueError(
             "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
      if audio is None and audio_target is None and text is None and text_target is None:
         raise ValueError(
             "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

      if audio is not None:
         _lowercase      :				Tuple      					=       self.feature_extractor(lowercase_      ,    *lowercase_      ,    sampling_rate=lowercase_      ,    **lowercase_  )
      elif text is not None:
         _lowercase      :				Any      					=       self.tokenizer(lowercase_      ,    **lowercase_  )
      else:
         _lowercase      :				Dict      					=       None

      if audio_target is not None:
         _lowercase      :				int      					=       self.feature_extractor(audio_target=lowercase_      ,    *lowercase_      ,    sampling_rate=lowercase_      ,    **lowercase_  )
         _lowercase      :				Tuple      					=       targets['''input_values''']
      elif text_target is not None:
         _lowercase      :				Union[str, Any]      					=       self.tokenizer(lowercase_      ,    **lowercase_  )
         _lowercase      :				int      					=       targets['''input_ids''']
      else:
         _lowercase      :				List[Any]      					=       None

      if inputs is None:
         return targets

      if targets is not None:
         _lowercase      :				List[Any]      					=       labels

         _lowercase      :				List[Any]      					=       targets.get("attention_mask"  )
         if decoder_attention_mask is not None:
            _lowercase      :				Tuple      					=       decoder_attention_mask

      return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       kwargs.pop("input_values"      ,    lowercase_  )
      _lowercase      :				Dict      					=       kwargs.pop("input_ids"      ,    lowercase_  )
      _lowercase      :				Dict      					=       kwargs.pop("labels"      ,    lowercase_  )

      if input_values is not None and input_ids is not None:
         raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
      if input_values is None and input_ids is None and labels is None:
         raise ValueError(
             "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

      if input_values is not None:
         _lowercase      :				List[str]      					=       self.feature_extractor.pad(lowercase_      ,    *lowercase_      ,    **lowercase_  )
      elif input_ids is not None:
         _lowercase      :				Optional[Any]      					=       self.tokenizer.pad(lowercase_      ,    **lowercase_  )
      else:
         _lowercase      :				Tuple      					=       None

      if labels is not None:
         if "input_ids" in labels or (isinstance(lowercase_      ,    lowercase_  ) and "input_ids" in labels[0]):
            _lowercase      :				Optional[int]      					=       self.tokenizer.pad(lowercase_      ,    **lowercase_  )
            _lowercase      :				str      					=       targets['''input_ids''']
         else:
            _lowercase      :				Any      					=       self.feature_extractor.feature_size
            _lowercase      :				Optional[int]      					=       self.feature_extractor.num_mel_bins
            _lowercase      :				str      					=       self.feature_extractor.pad(lowercase_      ,    *lowercase_      ,    **lowercase_  )
            _lowercase      :				Any      					=       feature_size_hack
            _lowercase      :				int      					=       targets['''input_values''']
      else:
         _lowercase      :				List[Any]      					=       None

      if inputs is None:
         return targets

      if targets is not None:
         _lowercase      :				int      					=       labels

         _lowercase      :				List[Any]      					=       targets.get("attention_mask"  )
         if decoder_attention_mask is not None:
            _lowercase      :				Any      					=       decoder_attention_mask

      return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*lowercase_      ,    **lowercase_  )






   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*lowercase_      ,    **lowercase_  )









'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


_snake_case         =  '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

_snake_case         =  '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


_snake_case         =  '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION  )
class        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(self._get_feature_types()  )      ,    reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"  )  ),
             "references": datasets.Sequence(datasets.Value("float"  )  ),
         }
      else:
         return {
             "predictions": datasets.Value("float"  ),
             "references": datasets.Value("float"  ),
         }






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






