



'''simple docstring'''






from ...processing_utils import ProcessorMixin

class 							a__(      lowerCamelCase__			):
    lowercase__				=					"""SpeechT5FeatureExtractor"""
    lowercase__				=					"""SpeechT5Tokenizer"""







    def __init__(   self				:						Dict		,    __snake_case				:						int		,    __snake_case				:						str			):
      super().__init__(__snake_case		,    __snake_case			)







    def __call__(   self				:						List[str]		,    *__snake_case				:						Tuple		,    **__snake_case				:						Optional[Any]			):
      a		:      List[str]       =  kwargs.pop('audio'		,    __snake_case			)
      a		:      str       =  kwargs.pop('text'		,    __snake_case			)
      a		:      Union[str, Any]       =  kwargs.pop('text_target'		,    __snake_case			)
      a		:      List[Any]       =  kwargs.pop('audio_target'		,    __snake_case			)
      a		:      Optional[Any]       =  kwargs.pop('sampling_rate'		,    __snake_case			)

      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:
        a		:      Optional[int]       =  self.feature_extractor(__snake_case		,    *__snake_case		,    sampling_rate=__snake_case		,    **__snake_case			)
      elif text is not None:
        a		:      List[str]       =  self.tokenizer(__snake_case		,    **__snake_case			)
      else:
        a		:      str       =  None

      if audio_target is not None:
        a		:      List[str]       =  self.feature_extractor(audio_target=__snake_case		,    *__snake_case		,    sampling_rate=__snake_case		,    **__snake_case			)
        a		:      Optional[Any]       =  targets['input_values']
      elif text_target is not None:
        a		:      Tuple       =  self.tokenizer(__snake_case		,    **__snake_case			)
        a		:      Dict       =  targets['input_ids']
      else:
        a		:      str       =  None

      if inputs is None:
        return targets

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

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

      return inputs







    def     lowercase_ (   self				:						List[str]		,    *__snake_case				:						Dict		,    **__snake_case				:						List[str]			):
      a		:      Optional[Any]       =  kwargs.pop('input_values'		,    __snake_case			)
      a		:      Any       =  kwargs.pop('input_ids'		,    __snake_case			)
      a		:      Any       =  kwargs.pop('labels'		,    __snake_case			)

      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:
        a		:      Dict       =  self.feature_extractor.pad(__snake_case		,    *__snake_case		,    **__snake_case			)
      elif input_ids is not None:
        a		:      List[str]       =  self.tokenizer.pad(__snake_case		,    **__snake_case			)
      else:
        a		:      Any       =  None

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

      if inputs is None:
        return targets

      if targets is not None:
        a		:      str       =  labels

        a		:      str       =  targets.get('attention_mask'			)
        if decoder_attention_mask is not None:
          a		:      Tuple       =  decoder_attention_mask

      return inputs







    def     lowercase_ (   self				:						Any		,    *__snake_case				:						List[str]		,    **__snake_case				:						Dict			):
      return self.tokenizer.batch_decode(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]		,    *__snake_case				:						List[Any]		,    **__snake_case				:						Dict			):
      return self.tokenizer.decode(*__snake_case		,    **__snake_case			)




'''simple docstring'''






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()