




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class      A       (						_a       ):
							lowercase_							     =							'MCTCTFeatureExtractor'
							lowercase_							     =							'AutoTokenizer'







							def __init__(    self			:    int				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				str:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.feature_extractor
														_a      	=							False







							def __call__(    self			:    Dict				,     *lowerCAmelCase_			:    Dict				,     **lowerCAmelCase_			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor(*lowerCAmelCase_				,     **lowerCAmelCase_   )

														if "raw_speech" in kwargs:
																					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
																					_a      	=							kwargs.pop('''raw_speech'''   )
														else:
																					_a      	=							kwargs.pop('''audio'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''sampling_rate'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''text'''				,     lowerCAmelCase_   )
														if len(lowerCAmelCase_   ) > 0:
																					_a      	=							args[0]
																					_a      	=							args[1:]

														if audio is None and text is None:
																					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''   )

														if audio is not None:
																					_a      	=							self.feature_extractor(lowerCAmelCase_				,     *lowerCAmelCase_				,     sampling_rate=lowerCAmelCase_				,     **lowerCAmelCase_   )
														if text is not None:
																					_a      	=							self.tokenizer(lowerCAmelCase_				,     **lowerCAmelCase_   )

														if text is None:
																					return inputs
														elif audio is None:
																					return encodings
														else:
																					_a      	=							encodings['''input_ids''']
																					return inputs







							def   __lowerCAmelCase     (    self			:    str				,     *lowerCAmelCase_			:    Any				,     **lowerCAmelCase_			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple				,     *lowerCAmelCase_			:    Optional[int]				,     **lowerCAmelCase_			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor.pad(*lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							kwargs.pop('''input_features'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''labels'''				,     lowerCAmelCase_   )
														if len(lowerCAmelCase_   ) > 0:
																					_a      	=							args[0]
																					_a      	=							args[1:]

														if input_features is not None:
																					_a      	=							self.feature_extractor.pad(lowerCAmelCase_				,     *lowerCAmelCase_				,     **lowerCAmelCase_   )
														if labels is not None:
																					_a      	=							self.tokenizer.pad(lowerCAmelCase_				,     **lowerCAmelCase_   )

														if labels is None:
																					return input_features
														elif input_features is None:
																					return labels
														else:
																					_a      	=							labels['''input_ids''']
																					return input_features







							def   __lowerCAmelCase     (    self			:    str				,     *lowerCAmelCase_			:    Dict				,     **lowerCAmelCase_			:    List[Any]   )     ->				Tuple:


														"""simple docstring"""


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







							@contextmanager
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														warnings.warn(
														    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
														    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
														    '''your audio inputs, or in a separate call.'''   )
														_a      	=							True
														_a      	=							self.tokenizer
														yield
														_a      	=							self.feature_extractor
														_a      	=							False







'''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 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

