


'''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					(      lowerCamelCase  :      dict):
      return (data["data"], data["target"])




def      lowerCamelCase					(      lowerCamelCase  :      np.ndarray							,						lowerCamelCase  :      np.ndarray							,						lowerCamelCase  :      np.ndarray):
      A_						:  int          =				XGBRegressor(verbosity=0							,						random_state=42)
      xgb.fit(lowerCamelCase							,						lowerCamelCase)
      # Predict target for test data
      A_						:  Union[str, Any]          =				xgb.predict(lowerCamelCase)
      A_						:  int          =				predictions.reshape(len(lowerCamelCase)							,						1)
      return predictions




def      lowerCamelCase					(      ):
      A_						:  Union[str, Any]          =				fetch_california_housing()
      A_   ,					A_						:  List[str]          =				data_handling(lowerCamelCase)
      A_   ,					A_   ,					A_   ,					A_						:  List[Any]          =				train_test_split(
          lowerCamelCase							,						lowerCamelCase							,						test_size=0.25							,						random_state=1)
      A_						:  Any          =				xgboost(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
      # Error printing
      print(F'Mean Absolute Error : {mean_absolute_error(lowerCamelCase							,						lowerCamelCase)}')
      print(F'Mean Square Error  : {mean_squared_error(lowerCamelCase							,						lowerCamelCase)}')


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()




'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=None	,_a :       List[str]=None	,**_a :       Any ):

        '''simple docstring'''




        A_						:  Any          =				None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""	,_a	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[Any]          =				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`.""" )
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`.""" )

        super().__init__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  int          =				self.tokenizer(
            text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_token_type_ids=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
        # add pixel_values + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




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






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,_a	,)
        return self.image_processor_class






  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,_a	,)
        return self.image_processor
