


"""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      (   __snake_case							:      dict   ):




   """simple docstring"""







   return (data["data"], data["target"])

def 				_snake_case      (   __snake_case							:      np.ndarray						, __snake_case							:      np.ndarray						, __snake_case							:      np.ndarray   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		XGBRegressor(verbosity=0						, random_state=42   )
   xgb.fit(__snake_case						, __snake_case   )
   # Predict target for test data
   _lowerCamelCase	:					str   =		xgb.predict(__snake_case   )
   _lowerCamelCase	:					Optional[Any]   =		predictions.reshape(len(__snake_case   )						, 1   )
   return predictions

def 				_snake_case      (   ):




   """simple docstring"""







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


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
 main()





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

