
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 				a						(							snake_case__:					str					):


				'''simple docstring'''
				return (data["data"], data["target"])




def 				a						(							snake_case__:					Dict						,   snake_case__:					int						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					XGBRegressor(verbosity=0						,   random_state=42					)
				xgb.fit(_UpperCamelCase						,   _UpperCamelCase					)
				# Predict target for test data
				lowercase_       =					xgb.predict(_UpperCamelCase					)
				lowercase_       =					predictions.reshape(len(_UpperCamelCase					)						,   1					)
				return predictions




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					fetch_california_housing()
				lowercase_       =					data_handling(_UpperCamelCase					)
				lowercase_       =					train_test_split(
				    _UpperCamelCase						,   _UpperCamelCase						,   test_size=0.2_5						,   random_state=1					)
				lowercase_       =					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()











import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
