


'''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 _UpperCamelCase       (     __A    )							->		tuple:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A      ,			__A      ,			__A    )							->		np.ndarray:


      '''simple docstring'''



      UpperCamelCase__										=				XGBRegressor(verbosity=0      ,			random_state=42    )
      xgb.fit(lowerCAmelCase__      ,			lowerCAmelCase__    )
      # Predict target for test data
      UpperCamelCase__										=				xgb.predict(lowerCAmelCase__    )
      UpperCamelCase__										=				predictions.reshape(len(lowerCAmelCase__    )      ,			1    )
      return predictions







def _UpperCamelCase       (     )							->		None:


      '''simple docstring'''



      UpperCamelCase__										=				fetch_california_housing()
      UpperCamelCase__					,	UpperCamelCase__										=				data_handling(lowerCAmelCase__    )
      UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				train_test_split(
          lowerCAmelCase__      ,			lowerCAmelCase__      ,			test_size=0.25      ,			random_state=1    )
      UpperCamelCase__										=				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 numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



