



'''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						(__A):



     """simple docstring"""



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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



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




def lowerCAmelCase						():



     """simple docstring"""



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


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()








'''simple docstring'''





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"):
  lowercase_	 =						{
      "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:
  lowercase_	 =						{
      "linear": PIL.Image.LINEAR,
      "bilinear": PIL.Image.BILINEAR,
      "bicubic": PIL.Image.BICUBIC,
      "lanczos": PIL.Image.LANCZOS,
      "nearest": PIL.Image.NEAREST,
  }




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						(images / 2 + 0.5).clamp(0						,			1)
     _a											=						images.cpu().permute(0						,			2						,			3						,			1).float().numpy()
     _a											=						numpy_to_pil(__A)
     return images




def lowerCAmelCase						(__A):



     """simple docstring"""



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

     return pil_images



