



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase						:		Dict			= {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["GLPNFeatureExtractor"]
       UpperCAmelCase						:		Union[str, Any]			= ["GLPNImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GLPNForDepthEstimation",
           "GLPNLayer",
           "GLPNModel",
           "GLPNPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_glpn import GLPNFeatureExtractor
              from .image_processing_glpn import GLPNImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_glpn import (
                  GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GLPNForDepthEstimation,
                  GLPNLayer,
                  GLPNModel,
                  GLPNPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= 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()

