



"""simple docstring"""
from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_pt_objects import *  # noqa F403
else:
       from .scheduling_consistency_models import CMStochasticIterativeScheduler
       from .scheduling_ddim import DDIMScheduler
       from .scheduling_ddim_inverse import DDIMInverseScheduler
       from .scheduling_ddim_parallel import DDIMParallelScheduler
       from .scheduling_ddpm import DDPMScheduler
       from .scheduling_ddpm_parallel import DDPMParallelScheduler
       from .scheduling_deis_multistep import DEISMultistepScheduler
       from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
       from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
       from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
       from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
       from .scheduling_euler_discrete import EulerDiscreteScheduler
       from .scheduling_heun_discrete import HeunDiscreteScheduler
       from .scheduling_ipndm import IPNDMScheduler
       from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
       from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
       from .scheduling_karras_ve import KarrasVeScheduler
       from .scheduling_pndm import PNDMScheduler
       from .scheduling_repaint import RePaintScheduler
       from .scheduling_sde_ve import ScoreSdeVeScheduler
       from .scheduling_sde_vp import ScoreSdeVpScheduler
       from .scheduling_unclip import UnCLIPScheduler
       from .scheduling_unipc_multistep import UniPCMultistepScheduler
       from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
       from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_objects import *  # noqa F403
else:
       from .scheduling_ddim_flax import FlaxDDIMScheduler
       from .scheduling_ddpm_flax import FlaxDDPMScheduler
       from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
       from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
       from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
       from .scheduling_pndm_flax import FlaxPNDMScheduler
       from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
       from .scheduling_utils_flax import (
           FlaxKarrasDiffusionSchedulers,
           FlaxSchedulerMixin,
           FlaxSchedulerOutput,
           broadcast_to_shape_from_left,
       )


try:
       if not (is_torch_available() and is_scipy_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
       from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
       if not (is_torch_available() and is_torchsde_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
       from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler






"""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()

