from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





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(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       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()




