

'''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 	_a       (				__lowerCAmelCase		:      dict     ):

		"""simple docstring"""

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




def 	_a       (				__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      np.ndarray     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  XGBRegressor(verbosity=0   ,							random_state=42     )
		xgb.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		# Predict target for test data
		snake_case__						:       Optional[int]       						=  xgb.predict(__lowerCAmelCase     )
		snake_case__						:       Any       						=  predictions.reshape(len(__lowerCAmelCase     )   ,							1     )
		return predictions




def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Tuple       						=  fetch_california_housing()
		snake_case__						,       snake_case__						:       List[str]       						=  data_handling(__lowerCAmelCase     )
		snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Tuple       						=  train_test_split(
		    __lowerCAmelCase   ,							__lowerCAmelCase   ,							test_size=0.25   ,							random_state=1     )
		snake_case__						:       List[str]       						=  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 unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
					import jax




@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    FlaxAutoencoderKL







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       Optional[int]       						=  (3_2, 3_2)

								snake_case__						:       Optional[int]       						=  jax.random.PRNGKey(0  )
								snake_case__						:       Union[str, Any]       						=  jax.random.uniform(snake_case_			,			((batch_size, num_channels) + sizes)  )

								return {"sample": image, "prng_key": prng_key}







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict



