





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase:	Union[str, Any] = logging.get_logger(__name__)

__UpperCamelCase:	Optional[int] = {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"efficientnet"

	def __init__(						self:						Tuple,					lowerCamelCase_:						int = 3,					lowerCamelCase_:						int = 600,					lowerCamelCase_:						float = 2.0,					lowerCamelCase_:						float = 3.1,					lowerCamelCase_:						int = 8,					lowerCamelCase_:						List[int] = [3, 3, 5, 3, 5, 5, 3],					lowerCamelCase_:						List[int] = [32, 16, 24, 40, 80, 112, 192],					lowerCamelCase_:						List[int] = [16, 24, 40, 80, 112, 192, 320],					lowerCamelCase_:						List[int] = [],					lowerCamelCase_:						List[int] = [1, 2, 2, 2, 1, 2, 1],					lowerCamelCase_:						List[int] = [1, 2, 2, 3, 3, 4, 1],					lowerCamelCase_:						List[int] = [1, 6, 6, 6, 6, 6, 6],					lowerCamelCase_:						float = 0.2_5,					lowerCamelCase_:						str = "swish",					lowerCamelCase_:						int = 2560,					lowerCamelCase_:						str = "mean",					lowerCamelCase_:						float = 0.0_2,					lowerCamelCase_:						float = 0.0_0_1,					lowerCamelCase_:						float = 0.9_9,					lowerCamelCase_:						float = 0.5,					lowerCamelCase_:						float = 0.2,					**lowerCamelCase_:						Optional[int],					):
			super().__init__(**lowerCamelCase_			)

			lowercase__					:      Any      =		num_channels
			lowercase__					:      int      =		image_size
			lowercase__					:      Dict      =		width_coefficient
			lowercase__					:      str      =		depth_coefficient
			lowercase__					:      str      =		depth_divisor
			lowercase__					:      Dict      =		kernel_sizes
			lowercase__					:      int      =		in_channels
			lowercase__					:      Dict      =		out_channels
			lowercase__					:      Tuple      =		depthwise_padding
			lowercase__					:      Union[str, Any]      =		strides
			lowercase__					:      Union[str, Any]      =		num_block_repeats
			lowercase__					:      Any      =		expand_ratios
			lowercase__					:      Dict      =		squeeze_expansion_ratio
			lowercase__					:      Optional[int]      =		hidden_act
			lowercase__					:      Dict      =		hidden_dim
			lowercase__					:      Optional[int]      =		pooling_type
			lowercase__					:      Union[str, Any]      =		initializer_range
			lowercase__					:      Optional[int]      =		batch_norm_eps
			lowercase__					:      Any      =		batch_norm_momentum
			lowercase__					:      Tuple      =		dropout_rate
			lowercase__					:      List[str]      =		drop_connect_rate
			lowercase__					:      int      =		sum(lowerCamelCase_			) * 4


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				version.parse("1.11"      )

	@property
	def    snake_case__(						self:						Any			):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]			)





	@property
	def    snake_case__(						self:						str			):
			return 1E-5









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__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      List[str]      =		XGBRegressor(verbosity=0   ,    random_state=42			)
		xgb.fit(_lowercase   ,    _lowercase			)
		# Predict target for test data
		lowercase__					:      Union[str, Any]      =		xgb.predict(_lowercase			)
		lowercase__					:      Tuple      =		predictions.reshape(len(_lowercase			)   ,    1			)
		return predictions


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


