
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


snake_case :		List[Any]							      =  logging.get_logger(__name__)

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





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'efficientnet'







			def __init__(				self						,   _a = 3						,   _a = 600						,   _a = 2.0						,   _a = 3.1						,   _a = 8						,   _a = [3, 3, 5, 3, 5, 5, 3]						,   _a = [32, 16, 24, 40, 80, 112, 192]						,   _a = [16, 24, 40, 80, 112, 192, 320]						,   _a = []						,   _a = [1, 2, 2, 2, 1, 2, 1]						,   _a = [1, 2, 2, 3, 3, 4, 1]						,   _a = [1, 6, 6, 6, 6, 6, 6]						,   _a = 0.25						,   _a = "swish"						,   _a = 2_560						,   _a = "mean"						,   _a = 0.02						,   _a = 0.0_01						,   _a = 0.99						,   _a = 0.5						,   _a = 0.2						,   **_a						,   ):
				super().__init__(**_a		)

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







class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    version.parse('1.11'				)







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







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return 1e-5


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


	if a < 0:
		raise ValueError("math domain error"   )

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()
