'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase = 1_0_0     ,)     ->			float:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						x_start
						_UpperCamelCase			:			Union[str, Any]   			=						fnc(UpperCAmelCase					)
						_UpperCamelCase			:			List[Any]   			=						0.0
						for _ in range(UpperCAmelCase					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												_UpperCamelCase			:			Tuple   			=						(x_end - x_start) / steps + xa
												_UpperCamelCase			:			int   			=						fnc(UpperCAmelCase					)
												area += abs(fxa + fxa					) * (xa - xa) / 2
												# Increment step
												_UpperCamelCase			:			Union[str, Any]   			=						xa
												_UpperCamelCase			:			Tuple   			=						fxa
						return area


if __name__ == "__main__":
			def 						__A							(     UpperCAmelCase					)     ->			Tuple:




									'''simple docstring'''
									return x**3 + x**2

			print("""f(x) = x^3 + x^2""")
			print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
			lowerCAmelCase_	:		Optional[int]			=     10
			while i <= 10_0000:
						print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
						i *= 10



'''simple docstring'''





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


lowerCAmelCase_	:		Optional[Any]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[Any]			=     {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''efficientnet'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						int = 3       ,		lowercase__					:						int = 600       ,		lowercase__					:						float = 2.0       ,		lowercase__					:						float = 3.1       ,		lowercase__					:						int = 8       ,		lowercase__					:						List[int] = [3, 3, 5, 3, 5, 5, 3]       ,		lowercase__					:						List[int] = [32, 16, 24, 40, 80, 112, 192]       ,		lowercase__					:						List[int] = [16, 24, 40, 80, 112, 192, 320]       ,		lowercase__					:						List[int] = []       ,		lowercase__					:						List[int] = [1, 2, 2, 2, 1, 2, 1]       ,		lowercase__					:						List[int] = [1, 2, 2, 3, 3, 4, 1]       ,		lowercase__					:						List[int] = [1, 6, 6, 6, 6, 6, 6]       ,		lowercase__					:						float = 0.2_5       ,		lowercase__					:						str = "swish"       ,		lowercase__					:						int = 2_560       ,		lowercase__					:						str = "mean"       ,		lowercase__					:						float = 0.0_2       ,		lowercase__					:						float = 0.0_0_1       ,		lowercase__					:						float = 0.9_9       ,		lowercase__					:						float = 0.5       ,		lowercase__					:						float = 0.2       ,		**lowercase__					:						int       ,		)							->Optional[int]:


										'''simple docstring'''




										super().__init__(**lowercase__					)

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





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							version.parse('''1.11'''	)





				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




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







				@property
				def  snake_case__				(						self					:						Any					)							->float:


										'''simple docstring'''




										return 1e-5


