



import math as m
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase=0.001							)		->		Any:







						"""simple docstring"""

						return (f(a + h							) - f(a - h							)) / (2 * h)
def 						snake_case__							(       __lowercase				,  __lowercase=0				,  __lowercase=1_0_0				,  __lowercase=0.0001				,  __lowercase=1E-6				,  __lowercase=False							)		->		int:







						"""simple docstring"""

						A__						:				Any         =			xa  # set the initial guess
						A__						:				Dict         =			[a]
						A__						:				Union[str, Any]         =			abs(f(lowerCAmelCase__							)							)
						A__						:				str         =			lambda __lowercase							: calc_derivative(lowerCAmelCase__				,  lowerCAmelCase__				,  h=lowerCAmelCase__							)  # noqa: E731  Derivative of f(x)
						for _ in range(lowerCAmelCase__							):
												if fa(lowerCAmelCase__							) == 0:
																		raise ValueError("No converging solution found"							)
												A__						:				Optional[Any]         =			a - f(lowerCAmelCase__							) / fa(lowerCAmelCase__							)  # Calculate the next estimate
												if logsteps:
																		steps.append(lowerCAmelCase__							)
												if error < maxerror:
																		break
						else:
												raise ValueError("Iteration limit reached, no converging solution found"							)
						if logsteps:
												# If logstep is true, then log intermediate steps
												return a, error, steps
						return a, error


if __name__ == "__main__":
		from matplotlib import pyplot as plt

		snake_case     :    Tuple						=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
		snake_case     :    str						=    newton_raphson(
		    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
		)
		plt.plot([abs(f(x)) for x in steps])
		plt.xlabel('step')
		plt.ylabel('error')
		plt.show()
		print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

snake_case_   :	Any							      ={
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= 'swiftformer'






		def __init__(      self							, lowercase__=3							, lowercase__=[3, 3, 6, 4]							, lowercase__=[48, 56, 112, 220]							, lowercase__=4							, lowercase__=[True, True, True, True]							, lowercase__="gelu"							, lowercase__=3							, lowercase__=2							, lowercase__=1							, lowercase__=0.0							, lowercase__=True							, lowercase__=1e-5							, lowercase__=1e-5							, **lowercase__							, )				->				Optional[Any]:
				super().__init__(**lowercase__						)
				__A    =							num_channels
				__A    =							depths
				__A    =							embed_dims
				__A    =							mlp_ratio
				__A    =							downsamples
				__A    =							hidden_act
				__A    =							down_patch_size
				__A    =							down_stride
				__A    =							down_pad
				__A    =							drop_path_rate
				__A    =							use_layer_scale
				__A    =							layer_scale_init_value
				__A    =							batch_norm_eps





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= version.parse('1.11'		)






		@property
		def 					_lowerCamelCase					(      self						)				->				Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]						)






		@property
		def 					_lowerCamelCase					(      self						)				->				float:
				return 1e-4

