
import math





class 					snake_case__  :





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	x
								a_       :   List[str]			=	y
								a_       :   Union[str, Any]			=	step_size
								a_       :   Tuple			=	function_to_optimize





	def     A				(  self							)		->			str:
								"""simple docstring"""






								return self.function(self.x				,    self.y							)





	def     A				(  self							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	self.step_size
								return [
								    SearchProblem(__a				,    __a				,    __a				,    self.function							)
								    for x, y in (
								        (self.x - step_size, self.y - step_size),
								        (self.x - step_size, self.y),
								        (self.x - step_size, self.y + step_size),
								        (self.x, self.y - step_size),
								        (self.x, self.y + step_size),
								        (self.x + step_size, self.y - step_size),
								        (self.x + step_size, self.y),
								        (self.x + step_size, self.y + step_size),
								    )
								]





	def __hash__(  self							)		->			Optional[Any]:
								"""simple docstring"""






								return hash(str(self							)							)





	def __eq__(  self				,    UpperCamelCase_							)		->			int:
								"""simple docstring"""






								if isinstance(__a				,    __a							):
															return hash(str(self							)							) == hash(str(__a							)							)
								return False





	def __str__(  self							)		->			Any:
								"""simple docstring"""






								return f"""x: {self.x} y: {self.y}"""





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: bool = True	,	SCREAMING_SNAKE_CASE_				: float = math.inf	,	SCREAMING_SNAKE_CASE_				: float = -math.inf	,	SCREAMING_SNAKE_CASE_				: float = math.inf	,	SCREAMING_SNAKE_CASE_				: float = -math.inf	,	SCREAMING_SNAKE_CASE_				: bool = False	,	SCREAMING_SNAKE_CASE_				: int = 1_00_00	,	):






							"""simple docstring"""



							a_       :   Optional[Any]			=	search_prob
							a_       :   List[Any]			=	[]  # list to store the current score at each iteration
							a_       :   Any			=	0
							a_       :   int			=	False
							a_       :   Tuple			=	set()
							while not solution_found and iterations < max_iter:
														visited.add(_SCREAMING_SNAKE_CASE      )
														iterations += 1
														a_       :   Optional[Any]			=	current_state.score()
														scores.append(_SCREAMING_SNAKE_CASE      )
														a_       :   List[Any]			=	current_state.get_neighbors()
														a_       :   Optional[Any]			=	-math.inf
														a_       :   str			=	math.inf
														a_       :   str			=	None  # to hold the next best neighbor
														for neighbor in neighbors:
																					if neighbor in visited:
																												continue  # do not want to visit the same state again
																					if (
																					    neighbor.x > max_x
																					    or neighbor.x < min_x
																					    or neighbor.y > max_y
																					    or neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds
																					a_       :   List[Any]			=	neighbor.score() - current_score
																					if find_max:  # finding max
																												# going to direction with greatest ascent
																												if change > max_change and change > 0:
																																			a_       :   Dict			=	change
																																			a_       :   str			=	neighbor
																					else:  # finding min
																												# to direction with greatest descent
																												if change < min_change and change < 0:
																																			a_       :   List[str]			=	change
																																			a_       :   Tuple			=	neighbor
														if next_state is not None:
																					# we found at least one neighbor which improved the current state
																					a_       :   Tuple			=	next_state
														else:
																					# since we have no neighbor that improves the solution we stop the search
																					a_       :   int			=	True

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(_SCREAMING_SNAKE_CASE      )	,	_SCREAMING_SNAKE_CASE      )
														plt.xlabel("""Iterations"""      )
														plt.ylabel("""Function values"""      )
														plt.show()

							return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()





	def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):






								"""simple docstring"""



								return (x**2) + (y**2)

	# starting the problem with initial coordinates (3, 4)
	SCREAMING_SNAKE_CASE : Dict          =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	SCREAMING_SNAKE_CASE : Tuple          =    hill_climbing(prob, find_max=False)
	print(
	    "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
	    F"""{local_min.score()}"""
	)

	# starting the problem with initial coordinates (12, 47)
	SCREAMING_SNAKE_CASE : Optional[Any]          =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	SCREAMING_SNAKE_CASE : Tuple          =    hill_climbing(
	    prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
	)
	print(
	    "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
	    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
	)





	def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






								"""simple docstring"""



								return (3 * x**2) - (6 * y)

	SCREAMING_SNAKE_CASE : Optional[Any]          =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	SCREAMING_SNAKE_CASE : List[Any]          =    hill_climbing(prob, find_max=True)

	print(
	    "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
	    F"""{local_min.score()}"""
	)






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


SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : str          =    {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = """mobilevit"""





	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	num_channels
								a_       :   Optional[Any]			=	image_size
								a_       :   int			=	patch_size
								a_       :   List[str]			=	hidden_sizes
								a_       :   Optional[int]			=	neck_hidden_sizes
								a_       :   List[Any]			=	num_attention_heads
								a_       :   Union[str, Any]			=	mlp_ratio
								a_       :   List[str]			=	expand_ratio
								a_       :   str			=	hidden_act
								a_       :   Union[str, Any]			=	conv_kernel_size
								a_       :   Optional[Any]			=	output_stride
								a_       :   Union[str, Any]			=	hidden_dropout_prob
								a_       :   List[Any]			=	attention_probs_dropout_prob
								a_       :   List[Any]			=	classifier_dropout_prob
								a_       :   Union[str, Any]			=	initializer_range
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   str			=	qkv_bias

								# decode head attributes for semantic segmentation
								a_       :   Optional[Any]			=	aspp_out_channels
								a_       :   Any			=	atrous_rates
								a_       :   Union[str, Any]			=	aspp_dropout_prob
								a_       :   Any			=	semantic_loss_ignore_index





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






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





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]							)
								else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





	@property
	def     A				(  self							)		->			float:
								"""simple docstring"""






								return 1e-4


