code: 
  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()}"""
  	)
  
  
  
style_context: 
  
  
  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
  
  
  