


"""simple docstring"""


import math
class snake_case					:







		def __init__(    self					:				int			,      a__					:				int			,      a__					:				int			,      a__					:				int			,      a__					:				Optional[int]						)				->  List[Any]:





						'''simple docstring'''







						_A         =	x
						_A         =	y
						_A         =	step_size
						_A         =	function_to_optimize







		def        a_       (    self					:				Optional[Any]						)				->  int:





						'''simple docstring'''







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







		def        a_       (    self					:				Union[str, Any]						)				->  List[str]:





						'''simple docstring'''







						_A         =	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					:				str						)				->  Optional[int]:





						'''simple docstring'''







						return hash(str(self						)						)







		def __eq__(    self					:				Optional[Any]			,      a__					:				List[Any]						)				->  Tuple:





						'''simple docstring'''







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







		def __str__(    self					:				str						)				->  Tuple:





						'''simple docstring'''







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






def    a__       ( __lowercase   ,	__lowercase = True   ,	__lowercase = math.inf   ,	__lowercase = -math.inf   ,	__lowercase = math.inf   ,	__lowercase = -math.inf   ,	__lowercase = False   ,	__lowercase = 1_0000   ,	)		->		SearchProblem:
				_A         =	search_prob
				_A         =	[]  # list to store the current score at each iteration
				_A         =	0
				_A         =	False
				_A         =	set()
				while not solution_found and iterations < max_iter:
								visited.add(__lowercase      )
								iterations += 1
								_A         =	current_state.score()
								scores.append(__lowercase      )
								_A         =	current_state.get_neighbors()
								_A         =	-math.inf
								_A         =	math.inf
								_A         =	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         =	neighbor.score() - current_score
												if find_max:  # finding max
																# going to direction with greatest ascent
																if change > max_change and change > 0:
																				_A         =	change
																				_A         =	neighbor
												else:  # finding min
																# to direction with greatest descent
																if change < min_change and change < 0:
																				_A         =	change
																				_A         =	neighbor
								if next_state is not None:
												# we found at least one neighbor which improved the current state
												_A         =	next_state
								else:
												# since we have no neighbor that improves the solution we stop the search
												_A         =	True

				if visualization:
								from matplotlib import pyplot as plt

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

				return current_state


if __name__ == "__main__":
		import doctest

		doctest.testmod()






		def    a__       ( __lowercase   ,	__lowercase      )		->		List[Any]:
						return (x**2) + (y**2)

		# starting the problem with initial coordinates (3, 4)
		a_											=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		a_											=			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)
		a_											=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		a_											=			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    a__       ( __lowercase   ,	__lowercase      )		->		str:
						return (3 * x**2) - (6 * y)

		a_											=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		a_											=			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()}'''
		)



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


a_											=			logging.get_logger(__name__)
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      *a__					:				Union[str, Any]			,      **a__					:				Optional[Any]						)				->  None:





						'''simple docstring'''







						warnings.warn(
						    "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use MobileNetV2ImageProcessor instead."			,      a__			,      )
						super().__init__(*a__			,      **a__						)