

UpperCAmelCase_       :				Any		   =      '''
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_       :				Tuple		   =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_       :				Any		   =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}








import math






class     lowerCamelCase_			:




	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
		__A   : Union[str, Any]								= x
		__A   : Union[str, Any]								= y
		__A   : Optional[int]								= step_size
		__A   : Dict								= function_to_optimize




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		return self.function(self.x				,			self.y       )




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Dict								= 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       ):
		return hash(str(self       )       )




	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
		if isinstance(__A				,			__A       ):
			return hash(str(self       )       ) == hash(str(__A       )       )
		return False




	def __str__(    self	:    List[Any]       ):
		return F"""x: {self.x} y: {self.y}"""






def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
	__A   : int								= search_prob
	__A   : List[str]								= []  # list to store the current score at each iteration
	__A   : Optional[Any]								= 0
	__A   : Any								= False
	__A   : Dict								= set()
	while not solution_found and iterations < max_iter:
		visited.add(a__					)
		iterations += 1
		__A   : Union[str, Any]								= current_state.score()
		scores.append(a__					)
		__A   : List[Any]								= current_state.get_neighbors()
		__A   : Tuple								= -math.inf
		__A   : str								= math.inf
		__A   : Optional[int]								= 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   : int								= neighbor.score() - current_score
			if find_max:  # finding max
				# going to direction with greatest ascent
				if change > max_change and change > 0:
					__A   : str								= change
					__A   : Optional[int]								= neighbor
			else:  # finding min
				# to direction with greatest descent
				if change < min_change and change < 0:
					__A   : List[Any]								= change
					__A   : List[str]								= 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   : str								= True

	if visualization:
		from matplotlib import pyplot as plt

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

	return current_state


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
				return (x**2) + (y**2)

			# starting the problem with initial coordinates (3, 4)
			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      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)
			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(
			    prob, find_max=False, max_x=100, 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        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
				return (3 * x**2) - (6 * y)

			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Dict		   =      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()}"""
			)





