
"""simple docstring"""







import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = True	,  _UpperCamelCase = math.inf	,  _UpperCamelCase = -math.inf	,  _UpperCamelCase = math.inf	,  _UpperCamelCase = -math.inf	,  _UpperCamelCase = False	,  _UpperCamelCase = 100	,  _UpperCamelCase = 0.01	,  _UpperCamelCase = 1	,  ):
	__lowerCAmelCase      :						Any						      =				False
	__lowerCAmelCase      :						Dict						      =				search_prob
	__lowerCAmelCase      :						List[Any]						      =				start_temperate
	__lowerCAmelCase      :						Dict						      =				[]
	__lowerCAmelCase      :						Optional[int]						      =				0
	__lowerCAmelCase      :						Union[str, Any]						      =				None

	while not search_end:
		__lowerCAmelCase      :						Tuple						      =				current_state.score()
		if best_state is None or current_score > best_state.score():
			__lowerCAmelCase      :						Optional[Any]						      =				current_state
		scores.append(_UpperCamelCase		)
		iterations += 1
		__lowerCAmelCase      :						int						      =				None
		__lowerCAmelCase      :						Union[str, Any]						      =				current_state.get_neighbors()
		while (
		    next_state is None and neighbors
		):  # till we do not find a neighbor that we can move to
			__lowerCAmelCase      :						str						      =				random.randint(0	,  len(_UpperCamelCase		) - 1		)  # picking a random neighbor
			__lowerCAmelCase      :						Optional[int]						      =				neighbors.pop(_UpperCamelCase		)
			__lowerCAmelCase      :						int						      =				picked_neighbor.score() - current_score

			if (
			    picked_neighbor.x > max_x
			    or picked_neighbor.x < min_x
			    or picked_neighbor.y > max_y
			    or picked_neighbor.y < min_y
			):
				continue  # neighbor outside our bounds

			if not find_max:
				__lowerCAmelCase      :						int						      =				change * -1  # in case we are finding minimum
			if change > 0:  # improves the solution
				__lowerCAmelCase      :						List[str]						      =				picked_neighbor
			else:
				__lowerCAmelCase      :						Union[str, Any]						      =				(math.e) ** (
				    change / current_temp
				)  # probability generation function
				if random.random() < probability:  # random number within probability
					__lowerCAmelCase      :						Optional[Any]						      =				picked_neighbor
		__lowerCAmelCase      :						Any						      =				current_temp - (current_temp * rate_of_decrease)

		if current_temp < threshold_temp or next_state is None:
			# temperature below threshold, or could not find a suitable neighbor
			__lowerCAmelCase      :						List[str]						      =				True
		else:
			__lowerCAmelCase      :						str						      =				next_state

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(_UpperCamelCase		)	,  _UpperCamelCase		)
		plt.xlabel('Iterations'		)
		plt.ylabel('Function values'		)
		plt.show()
	return best_state


if __name__ == "__main__":
							def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
								return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							lowerCamelCase__        =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(
							    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()}'
							)

							# starting the problem with initial coordinates (12, 47)
							lowerCamelCase__        =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(
							    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
							)
							print(
							    """The maximum 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    (_UpperCamelCase	,  _UpperCamelCase		):
								return (3 * x**2) - (6 * y)

							lowerCamelCase__        =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    f'{local_min.score()}'
							)

							lowerCamelCase__        =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(prob, find_max=True, visualization=True)
							print(
							    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    f'{local_min.score()}'
							)

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)