





'''simple docstring'''



import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  bool = True		,       lowerCamelCase_			:  float = math.inf		,       lowerCamelCase_			:  float = -math.inf		,       lowerCamelCase_			:  float = math.inf		,       lowerCamelCase_			:  float = -math.inf		,       lowerCamelCase_			:  bool = False		,       lowerCamelCase_			:  float = 1_0_0		,       lowerCamelCase_			:  float = 0.01		,       lowerCamelCase_			:  float = 1		,       ):
			__lowercase										=							False
			__lowercase										=							search_prob
			__lowercase										=							start_temperate
			__lowercase										=							[]
			__lowercase										=							0
			__lowercase										=							None

			while not search_end:
						__lowercase										=							current_state.score()
						if best_state is None or current_score > best_state.score():
									__lowercase										=							current_state
						scores.append(lowerCamelCase_				)
						iterations += 1
						__lowercase										=							None
						__lowercase										=							current_state.get_neighbors()
						while (
						    next_state is None and neighbors
						):  # till we do not find a neighbor that we can move to
									__lowercase										=							random.randint(0		,       len(lowerCamelCase_				) - 1				)  # picking a random neighbor
									__lowercase										=							neighbors.pop(lowerCamelCase_				)
									__lowercase										=							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:
												__lowercase										=							change * -1  # in case we are finding minimum
									if change > 0:  # improves the solution
												__lowercase										=							picked_neighbor
									else:
												__lowercase										=							(math.e) ** (
												    change / current_temp
												)  # probability generation function
												if random.random() < probability:  # random number within probability
															__lowercase										=							picked_neighbor
						__lowercase										=							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
									__lowercase										=							True
						else:
									__lowercase										=							next_state

			if visualization:
						from matplotlib import pyplot as plt

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


if __name__ == "__main__":




	def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Dict				):
				return (x**2) + (y**2)

	# starting the problem with initial coordinates (12, 47)
	_SCREAMING_SNAKE_CASE					=    SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
	_SCREAMING_SNAKE_CASE					=    simulated_annealing(
	    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
	_SCREAMING_SNAKE_CASE					=    SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
	_SCREAMING_SNAKE_CASE					=    simulated_annealing(
	    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[Any]				):
				return (3 * x**2) - (6 * y)

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

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE					=    {
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPSegModel''',
	    '''CLIPSegPreTrainedModel''',
	    '''CLIPSegTextModel''',
	    '''CLIPSegVisionModel''',
	    '''CLIPSegForImageSegmentation''',
	]

if TYPE_CHECKING:
	from .configuration_clipseg import (
	    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPSegConfig,
	    CLIPSegTextConfig,
	    CLIPSegVisionConfig,
	)
	from .processing_clipseg import CLIPSegProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clipseg import (
		    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPSegForImageSegmentation,
		    CLIPSegModel,
		    CLIPSegPreTrainedModel,
		    CLIPSegTextModel,
		    CLIPSegVisionModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





