



"""simple docstring"""

import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    __magic_name__     ( _lowerCamelCase					:			Optional[Any]	,							_lowerCamelCase					:			Any = True	,							_lowerCamelCase					:			List[Any] = math.inf	,							_lowerCamelCase					:			List[str] = -math.inf	,							_lowerCamelCase					:			List[str] = math.inf	,							_lowerCamelCase					:			int = -math.inf	,							_lowerCamelCase					:			Optional[Any] = False	,							_lowerCamelCase					:			Tuple = 1_0_0	,							_lowerCamelCase					:			Union[str, Any] = 0.01	,							_lowerCamelCase					:			Optional[Any] = 1	,							):
							__a		:		Optional[int]						=							False
							__a		:		str						=							search_prob
							__a		:		Union[str, Any]						=							start_temperate
							__a		:		Dict						=							[]
							__a		:		Dict						=							0
							__a		:		int						=							None

							while not search_end:
														__a		:		Optional[Any]						=							current_state.score()
														if best_state is None or current_score > best_state.score():
																					__a		:		str						=							current_state
														scores.append(__UpperCamelCase       )
														iterations += 1
														__a		:		Dict						=							None
														__a		:		Dict						=							current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					__a		:		Any						=							random.randint(0	,							len(__UpperCamelCase       ) - 1       )  # picking a random neighbor
																					__a		:		Union[str, Any]						=							neighbors.pop(__UpperCamelCase       )
																					__a		:		Optional[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:
																												__a		:		str						=							change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												__a		:		List[Any]						=							picked_neighbor
																					else:
																												__a		:		Optional[int]						=							(math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			__a		:		List[str]						=							picked_neighbor
														__a		:		Optional[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
																					__a		:		int						=							True
														else:
																					__a		:		int						=							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    __magic_name__     ( _lowerCamelCase					:			str	,							_lowerCamelCase					:			Union[str, Any]       ):
								return (x**2) + (y**2)

	# starting the problem with initial coordinates (12, 47)
	lowercase__       =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	lowercase__       =	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)
	lowercase__       =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	lowercase__       =	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    __magic_name__     ( _lowerCamelCase					:			Any	,							_lowerCamelCase					:			Any       ):
								return (3 * x**2) - (6 * y)

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		       =      {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""BlipImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlipModel""",
						    """BlipPreTrainedModel""",
						    """BlipForConditionalGeneration""",
						    """BlipForQuestionAnswering""",
						    """BlipVisionModel""",
						    """BlipTextModel""",
						    """BlipForImageTextRetrieval""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFBlipModel""",
						    """TFBlipPreTrainedModel""",
						    """TFBlipForConditionalGeneration""",
						    """TFBlipForQuestionAnswering""",
						    """TFBlipVisionModel""",
						    """TFBlipTextModel""",
						    """TFBlipForImageTextRetrieval""",
						]

if TYPE_CHECKING:
						from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
						from .processing_blip import BlipProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_blip import BlipImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blip import (
												    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlipForConditionalGeneration,
												    BlipForImageTextRetrieval,
												    BlipForQuestionAnswering,
												    BlipModel,
												    BlipPreTrainedModel,
												    BlipTextModel,
												    BlipVisionModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blip import (
												    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBlipForConditionalGeneration,
												    TFBlipForImageTextRetrieval,
												    TFBlipForQuestionAnswering,
												    TFBlipModel,
												    TFBlipPreTrainedModel,
												    TFBlipTextModel,
												    TFBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


