



import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			bool = True				,     _SCREAMING_SNAKE_CASE       :			float = math.inf				,     _SCREAMING_SNAKE_CASE       :			float = -math.inf				,     _SCREAMING_SNAKE_CASE       :			float = math.inf				,     _SCREAMING_SNAKE_CASE       :			float = -math.inf				,     _SCREAMING_SNAKE_CASE       :			bool = False				,     _SCREAMING_SNAKE_CASE       :			float = 100				,     _SCREAMING_SNAKE_CASE       :			float = 0.01				,     _SCREAMING_SNAKE_CASE       :			float = 1				,     ):
				"""simple docstring"""







				__a       =		False
				__a       =		search_prob
				__a       =		start_temperate
				__a       =		[]
				__a       =		0
				__a       =		None

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

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(_SCREAMING_SNAKE_CASE    )				,     _SCREAMING_SNAKE_CASE    )
								plt.xlabel("""Iterations"""    )
								plt.ylabel("""Function values"""    )
								plt.show()
				return best_state


if __name__ == "__main__":

						def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
										"""simple docstring"""







										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__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
										"""simple docstring"""







										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()}"""
						)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

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