"""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 = 0.0_1     ,__UpperCAmelCase = 1     ,):






	"""simple docstring"""





	_lowercase :    List[Any]							=							False
	_lowercase :    Optional[Any]							=							search_prob
	_lowercase :    Tuple							=							start_temperate
	_lowercase :    List[str]							=							[]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Optional[Any]							=							None

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






							"""simple docstring"""





							return (x**2) + (y**2)

						# starting the problem with initial coordinates (12, 47)
						SCREAMING_SNAKE_CASE									=						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						SCREAMING_SNAKE_CASE									=						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)
						SCREAMING_SNAKE_CASE									=						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						SCREAMING_SNAKE_CASE									=						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			):






							"""simple docstring"""





							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 __future__ import annotations

import collections
import pprint
from pathlib import Path


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return "".join(sorted(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return word_by_signature[signature(__UpperCAmelCase			)]


SCREAMING_SNAKE_CASE									=						Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE									=						sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE									=						collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



