import math
import random
from typing import Any

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

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

						if visualization:
												from matplotlib import pyplot as plt

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


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

					# starting the problem with initial coordinates (12, 47)
					__UpperCamelCase		:       Tuple									=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					__UpperCamelCase		:       str									=     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)
					__UpperCamelCase		:       Tuple									=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					__UpperCamelCase		:       Dict									=     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       A	(    _lowercase		,  _lowercase						):
											return (3 * x**2) - (6 * y)

					__UpperCamelCase		:       Dict									=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					__UpperCamelCase		:       Optional[int]									=     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()}"""
					)

					__UpperCamelCase		:       List[str]									=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					__UpperCamelCase		:       Dict									=     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()}"""
					)






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





