


"""simple docstring"""







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = True  ,						lowerCAmelCase_ = math.inf  ,						lowerCAmelCase_ = -math.inf  ,						lowerCAmelCase_ = math.inf  ,						lowerCAmelCase_ = -math.inf  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = 100  ,						lowerCAmelCase_ = 0.01  ,						lowerCAmelCase_ = 1  ,						):




					'''simple docstring'''


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

					while not search_end:
										__SCREAMING_SNAKE_CASE						   =				current_state.score()
										if best_state is None or current_score > best_state.score():
															__SCREAMING_SNAKE_CASE						   =				current_state
										scores.append(lowerCAmelCase_   )
										iterations += 1
										__SCREAMING_SNAKE_CASE						   =				None
										__SCREAMING_SNAKE_CASE						   =				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						   =				random.randint(0  ,						len(lowerCAmelCase_   ) - 1   )  # picking a random neighbor
															__SCREAMING_SNAKE_CASE						   =				neighbors.pop(lowerCAmelCase_   )
															__SCREAMING_SNAKE_CASE						   =				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						   =				change * -1  # in case we are finding minimum
															if change > 0:  # improves the solution
																				__SCREAMING_SNAKE_CASE						   =				picked_neighbor
															else:
																				__SCREAMING_SNAKE_CASE						   =				(math.e) ** (
																				    change / current_temp
																				)  # probability generation function
																				if random.random() < probability:  # random number within probability
																									__SCREAMING_SNAKE_CASE						   =				picked_neighbor
										__SCREAMING_SNAKE_CASE						   =				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						   =				True
										else:
															__SCREAMING_SNAKE_CASE						   =				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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




							'''simple docstring'''


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

		# starting the problem with initial coordinates (12, 47)
		a__				:							int						=     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		a__				:							str						=     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)
		a__				:							int						=     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		a__				:							Union[str, Any]						=     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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




							'''simple docstring'''


							return (3 * x**2) - (6 * y)

		a__				:							int						=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		a__				:							Any						=     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()}"
		)

		a__				:							str						=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		a__				:							Optional[Any]						=     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"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint




@require_torch
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[str]:
						# For consistency across different places the DisjunctiveConstraint is called,
						# dc.token_ids is a list of integers. It is also initialized only by integers.

						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 4], [1, 2, 3, 4]]
						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )
						self.assertTrue(isinstance(dc.token_ids			,			UpperCAmelCase__   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	int:
						# We can't have constraints that are complete subsets of another. This leads to a preverse
						# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
						# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
						# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
						# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
						__SCREAMING_SNAKE_CASE						   =				[[1, 2], [1, 2, 3, 4]]

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(UpperCAmelCase__   )  # fails here




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(3   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is True and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 3]   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(4   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2, 4]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

						dc.reset()

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 3   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 2   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.remaining() == 0   )
						self.assertTrue(dc.current_seq == [1, 2, 5]   )
