




"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 							UpperCAmelCase		(		snake_case			:					Optional[Any]      ,       snake_case			:					Optional[Any] = True      ,       snake_case			:					int = math.inf      ,       snake_case			:					Optional[Any] = -math.inf      ,       snake_case			:					str = math.inf      ,       snake_case			:					List[Any] = -math.inf      ,       snake_case			:					Optional[int] = False      ,       snake_case			:					Dict = 100      ,       snake_case			:					List[Any] = 0.01      ,       snake_case			:					Any = 1      ,       ):
					_lowerCAmelCase:Dict									=  False
					_lowerCAmelCase:Optional[Any]									=  search_prob
					_lowerCAmelCase:Any									=  start_temperate
					_lowerCAmelCase:int									=  []
					_lowerCAmelCase:List[Any]									=  0
					_lowerCAmelCase:List[Any]									=  None

					while not search_end:
										_lowerCAmelCase:int									=  current_state.score()
										if best_state is None or current_score > best_state.score():
															_lowerCAmelCase:int									=  current_state
										scores.append(_lowercase    )
										iterations += 1
										_lowerCAmelCase:Tuple									=  None
										_lowerCAmelCase: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
															_lowerCAmelCase:Optional[Any]									=  random.randint(0      ,       len(_lowercase    ) - 1    )  # picking a random neighbor
															_lowerCAmelCase:List[Any]									=  neighbors.pop(_lowercase    )
															_lowerCAmelCase:List[str]									=  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:
																				_lowerCAmelCase:Union[str, Any]									=  change * -1  # in case we are finding minimum
															if change > 0:  # improves the solution
																				_lowerCAmelCase:str									=  picked_neighbor
															else:
																				_lowerCAmelCase:str									=  (math.e) ** (
																				    change / current_temp
																				)  # probability generation function
																				if random.random() < probability:  # random number within probability
																									_lowerCAmelCase:List[Any]									=  picked_neighbor
										_lowerCAmelCase: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
															_lowerCAmelCase:Dict									=  True
										else:
															_lowerCAmelCase:int									=  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 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					str    ):
									return (x**2) + (y**2)

				# starting the problem with initial coordinates (12, 47)
				UpperCamelCase__						=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				UpperCamelCase__						=			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)
				UpperCamelCase__						=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				UpperCamelCase__						=			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		(		snake_case			:					int      ,       snake_case			:					List[str]    ):
									return (3 * x**2) - (6 * y)

				UpperCamelCase__						=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				UpperCamelCase__						=			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__						=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				UpperCamelCase__						=			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 torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   a__			(       UpperCamelCase_  ):
						snake_case__             = (KDPMaDiscreteScheduler,)
						snake_case__             = 1_0





						def       __UpperCamelCase     (							self      :	Dict     ,**a__      :	str)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''num_train_timesteps''': 1100,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**a__)
											return config





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Optional[int]:




											"""simple docstring"""







											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[int]:




											"""simple docstring"""







											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,[0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=a__     ,beta_end=a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=a__)





						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=a__)





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.scheduler_classes[0]
											_lowerCAmelCase:Union[str, Any]									=  self.get_scheduler_config(prediction_type='''v_prediction''')
											_lowerCAmelCase:Tuple									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Optional[Any]									=  self.dummy_model()
											_lowerCAmelCase:List[Any]									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:Optional[Any]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Any									=  model(a__     ,a__)

																_lowerCAmelCase:Union[str, Any]									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Optional[int]									=  output.prev_sample

											_lowerCAmelCase:str									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Union[str, Any]									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
																assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:Tuple									=  self.scheduler_classes[0]
											_lowerCAmelCase:List[str]									=  self.get_scheduler_config()
											_lowerCAmelCase:Union[str, Any]									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Any									=  self.dummy_model()
											_lowerCAmelCase:str									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:List[str]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:List[str]									=  model(a__     ,a__)

																_lowerCAmelCase:int									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:List[Any]									=  output.prev_sample

											_lowerCAmelCase:Tuple									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Dict									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3







						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       List[Any]:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:str									=  self.scheduler_classes[0]
											_lowerCAmelCase:Optional[Any]									=  self.get_scheduler_config()
											_lowerCAmelCase:Any									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps     ,device=a__)

											_lowerCAmelCase:Optional[int]									=  self.dummy_model()
											_lowerCAmelCase:Optional[Any]									=  self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																_lowerCAmelCase:Union[str, Any]									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Tuple									=  model(a__     ,a__)

																_lowerCAmelCase:str									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Dict									=  output.prev_sample

											_lowerCAmelCase:Any									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:str									=  torch.mean(torch.abs(a__))

											if str(a__).startswith('''cpu'''):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3






