

'''simple docstring'''

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class lowerCamelCase   (    _lowerCamelCase				):
				snake_case_							     = (DPMSolverMultistepScheduler,)
				snake_case_							     = (('''num_inference_steps''', 25),)

				def 					_lowerCamelCase    (       self,						**lowercase_ )    ->							Optional[Any]:
					snake_case							=					{
					    '''num_train_timesteps''': 1000,
					    '''beta_start''': 0.0_001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''solver_order''': 2,
					    '''prediction_type''': '''epsilon''',
					    '''thresholding''': False,
					    '''sample_max_value''': 1.0,
					    '''algorithm_type''': '''dpmsolver++''',
					    '''solver_type''': '''midpoint''',
					    '''lower_order_final''': False,
					    '''lambda_min_clipped''': -float('inf' ),
					    '''variance_type''': None,
					}

					config.update(**lowerCamelCase_ )
					return config

				def 					_lowerCamelCase    (       self,						lowercase_=0,						**lowercase_ )    ->							Dict:
					snake_case							=					dict(self.forward_default_kwargs )
					snake_case							=					kwargs.pop('num_inference_steps',						lowerCamelCase_ )
					snake_case							=					self.dummy_sample
					snake_case							=					0.1 * sample
					snake_case							=					[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						snake_case							=					self.get_scheduler_config(**lowerCamelCase_ )
						snake_case							=					scheduler_class(**lowerCamelCase_ )
						scheduler.set_timesteps(lowerCamelCase_ )
						# copy over dummy past residuals
						snake_case							=					dummy_past_residuals[: scheduler.config.solver_order]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_ )
							snake_case							=					scheduler_class.from_pretrained(lowerCamelCase_ )
							new_scheduler.set_timesteps(lowerCamelCase_ )
							# copy over dummy past residuals
							snake_case							=					dummy_past_residuals[: new_scheduler.config.solver_order]

						snake_case							=					sample, sample
						for t in range(lowerCamelCase_,						time_step + scheduler.config.solver_order + 1 ):
							snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample
							snake_case							=					new_scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

				def 					_lowerCamelCase    (       self )    ->							int:
					pass

				def 					_lowerCamelCase    (       self,						lowercase_=0,						**lowercase_ )    ->							List[Any]:
					snake_case							=					dict(self.forward_default_kwargs )
					snake_case							=					kwargs.pop('num_inference_steps',						lowerCamelCase_ )
					snake_case							=					self.dummy_sample
					snake_case							=					0.1 * sample
					snake_case							=					[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						snake_case							=					self.get_scheduler_config()
						snake_case							=					scheduler_class(**lowerCamelCase_ )
						scheduler.set_timesteps(lowerCamelCase_ )

						# copy over dummy past residuals (must be after setting timesteps)
						snake_case							=					dummy_past_residuals[: scheduler.config.solver_order]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_ )
							snake_case							=					scheduler_class.from_pretrained(lowerCamelCase_ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(lowerCamelCase_ )

							# copy over dummy past residual (must be after setting timesteps)
							snake_case							=					dummy_past_residuals[: new_scheduler.config.solver_order]

						snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample
						snake_case							=					new_scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

				def 					_lowerCamelCase    (       self,						lowercase_=None,						**lowercase_ )    ->							Optional[Any]:
					if scheduler is None:
						snake_case							=					self.scheduler_classes[0]
						snake_case							=					self.get_scheduler_config(**lowerCamelCase_ )
						snake_case							=					scheduler_class(**lowerCamelCase_ )

					snake_case							=					10
					snake_case							=					self.dummy_model()
					snake_case							=					self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase_ )

					for i, t in enumerate(scheduler.timesteps ):
						snake_case							=					model(lowerCamelCase_,						lowerCamelCase_ )
						snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_ ).prev_sample

					return sample

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					dict(self.forward_default_kwargs )

					snake_case							=					kwargs.pop('num_inference_steps',						lowerCamelCase_ )

					for scheduler_class in self.scheduler_classes:
						snake_case							=					self.get_scheduler_config()
						snake_case							=					scheduler_class(**lowerCamelCase_ )

						snake_case							=					self.dummy_sample
						snake_case							=					0.1 * sample

						if num_inference_steps is not None and hasattr(lowerCamelCase_,						'set_timesteps' ):
							scheduler.set_timesteps(lowerCamelCase_ )
						elif num_inference_steps is not None and not hasattr(lowerCamelCase_,						'set_timesteps' ):
							snake_case							=					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						snake_case							=					[residual + 0.2, residual + 0.15, residual + 0.10]
						snake_case							=					dummy_past_residuals[: scheduler.config.solver_order]

						snake_case							=					scheduler.timesteps[5]
						snake_case							=					scheduler.timesteps[6]

						snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample
						snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						**lowerCamelCase_ ).prev_sample

						self.assertEqual(output_a.shape,						sample.shape )
						self.assertEqual(output_a.shape,						output_a.shape )

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					for timesteps in [25, 50, 100, 999, 1000]:
						self.check_over_configs(num_train_timesteps=lowerCamelCase_ )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					self.check_over_configs(thresholding=lowerCamelCase_ )
					for order in [1, 2, 3]:
						for solver_type in ["midpoint", "heun"]:
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    thresholding=lowerCamelCase_,						prediction_type=lowerCamelCase_,						sample_max_value=lowerCamelCase_,						algorithm_type='dpmsolver++',						solver_order=lowerCamelCase_,						solver_type=lowerCamelCase_,						)

				def 					_lowerCamelCase    (       self )    ->							Dict:
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=lowerCamelCase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
						for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
								for prediction_type in ["epsilon", "sample"]:
									if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
										if order == 3:
											continue
									else:
										self.check_over_configs(
										    solver_order=lowerCamelCase_,						solver_type=lowerCamelCase_,						prediction_type=lowerCamelCase_,						algorithm_type=lowerCamelCase_,						)
									snake_case							=					self.full_loop(
									    solver_order=lowerCamelCase_,						solver_type=lowerCamelCase_,						prediction_type=lowerCamelCase_,						algorithm_type=lowerCamelCase_,						)
									assert not torch.isnan(lowerCamelCase_ ).any(), "Samples have nan numbers"

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					self.check_over_configs(lower_order_final=lowerCamelCase_ )
					self.check_over_configs(lower_order_final=lowerCamelCase_ )

				def 					_lowerCamelCase    (       self )    ->							str:
					self.check_over_configs(lambda_min_clipped=-float('inf' ) )
					self.check_over_configs(lambda_min_clipped=-5.1 )

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					self.check_over_configs(variance_type=lowerCamelCase_ )
					self.check_over_configs(variance_type='learned_range' )

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
						self.check_over_forward(num_inference_steps=lowerCamelCase_,						time_step=0 )

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.full_loop()
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 0.3_301 ) < 1E-3

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.full_loop(thresholding=lowerCamelCase_,						dynamic_thresholding_ratio=0.87,						sample_max_value=0.5 )
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 1.1_364 ) < 1E-3

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					self.full_loop(prediction_type='v_prediction' )
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 0.2_251 ) < 1E-3

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.full_loop(prediction_type='v_prediction',						use_karras_sigmas=lowerCamelCase_ )
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 0.2_096 ) < 1E-3

				def 					_lowerCamelCase    (       self )    ->							Dict:
					# make sure that iterating over schedulers with same config names gives same results
					# for defaults
					snake_case							=					DPMSolverMultistepScheduler(**self.get_scheduler_config() )
					snake_case							=					self.full_loop(scheduler=lowerCamelCase_ )
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 0.3_301 ) < 1E-3

					snake_case							=					DPMSolverSinglestepScheduler.from_config(scheduler.config )
					snake_case							=					UniPCMultistepScheduler.from_config(scheduler.config )
					snake_case							=					DEISMultistepScheduler.from_config(scheduler.config )
					snake_case							=					DPMSolverMultistepScheduler.from_config(scheduler.config )

					snake_case							=					self.full_loop(scheduler=lowerCamelCase_ )
					snake_case							=					torch.mean(torch.abs(lowerCamelCase_ ) )

					assert abs(result_mean.item() - 0.3_301 ) < 1E-3

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					self.scheduler_classes[0]
					snake_case							=					self.get_scheduler_config(thresholding=lowerCamelCase_,						dynamic_thresholding_ratio=0 )
					snake_case							=					scheduler_class(**lowerCamelCase_ )

					snake_case							=					10
					snake_case							=					self.dummy_model()
					snake_case							=					self.dummy_sample_deter.half()
					scheduler.set_timesteps(lowerCamelCase_ )

					for i, t in enumerate(scheduler.timesteps ):
						snake_case							=					model(lowerCamelCase_,						lowerCamelCase_ )
						snake_case							=					scheduler.step(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_ ).prev_sample

					assert sample.dtype == torch.floataa






				def 					_lowerCamelCase    (       self,						**lowercase_ )    ->							Union[str, Any]:
					for scheduler_class in self.scheduler_classes:
						snake_case							=					self.get_scheduler_config(**lowerCamelCase_ )
						snake_case							=					scheduler_class(**lowerCamelCase_ )

						scheduler.set_timesteps(scheduler.config.num_train_timesteps )
						assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps











import math


class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Union[str, Any]	)    -> Optional[Any]:
								__magic_name__				:						Union[str, Any]     				=				x
								__magic_name__				:						Optional[int]     				=				y
								__magic_name__				:						List[str]     				=				step_size
								__magic_name__				:						Union[str, Any]     				=				function_to_optimize

			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								return self.function(self.x							,    self.y	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								__magic_name__				:						Optional[int]     				=				self.step_size
								return [
								    SearchProblem(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    self.function	)
								    for x, y in (
								        (self.x - step_size, self.y - step_size),
								        (self.x - step_size, self.y),
								        (self.x - step_size, self.y + step_size),
								        (self.x, self.y - step_size),
								        (self.x, self.y + step_size),
								        (self.x + step_size, self.y - step_size),
								        (self.x + step_size, self.y),
								        (self.x + step_size, self.y + step_size),
								    )
								]

			def __hash__(			self      : Dict	)    -> Tuple:
								return hash(str(self	)	)

			def __eq__(			self      : List[str]							,    lowerCamelCase_      : Dict	)    -> Union[str, Any]:
								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													return hash(str(self	)	) == hash(str(lowerCamelCase_	)	)
								return False


			def __str__(			self      : Tuple	)    -> Tuple:
								return F'''x: {self.x} y: {self.y}'''







def 		lowercase__ (		__A:						int				,__A:						bool = True				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						bool = False				,__A:						int = 1_0_0_0_0				,):





					'''simple docstring'''






					__magic_name__				:						Union[str, Any]     				=				search_prob
					__magic_name__				:						int     				=				[]  # list to store the current score at each iteration
					__magic_name__				:						Any     				=				0
					__magic_name__				:						Optional[Any]     				=				False
					__magic_name__				:						Dict     				=				set()
					while not solution_found and iterations < max_iter:
										visited.add(__A					)
										iterations += 1
										__magic_name__				:						int     				=				current_state.score()
										scores.append(__A					)
										__magic_name__				:						Union[str, Any]     				=				current_state.get_neighbors()
										__magic_name__				:						str     				=				-math.inf
										__magic_name__				:						Optional[Any]     				=				math.inf
										__magic_name__				:						Any     				=				None  # to hold the next best neighbor
										for neighbor in neighbors:
															if neighbor in visited:
																				continue  # do not want to visit the same state again
															if (
															    neighbor.x > max_x
															    or neighbor.x < min_x
															    or neighbor.y > max_y
															    or neighbor.y < min_y
															):
																				continue  # neighbor outside our bounds
															__magic_name__				:						Tuple     				=				neighbor.score() - current_score
															if find_max:  # finding max
																				# going to direction with greatest ascent
																				if change > max_change and change > 0:
																									__magic_name__				:						List[str]     				=				change
																									__magic_name__				:						List[str]     				=				neighbor
															else:  # finding min
																				# to direction with greatest descent
																				if change < min_change and change < 0:
																									__magic_name__				:						List[Any]     				=				change
																									__magic_name__				:						Optional[int]     				=				neighbor
										if next_state is not None:
															# we found at least one neighbor which improved the current state
															__magic_name__				:						Any     				=				next_state
										else:
															# since we have no neighbor that improves the solution we stop the search
															__magic_name__				:						List[str]     				=				True

					if visualization:
										from matplotlib import pyplot as plt

										plt.plot(range(__A					)				,__A					)
										plt.xlabel('''Iterations'''					)
										plt.ylabel('''Function values'''					)
										plt.show()

					return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()







	def 		lowercase__ (		__A:						Optional[Any]				,__A:						str					):





						'''simple docstring'''






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

	# starting the problem with initial coordinates (3, 4)
	__lowerCamelCase :    Any      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Any      						=      hill_climbing(prob, find_max=False)
	print(
	    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)

	# starting the problem with initial coordinates (12, 47)
	__lowerCamelCase :    Optional[int]      						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Union[str, Any]      						=      hill_climbing(
	    prob, find_max=False, max_x=1_00, 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()}"""
	)







	def 		lowercase__ (		__A:						List[Any]				,__A:						int					):





						'''simple docstring'''






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

	__lowerCamelCase :    List[str]      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Optional[int]      						=      hill_climbing(prob, find_max=True)
	print(
	    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)



