
"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (DPMSolverSinglestepScheduler,)
	UpperCamelCase				=  (("""num_inference_steps""", 25),)





	def   __lowerCAmelCase (						self    :List[str]		, **lowerCamelCase_    :int					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= {
			    '''num_train_timesteps''': 10_00,
			    '''beta_start''': 0.0_0_0_1,
			    '''beta_end''': 0.0_2,
			    '''beta_schedule''': '''linear''',
			    '''solver_order''': 2,
			    '''prediction_type''': '''epsilon''',
			    '''thresholding''': False,
			    '''sample_max_value''': 1.0,
			    '''algorithm_type''': '''dpmsolver++''',
			    '''solver_type''': '''midpoint''',
			    '''lambda_min_clipped''': -float('''inf'''					),
			    '''variance_type''': None,
			}

			config.update(**lowerCamelCase_					)
			return config





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :List[Any]=0		, **lowerCamelCase_    :Optional[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= dict(self.forward_default_kwargs					)
			SCREAMING_SNAKE_CASE       :     List[str]				= kwargs.pop('''num_inference_steps'''		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.dummy_sample
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0.1 * sample
			SCREAMING_SNAKE_CASE       :     Tuple				= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= sample, sample
					for t in range(lowerCamelCase_		, time_step + scheduler.config.solver_order + 1					):
							SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_					).prev_sample
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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    :Optional[Any]					)							->			List[Any]:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Optional[Any]=0		, **lowerCamelCase_    :Tuple					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= dict(self.forward_default_kwargs					)
			SCREAMING_SNAKE_CASE       :     List[str]				= kwargs.pop('''num_inference_steps'''		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.dummy_sample
			SCREAMING_SNAKE_CASE       :     List[str]				= 0.1 * sample
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

			for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE       :     str				= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE       :     Tuple				= scheduler_class(**lowerCamelCase_					)
					scheduler.set_timesteps(lowerCamelCase_					)

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     List[Any]				= 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)
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= dummy_past_residuals[: new_scheduler.config.solver_order]

					SCREAMING_SNAKE_CASE       :     Any				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_					).prev_sample
					SCREAMING_SNAKE_CASE       :     str				= 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    :Any		, lowerCamelCase_    :Optional[Any]=None		, **lowerCamelCase_    :List[str]					)							->			str:







			'''simple docstring'''




			if scheduler is None:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE       :     Dict				= self.get_scheduler_config(**lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Any				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_scheduler_config(**lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     str				= 10
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					).prev_sample

			return sample





	def   __lowerCAmelCase (						self    :int					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= 50
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_					)

			# make sure that the first t is uneven
			for i, t in enumerate(scheduler.timesteps[3:]					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					).prev_sample

			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.2_5_7_4					) < 1E-3





	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			for timesteps in [25, 50, 1_00, 9_99, 10_00]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.full_loop(scheduler=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.2_7_9_1					) < 1E-3

			SCREAMING_SNAKE_CASE       :     str				= DEISMultistepScheduler.from_config(scheduler.config					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= DPMSolverMultistepScheduler.from_config(scheduler.config					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= UniPCMultistepScheduler.from_config(scheduler.config					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= DPMSolverSinglestepScheduler.from_config(scheduler.config					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.full_loop(scheduler=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.2_7_9_1					) < 1E-3





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			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    :Any					)							->			Tuple:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Any					)							->			List[str]:







			'''simple docstring'''




			for algorithm_type in ["dpmsolver", "dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=lowerCamelCase_		, solver_type=lowerCamelCase_		, prediction_type=lowerCamelCase_		, algorithm_type=lowerCamelCase_		, )
											SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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    :Any					)							->			Tuple:







			'''simple docstring'''




			self.check_over_configs(lower_order_final=lowerCamelCase_					)
			self.check_over_configs(lower_order_final=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Any:







			'''simple docstring'''




			self.check_over_configs(lambda_min_clipped=-float('''inf'''					)					)
			self.check_over_configs(lambda_min_clipped=-5.1					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			self.check_over_configs(variance_type=lowerCamelCase_					)
			self.check_over_configs(variance_type='''learned_range'''					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
					self.check_over_forward(num_inference_steps=lowerCamelCase_		, time_step=0					)





	def   __lowerCAmelCase (						self    :str					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.full_loop()
			SCREAMING_SNAKE_CASE       :     int				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.2_7_9_1					) < 1E-3





	def   __lowerCAmelCase (						self    :List[Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.full_loop(use_karras_sigmas=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.2_2_4_8					) < 1E-3





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.full_loop(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.1_4_5_3					) < 1E-3





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.full_loop(prediction_type='''v_prediction'''		, use_karras_sigmas=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_mean.item() - 0.0_6_4_9					) < 1E-3







	def   __lowerCAmelCase (						self    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_scheduler_config(thresholding=lowerCamelCase_		, dynamic_thresholding_ratio=0					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= 10
			SCREAMING_SNAKE_CASE       :     Any				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Tuple				= self.dummy_sample_deter.half()
			scheduler.set_timesteps(lowerCamelCase_					)

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

			assert sample.dtype == torch.floataa







"""simple docstring"""






import math




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :List[str]=0					)							->			List[Any]:  # a graph with Node 0,1,...,N-1







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= n
			SCREAMING_SNAKE_CASE       :     List[Any]				= [
			    [math.inf for j in range(0		, lowerCamelCase_					)] for i in range(0		, lowerCamelCase_					)
			]  # adjacency matrix for weight
			SCREAMING_SNAKE_CASE       :     Any				= [
			    [math.inf for j in range(0		, lowerCamelCase_					)] for i in range(0		, lowerCamelCase_					)
			]  # dp[i][j] stores minimum distance from i to j





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= w





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







			'''simple docstring'''




			for k in range(0		, self.n					):
					for i in range(0		, self.n					):
							for j in range(0		, self.n					):
									SCREAMING_SNAKE_CASE       :     List[str]				= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]					)







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







			'''simple docstring'''




			return self.dp[u][v]


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)





