


'''simple docstring'''



import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 							__UpperCamelCase    (		_a ):
	A_           =   (DPMSolverSDEScheduler,)
	A_           =   10




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						__a      :	List[str]    =  {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "noise_sampler_seed": 0,
						}

						config.update(**_a							)
						return config




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''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							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.scheduler_classes[0]
						__a      :	str    =  self.get_scheduler_config()
						__a      :	Tuple    =  scheduler_class(**_a							)

						scheduler.set_timesteps(self.num_inference_steps							)

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

						for i, t in enumerate(scheduler.timesteps							):
											__a      :	List[Any]    =  scheduler.scale_model_input(_a						,    _a							)

											__a      :	Dict    =  model(_a						,    _a							)

											__a      :	Optional[Any]    =  scheduler.step(_a						,    _a						,    _a							)
											__a      :	Optional[int]    =  output.prev_sample

						__a      :	Optional[Any]    =  torch.sum(torch.abs(_a							)							)
						__a      :	Optional[Any]    =  torch.mean(torch.abs(_a							)							)

						if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.47_8210_4492_1875							) < 1E-2
											assert abs(result_mean.item() - 0.2178705964565277							) < 1E-3
						elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_3521_1181_6406							) < 1E-2
											assert abs(result_mean.item() - 0.22342906892299652							) < 1E-3
						else:
											assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
											assert abs(result_mean.item() - 0.211619570851326							) < 1E-3




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.scheduler_classes[0]
						__a      :	Dict    =  self.get_scheduler_config(prediction_type='v_prediction'							)
						__a      :	Any    =  scheduler_class(**_a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a      :	Union[str, Any]    =  self.dummy_model()
						__a      :	List[str]    =  self.dummy_sample_deter * scheduler.init_noise_sigma
						__a      :	Dict    =  sample.to(_a							)

						for i, t in enumerate(scheduler.timesteps							):
											__a      :	Dict    =  scheduler.scale_model_input(_a						,    _a							)

											__a      :	str    =  model(_a						,    _a							)

											__a      :	int    =  scheduler.step(_a						,    _a						,    _a							)
											__a      :	List[Any]    =  output.prev_sample

						__a      :	Optional[Any]    =  torch.sum(torch.abs(_a							)							)
						__a      :	List[Any]    =  torch.mean(torch.abs(_a							)							)

						if torch_device in ["mps"]:
											assert abs(result_sum.item() - 124.77_1492_0043_9453							) < 1E-2
											assert abs(result_mean.item() - 0.16226289014816284							) < 1E-3
						elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 128.1_6633_6059_5703							) < 1E-2
											assert abs(result_mean.item() - 0.16688326001167297							) < 1E-3
						else:
											assert abs(result_sum.item() - 119.8_4875_4882_8125							) < 1E-2
											assert abs(result_mean.item() - 0.1560530662536621							) < 1E-3




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.scheduler_classes[0]
						__a      :	Union[str, Any]    =  self.get_scheduler_config()
						__a      :	Dict    =  scheduler_class(**_a							)

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

						__a      :	Any    =  self.dummy_model()
						__a      :	List[str]    =  self.dummy_sample_deter.to(_a							) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__a      :	Optional[Any]    =  scheduler.scale_model_input(_a						,    _a							)

											__a      :	List[str]    =  model(_a						,    _a							)

											__a      :	str    =  scheduler.step(_a						,    _a						,    _a							)
											__a      :	Optional[Any]    =  output.prev_sample

						__a      :	List[str]    =  torch.sum(torch.abs(_a							)							)
						__a      :	Dict    =  torch.mean(torch.abs(_a							)							)

						if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.46_9573_9746_0938							) < 1E-2
											assert abs(result_mean.item() - 0.21805934607982635							) < 1E-3
						elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_3536_3769_5312							) < 1E-2
											assert abs(result_mean.item() - 0.22342908382415771							) < 1E-3
						else:
											assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
											assert abs(result_mean.item() - 0.211619570851326							) < 1E-3





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.scheduler_classes[0]
						__a      :	Optional[Any]    =  self.get_scheduler_config()
						__a      :	List[Any]    =  scheduler_class(**_a						,    use_karras_sigmas=_a							)

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

						__a      :	List[str]    =  self.dummy_model()
						__a      :	List[str]    =  self.dummy_sample_deter.to(_a							) * scheduler.init_noise_sigma
						__a      :	Optional[int]    =  sample.to(_a							)

						for t in scheduler.timesteps:
											__a      :	Tuple    =  scheduler.scale_model_input(_a						,    _a							)

											__a      :	Any    =  model(_a						,    _a							)

											__a      :	int    =  scheduler.step(_a						,    _a						,    _a							)
											__a      :	int    =  output.prev_sample

						__a      :	List[Any]    =  torch.sum(torch.abs(_a							)							)
						__a      :	List[str]    =  torch.mean(torch.abs(_a							)							)

						if torch_device in ["mps"]:
											assert abs(result_sum.item() - 176.66_9741_3574_2188							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
						elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 177.63_6535_6445_3125							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
						else:
											assert abs(result_sum.item() - 170.3_1352_2338_8672							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       int       ):
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										raise ValueError('iterations must be defined as integers'       )
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'       )
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'       )

					__a      :	Dict    =  ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(_SCREAMING_SNAKE_CASE       )

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()



