


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		(EulerDiscreteScheduler,)
			_UpperCamelCase		:					Dict    =		10




			def 				snake_case__				(							self      ,						**snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**snake_case     )
						return config




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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




			def 				snake_case__				(							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=snake_case      ,						beta_end=snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.scheduler_classes[0]
						UpperCamelCase__					=       self.get_scheduler_config()
						UpperCamelCase__					=       scheduler_class(**snake_case     )

						scheduler.set_timesteps(self.num_inference_steps     )

						UpperCamelCase__					=       torch.manual_seed(0     )

						UpperCamelCase__					=       self.dummy_model()
						UpperCamelCase__					=       self.dummy_sample_deter * scheduler.init_noise_sigma
						UpperCamelCase__					=       sample.to(snake_case     )

						for i, t in enumerate(scheduler.timesteps     ):
									UpperCamelCase__					=       scheduler.scale_model_input(snake_case      ,						snake_case     )

									UpperCamelCase__					=       model(snake_case      ,						snake_case     )

									UpperCamelCase__					=       scheduler.step(snake_case      ,						snake_case      ,						snake_case      ,						generator=snake_case     )
									UpperCamelCase__					=       output.prev_sample

						UpperCamelCase__					=       torch.sum(torch.abs(snake_case     )     )
						UpperCamelCase__					=       torch.mean(torch.abs(snake_case     )     )

						assert abs(result_sum.item() - 10.0807     ) < 1E-2
						assert abs(result_mean.item() - 0.0131     ) < 1E-3




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.scheduler_classes[0]
						UpperCamelCase__					=       self.get_scheduler_config(prediction_type="v_prediction"     )
						UpperCamelCase__					=       scheduler_class(**snake_case     )

						scheduler.set_timesteps(self.num_inference_steps     )

						UpperCamelCase__					=       torch.manual_seed(0     )

						UpperCamelCase__					=       self.dummy_model()
						UpperCamelCase__					=       self.dummy_sample_deter * scheduler.init_noise_sigma
						UpperCamelCase__					=       sample.to(snake_case     )

						for i, t in enumerate(scheduler.timesteps     ):
									UpperCamelCase__					=       scheduler.scale_model_input(snake_case      ,						snake_case     )

									UpperCamelCase__					=       model(snake_case      ,						snake_case     )

									UpperCamelCase__					=       scheduler.step(snake_case      ,						snake_case      ,						snake_case      ,						generator=snake_case     )
									UpperCamelCase__					=       output.prev_sample

						UpperCamelCase__					=       torch.sum(torch.abs(snake_case     )     )
						UpperCamelCase__					=       torch.mean(torch.abs(snake_case     )     )

						assert abs(result_sum.item() - 0.0002     ) < 1E-2
						assert abs(result_mean.item() - 2.2_6_7_6E-0_6     ) < 1E-3




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.scheduler_classes[0]
						UpperCamelCase__					=       self.get_scheduler_config()
						UpperCamelCase__					=       scheduler_class(**snake_case     )

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

						UpperCamelCase__					=       torch.manual_seed(0     )

						UpperCamelCase__					=       self.dummy_model()
						UpperCamelCase__					=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCamelCase__					=       sample.to(snake_case     )

						for t in scheduler.timesteps:
									UpperCamelCase__					=       scheduler.scale_model_input(snake_case      ,						snake_case     )

									UpperCamelCase__					=       model(snake_case      ,						snake_case     )

									UpperCamelCase__					=       scheduler.step(snake_case      ,						snake_case      ,						snake_case      ,						generator=snake_case     )
									UpperCamelCase__					=       output.prev_sample

						UpperCamelCase__					=       torch.sum(torch.abs(snake_case     )     )
						UpperCamelCase__					=       torch.mean(torch.abs(snake_case     )     )

						assert abs(result_sum.item() - 10.0807     ) < 1E-2
						assert abs(result_mean.item() - 0.0131     ) < 1E-3







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.scheduler_classes[0]
						UpperCamelCase__					=       self.get_scheduler_config()
						UpperCamelCase__					=       scheduler_class(**snake_case      ,						use_karras_sigmas=snake_case     )

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

						UpperCamelCase__					=       torch.manual_seed(0     )

						UpperCamelCase__					=       self.dummy_model()
						UpperCamelCase__					=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCamelCase__					=       sample.to(snake_case     )

						for t in scheduler.timesteps:
									UpperCamelCase__					=       scheduler.scale_model_input(snake_case      ,						snake_case     )

									UpperCamelCase__					=       model(snake_case      ,						snake_case     )

									UpperCamelCase__					=       scheduler.step(snake_case      ,						snake_case      ,						snake_case      ,						generator=snake_case     )
									UpperCamelCase__					=       output.prev_sample

						UpperCamelCase__					=       torch.sum(torch.abs(snake_case     )     )
						UpperCamelCase__					=       torch.mean(torch.abs(snake_case     )     )

						assert abs(result_sum.item() - 124.52299499511719     ) < 1E-2
						assert abs(result_mean.item() - 0.16213932633399963     ) < 1E-3






def        UpperCamelCase_(				_A   :List[str]				,     _A   :Tuple				,     _A   :Any				,     _A   :Tuple				,     _A   :Optional[int]				,     _A   :str  )->		List[str]:
			if index == r:
						for j in range(_A  ):
									print(data[j]				,     end=" "  )
						print(" "  )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			UpperCamelCase__					=       arr[i]
			combination_util(_A				,     _A				,     _A				,     index + 1				,     _A				,     i + 1  )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(_A				,     _A				,     _A				,     _A				,     _A				,     i + 1  )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()
def        UpperCamelCase_(				_A   :Dict				,     _A   :Optional[Any]				,     _A   :Union[str, Any]  )->		Optional[int]:
			# A temporary array to store all combination one by one
			UpperCamelCase__					=       [0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(_A				,     _A				,     _A				,     0				,     _A				,     0  )


if __name__ == "__main__":
						# Driver code to check the function above
						__UpperCamelCase    		=       [1_0, 2_0, 3_0, 4_0, 5_0]
						print_combination(arr, len(arr), 3)
						# This code is contributed by Ambuj sahu


