
"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (DDPMParallelScheduler,)





	def   __lowerCAmelCase (						self    :int		, **lowerCamelCase_    :Dict					)							->			int:







			'''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''',
			    '''variance_type''': '''fixed_small''',
			    '''clip_sample''': True,
			}

			config.update(**lowerCamelCase_					)
			return config





	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			for timesteps in [1, 5, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_					)





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







			'''simple docstring'''




			for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		, [0.0_0_2, 0.0_2, 0.2, 2]					):
					self.check_over_configs(beta_start=lowerCamelCase_		, beta_end=lowerCamelCase_					)





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







			'''simple docstring'''




			for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			for variance in ["fixed_small", "fixed_large", "other"]:
					self.check_over_configs(variance_type=lowerCamelCase_					)





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







			'''simple docstring'''




			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=lowerCamelCase_					)





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







			'''simple docstring'''




			self.check_over_configs(thresholding=lowerCamelCase_					)
			for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(
							    thresholding=lowerCamelCase_		, prediction_type=lowerCamelCase_		, sample_max_value=lowerCamelCase_		, )





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







			'''simple docstring'''




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





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







			'''simple docstring'''




			for t in [0, 5_00, 9_99]:
					self.check_over_forward(time_step=lowerCamelCase_					)





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







			'''simple docstring'''




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

			assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0_9_7_9					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.0_2					)					) < 1E-5





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_sample_deter + 0.1
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_sample_deter - 0.1

			SCREAMING_SNAKE_CASE       :     List[str]				= samplea.shape[0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.stack([samplea, samplea, samplea]		, dim=0					)
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.arange(lowerCamelCase_					)[0:3, None].repeat(1		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= model(samples.flatten(0		, 1					)		, timesteps.flatten(0		, 1					)					)
			SCREAMING_SNAKE_CASE       :     List[str]				= scheduler.batch_step_no_noise(lowerCamelCase_		, timesteps.flatten(0		, 1					)		, samples.flatten(0		, 1					)					)

			SCREAMING_SNAKE_CASE       :     Any				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 11_53.18_33					) < 1E-2
			assert abs(result_mean.item() - 0.5_0_0_5					) < 1E-3





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.manual_seed(0					)

			for t in reversed(range(lowerCamelCase_					)					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCamelCase_		, lowerCamelCase_					)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     Tuple				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Dict				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     str				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_58.96_06					) < 1E-2
			assert abs(result_mean.item() - 0.3_3_7_2					) < 1E-3





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_scheduler_config(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     Any				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.manual_seed(0					)

			for t in reversed(range(lowerCamelCase_					)					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model(lowerCamelCase_		, lowerCamelCase_					)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     Tuple				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     Optional[int]				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Any				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_02.02_96					) < 1E-2
			assert abs(result_mean.item() - 0.2_6_3_1					) < 1E-3





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







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Any				= [1_00, 87, 50, 1, 0]

			scheduler.set_timesteps(timesteps=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.timesteps

			for i, timestep in enumerate(lowerCamelCase_					):
					if i == len(lowerCamelCase_					) - 1:
							SCREAMING_SNAKE_CASE       :     List[str]				= -1
					else:
							SCREAMING_SNAKE_CASE       :     int				= timesteps[i + 1]

					SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.previous_timestep(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= prev_t.item()

					self.assertEqual(lowerCamelCase_		, lowerCamelCase_					)





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







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [1_00, 87, 50, 51, 0]

			with self.assertRaises(lowerCamelCase_		, msg='''`custom_timesteps` must be in descending order.'''					):
					scheduler.set_timesteps(timesteps=lowerCamelCase_					)





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







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Tuple				= [1_00, 87, 50, 1, 0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= len(lowerCamelCase_					)

			with self.assertRaises(lowerCamelCase_		, msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''					):
					scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		, timesteps=lowerCamelCase_					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Any				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= [scheduler.config.num_train_timesteps]

			with self.assertRaises(
			    lowerCamelCase_		, msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		, ):
					scheduler.set_timesteps(timesteps=lowerCamelCase_					)







"""simple docstring"""






def __A						(   a_ :							list		,		a_ :							int		,		a_ :							int = 0		,		a_ :							int = 0     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= right or len(a_     ) - 1
		if left > right:
				return -1
		elif list_data[left] == key:
				return left
		elif list_data[right] == key:
				return right
		else:
				return search(a_		,		a_		,		left + 1		,		right - 1     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





