



'''simple docstring'''




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		(CMStochasticIterativeScheduler,)
			_A      :		Optional[int]			    =		10

			def UpperCamelCase__					(							self     : Any							,     **lowerCAmelCase__     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{
					    """num_train_timesteps""": 2_0_1,
					    """sigma_min""": 0.0_02,
					    """sigma_max""": 80.0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		1_0

					__SCREAMING_SNAKE_CASE       :		str           =		self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE       :		Any           =		self.scheduler_classes[0](**lowerCAmelCase__						)

					scheduler.set_timesteps(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		scheduler.timesteps[0]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		scheduler.timesteps[1]

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.dummy_sample
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		0.1 * sample

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		scheduler.step(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						).prev_sample
					__SCREAMING_SNAKE_CASE       :		List[str]           =		scheduler.step(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						).prev_sample

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

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE       :		str           =		scheduler_class(**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		1
					scheduler.set_timesteps(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		scheduler.timesteps

					__SCREAMING_SNAKE_CASE       :		Tuple           =		torch.manual_seed(0						)

					__SCREAMING_SNAKE_CASE       :		Dict           =		self.dummy_model()
					__SCREAMING_SNAKE_CASE       :		int           =		self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(lowerCAmelCase__						):
							# 1. scale model input
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		scheduler.scale_model_input(lowerCAmelCase__							,     lowerCAmelCase__						)

							# 2. predict noise residual
							__SCREAMING_SNAKE_CASE       :		List[str]           =		model(lowerCAmelCase__							,     lowerCAmelCase__						)

							# 3. predict previous sample x_t-1
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		scheduler.step(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     generator=lowerCAmelCase__						).prev_sample

							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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() - 1_92.76_14						) < 1E-2
					assert abs(result_mean.item() - 0.25_10						) < 1E-3

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE       :		List[str]           =		scheduler_class(**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[1_0_6, 0]
					scheduler.set_timesteps(timesteps=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		scheduler.timesteps

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.manual_seed(0						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.dummy_model()
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.dummy_sample_deter * scheduler.init_noise_sigma

					for t in timesteps:
							# 1. scale model input
							__SCREAMING_SNAKE_CASE       :		Tuple           =		scheduler.scale_model_input(lowerCAmelCase__							,     lowerCAmelCase__						)

							# 2. predict noise residual
							__SCREAMING_SNAKE_CASE       :		str           =		model(lowerCAmelCase__							,     lowerCAmelCase__						)

							# 3. predict previous sample x_t-1
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		scheduler.step(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     generator=lowerCAmelCase__						).prev_sample

							__SCREAMING_SNAKE_CASE       :		int           =		pred_prev_sample

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.sum(torch.abs(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE       :		int           =		torch.mean(torch.abs(lowerCAmelCase__						)						)

					assert abs(result_sum.item() - 3_47.63_57						) < 1E-2
					assert abs(result_mean.item() - 0.45_27						) < 1E-3

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE       :		List[str]           =		[3_9, 3_0, 1_2, 1_5, 0]

					with self.assertRaises(lowerCAmelCase__							,     msg="""`timesteps` must be in descending order."""						):
							scheduler.set_timesteps(timesteps=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE       :		str           =		self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		scheduler_class(**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		[3_9, 3_0, 1_2, 1, 0]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		len(lowerCAmelCase__						)

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



			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE       :		int           =		scheduler_class(**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		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'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]					):
		if not nums:
				return 0
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nums[0]
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for num in nums[1:]:
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		(
				    max_excluding + num,
				    max(_lowerCamelCase       ,      _lowerCamelCase					),
				)
		return max(_lowerCamelCase       ,      _lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()