





'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   (CMStochasticIterativeScheduler,)
	snake_case_      =   10



	def   _lowerCamelCase    (   self		,      **a_	):
			lowerCAmelCase     :    str						=   {
			    "num_train_timesteps": 201,
			    "sigma_min": 0.002,
			    "sigma_max": 80.0,
			}

			config.update(**a_	)
			return config



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   10

			lowerCAmelCase     :    Optional[Any]						=   self.get_scheduler_config()
			lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0](**a_	)

			scheduler.set_timesteps(a_	)

			lowerCAmelCase     :    str						=   scheduler.timesteps[0]
			lowerCAmelCase     :    int						=   scheduler.timesteps[1]

			lowerCAmelCase     :    Dict						=   self.dummy_sample
			lowerCAmelCase     :    str						=   0.1 * sample

			lowerCAmelCase     :    Tuple						=   scheduler.step(a_		,      a_		,      a_	).prev_sample
			lowerCAmelCase     :    Union[str, Any]						=   scheduler.step(a_		,      a_		,      a_	).prev_sample

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



	def   _lowerCamelCase    (   self	):
			for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=a_	)



	def   _lowerCamelCase    (   self	):
			for clip_denoised in [True, False]:
					self.check_over_configs(clip_denoised=a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.scheduler_classes[0]
			lowerCAmelCase     :    str						=   self.get_scheduler_config()
			lowerCAmelCase     :    List[str]						=   scheduler_class(**a_	)

			lowerCAmelCase     :    str						=   1
			scheduler.set_timesteps(a_	)
			lowerCAmelCase     :    List[str]						=   scheduler.timesteps

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)

			lowerCAmelCase     :    Tuple						=   self.dummy_model()
			lowerCAmelCase     :    Tuple						=   self.dummy_sample_deter * scheduler.init_noise_sigma

			for i, t in enumerate(a_	):
					# 1. scale model input
					lowerCAmelCase     :    int						=   scheduler.scale_model_input(a_		,      a_	)

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

					# 3. predict previous sample x_t-1
					lowerCAmelCase     :    List[str]						=   scheduler.step(a_		,      a_		,      a_		,      generator=a_	).prev_sample

					lowerCAmelCase     :    int						=   pred_prev_sample

			lowerCAmelCase     :    Tuple						=   torch.sum(torch.abs(a_	)	)
			lowerCAmelCase     :    List[str]						=   torch.mean(torch.abs(a_	)	)

			assert abs(result_sum.item() - 192.7614	) < 1e-2
			assert abs(result_mean.item() - 0.2510	) < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0]
			lowerCAmelCase     :    int						=   self.get_scheduler_config()
			lowerCAmelCase     :    int						=   scheduler_class(**a_	)

			lowerCAmelCase     :    Optional[int]						=   [106, 0]
			scheduler.set_timesteps(timesteps=a_	)
			lowerCAmelCase     :    Any						=   scheduler.timesteps

			lowerCAmelCase     :    int						=   torch.manual_seed(0	)

			lowerCAmelCase     :    Any						=   self.dummy_model()
			lowerCAmelCase     :    str						=   self.dummy_sample_deter * scheduler.init_noise_sigma

			for t in timesteps:
					# 1. scale model input
					lowerCAmelCase     :    Optional[Any]						=   scheduler.scale_model_input(a_		,      a_	)

					# 2. predict noise residual
					lowerCAmelCase     :    Any						=   model(a_		,      a_	)

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

					lowerCAmelCase     :    Union[str, Any]						=   pred_prev_sample

			lowerCAmelCase     :    int						=   torch.sum(torch.abs(a_	)	)
			lowerCAmelCase     :    Tuple						=   torch.mean(torch.abs(a_	)	)

			assert abs(result_sum.item() - 347.6357	) < 1e-2
			assert abs(result_mean.item() - 0.4527	) < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0]
			lowerCAmelCase     :    Optional[Any]						=   self.get_scheduler_config()
			lowerCAmelCase     :    List[Any]						=   scheduler_class(**a_	)

			lowerCAmelCase     :    int						=   [39, 30, 12, 15, 0]

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.scheduler_classes[0]
			lowerCAmelCase     :    Any						=   self.get_scheduler_config()
			lowerCAmelCase     :    Any						=   scheduler_class(**a_	)

			lowerCAmelCase     :    Optional[Any]						=   [39, 30, 12, 1, 0]
			lowerCAmelCase     :    str						=   len(a_	)

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





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.scheduler_classes[0]
			lowerCAmelCase     :    List[Any]						=   self.get_scheduler_config()
			lowerCAmelCase     :    str						=   scheduler_class(**a_	)

			lowerCAmelCase     :    Union[str, Any]						=   [scheduler.config.num_train_timesteps]

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










'''simple docstring'''






from __future__ import annotations






def      __A						(							a_						:  float      ,a_						:  float      ,a_						:  float      ,):
		if (stress, tangential_force, area).count(0	) != 1:
				raise ValueError("You cannot supply more or less than 2 values"	)
		elif stress < 0:
				raise ValueError("Stress cannot be negative"	)
		elif tangential_force < 0:
				raise ValueError("Tangential Force cannot be negative"	)
		elif area < 0:
				raise ValueError("Area cannot be negative"	)
		elif stress == 0:
				return (
				    "stress",
				    tangential_force / area,
				)
		elif tangential_force == 0:
				return (
				    "tangential_force",
				    stress * area,
				)
		else:
				return (
				    "area",
				    tangential_force / stress,
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



