

'''simple docstring'''


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (CMStochasticIterativeScheduler,)
						__UpperCAmelCase			     =    10







						def       __magic_name__							(							self					:      Tuple			,			**snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  {
								    '''num_train_timesteps''': 2_0_1,
								    '''sigma_min''': 0.0_0_2,
								    '''sigma_max''': 8_0.0,
								}

								config.update(**snake_case_  )
								return config







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  1_0

								snake_case__						:       Any       						=  self.get_scheduler_config()
								snake_case__						:       str       						=  self.scheduler_classes[0](**snake_case_  )

								scheduler.set_timesteps(snake_case_  )

								snake_case__						:       List[str]       						=  scheduler.timesteps[0]
								snake_case__						:       str       						=  scheduler.timesteps[1]

								snake_case__						:       Tuple       						=  self.dummy_sample
								snake_case__						:       List[str]       						=  0.1 * sample

								snake_case__						:       int       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample
								snake_case__						:       Optional[int]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample

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







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.scheduler_classes[0]
								snake_case__						:       Union[str, Any]       						=  self.get_scheduler_config()
								snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       List[Any]       						=  1
								scheduler.set_timesteps(snake_case_  )
								snake_case__						:       Optional[int]       						=  scheduler.timesteps

								snake_case__						:       List[Any]       						=  torch.manual_seed(0  )

								snake_case__						:       Dict       						=  self.dummy_model()
								snake_case__						:       List[Any]       						=  self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(snake_case_  ):
										# 1. scale model input
										snake_case__						:       List[str]       						=  scheduler.scale_model_input(snake_case_			,			snake_case_  )

										# 2. predict noise residual
										snake_case__						:       Optional[Any]       						=  model(snake_case_			,			snake_case_  )

										# 3. predict previous sample x_t-1
										snake_case__						:       str       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			generator=snake_case_  ).prev_sample

										snake_case__						:       str       						=  pred_prev_sample

								snake_case__						:       int       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       Optional[int]       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 1_9_2.7_6_1_4  ) < 1e-2
								assert abs(result_mean.item() - 0.2_5_1_0  ) < 1e-3







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.scheduler_classes[0]
								snake_case__						:       int       						=  self.get_scheduler_config()
								snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       List[str]       						=  [1_0_6, 0]
								scheduler.set_timesteps(timesteps=snake_case_  )
								snake_case__						:       Optional[int]       						=  scheduler.timesteps

								snake_case__						:       Any       						=  torch.manual_seed(0  )

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

								for t in timesteps:
										# 1. scale model input
										snake_case__						:       Optional[int]       						=  scheduler.scale_model_input(snake_case_			,			snake_case_  )

										# 2. predict noise residual
										snake_case__						:       Tuple       						=  model(snake_case_			,			snake_case_  )

										# 3. predict previous sample x_t-1
										snake_case__						:       Optional[int]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			generator=snake_case_  ).prev_sample

										snake_case__						:       Union[str, Any]       						=  pred_prev_sample

								snake_case__						:       Dict       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       str       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 3_4_7.6_3_5_7  ) < 1e-2
								assert abs(result_mean.item() - 0.4_5_2_7  ) < 1e-3







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



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

								snake_case__						:       List[Any]       						=  [3_9, 3_0, 1_2, 1_5, 0]

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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.scheduler_classes[0]
								snake_case__						:       Optional[Any]       						=  self.get_scheduler_config()
								snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       List[str]       						=  [3_9, 3_0, 1_2, 1, 0]
								snake_case__						:       Optional[int]       						=  len(snake_case_  )

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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.scheduler_classes[0]
								snake_case__						:       Union[str, Any]       						=  self.get_scheduler_config()
								snake_case__						:       int       						=  scheduler_class(**snake_case_  )

								snake_case__						:       List[str]       						=  [scheduler.config.num_train_timesteps]

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






'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)



