


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


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

							def        __snake_case   (				self			:    Any       ,      **lowerCamelCase			:    Union[str, Any]							)      ->       Optional[Any]:
														__snake_case						:     Union[str, Any]									=  {
														    "num_train_timesteps": 201,
														    "sigma_min": 0.0_02,
														    "sigma_max": 80.0,
														}

														config.update(**lowerCamelCase							)
														return config

							def        __snake_case   (				self			:    Optional[int]							)      ->       List[str]:
														__snake_case						:     Optional[int]									=  10

														__snake_case						:     Any									=  self.get_scheduler_config()
														__snake_case						:     List[str]									=  self.scheduler_classes[0](**lowerCamelCase							)

														scheduler.set_timesteps(lowerCamelCase							)

														__snake_case						:     List[Any]									=  scheduler.timesteps[0]
														__snake_case						:     List[Any]									=  scheduler.timesteps[1]

														__snake_case						:     int									=  self.dummy_sample
														__snake_case						:     Optional[int]									=  0.1 * sample

														__snake_case						:     str									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample
														__snake_case						:     str									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample

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

							def        __snake_case   (				self			:    Dict							)      ->       str:
														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Dict:
														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														__snake_case						:     Tuple									=  self.scheduler_classes[0]
														__snake_case						:     Union[str, Any]									=  self.get_scheduler_config()
														__snake_case						:     Any									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  1
														scheduler.set_timesteps(lowerCamelCase							)
														__snake_case						:     Union[str, Any]									=  scheduler.timesteps

														__snake_case						:     Optional[Any]									=  torch.manual_seed(0							)

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

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

																					# 2. predict noise residual
																					__snake_case						:     Union[str, Any]									=  model(lowerCamelCase       ,      lowerCamelCase							)

																					# 3. predict previous sample x_t-1
																					__snake_case						:     Any									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      generator=lowerCamelCase							).prev_sample

																					__snake_case						:     Tuple									=  pred_prev_sample

														__snake_case						:     Union[str, Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     List[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        __snake_case   (				self			:    str							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.scheduler_classes[0]
														__snake_case						:     List[Any]									=  self.get_scheduler_config()
														__snake_case						:     Union[str, Any]									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Tuple									=  [106, 0]
														scheduler.set_timesteps(timesteps=lowerCamelCase							)
														__snake_case						:     Union[str, Any]									=  scheduler.timesteps

														__snake_case						:     List[str]									=  torch.manual_seed(0							)

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

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

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

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

																					__snake_case						:     List[Any]									=  pred_prev_sample

														__snake_case						:     Any									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Optional[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        __snake_case   (				self			:    List[str]							)      ->       List[Any]:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     str									=  self.get_scheduler_config()
														__snake_case						:     int									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Any									=  [39, 30, 12, 15, 0]

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

							def        __snake_case   (				self			:    Tuple							)      ->       Union[str, Any]:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     int									=  self.get_scheduler_config()
														__snake_case						:     Any									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Tuple									=  [39, 30, 12, 1, 0]
														__snake_case						:     Union[str, 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        __snake_case   (				self			:    List[Any]							)      ->       Optional[int]:
														__snake_case						:     str									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     int									=  [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"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )