






import unittest

import torch

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


A__			:    Any         =				False
class    _UpperCAmelCase      ( unittest.TestCase			):

    """simple docstring"""




    def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[Any]=32						):

        '''simple docstring'''
        set_seed(0						)
        lowercase__     				=    UNetaDModel(sample_size=lowerCamelCase,						in_channels=3,						out_channels=3						)
        lowercase__     				=    torch.optim.SGD(model.parameters(),						lr=0.0001						)
        return model, optimizer




    @slow
    def        lowercase__     (  self		:					List[Any]						):

        '''simple docstring'''
        lowercase__     				=    '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        lowercase__     				=    DDPMScheduler(
            num_train_timesteps=1_000,						beta_start=0.0001,						beta_end=0.02,						beta_schedule='''linear''',						clip_sample=lowerCamelCase,						)
        lowercase__     				=    DDIMScheduler(
            num_train_timesteps=1_000,						beta_start=0.0001,						beta_end=0.02,						beta_schedule='''linear''',						clip_sample=lowerCamelCase,						)

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

        # shared batches for DDPM and DDIM
        set_seed(0						)
        lowercase__     				=    [torch.randn((4, 3, 32, 32)						).clip(-1,						1						).to(lowerCamelCase						) for _ in range(4						)]
        lowercase__     				=    [torch.randn((4, 3, 32, 32)						).to(lowerCamelCase						) for _ in range(4						)]
        lowercase__     				=    [torch.randint(0,						1_000,						(4,)						).long().to(lowerCamelCase						) for _ in range(4						)]

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

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

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












def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				if exponent == 1:
								return base
				if exponent % 2 == 0:
								lowercase__     				=    _modexpt(lowerCamelCase_	,     exponent // 2	,     lowerCamelCase_       ) % modulo_value
								return (x * x) % modulo_value
				else:
								return (base * _modexpt(lowerCamelCase_	,     exponent - 1	,     lowerCamelCase_       )) % modulo_value






def 						a    (		lowerCamelCase_ = 1777	,     lowerCamelCase_ = 1855	,     lowerCamelCase_ = 8       ):






				'''simple docstring'''
				lowercase__     				=    base
				for _ in range(1	,     lowerCamelCase_       ):
								lowercase__     				=    _modexpt(lowerCamelCase_	,     lowerCamelCase_	,     10**digits       )

				return result


if __name__ == "__main__":
							print(F"{solution() = }")




