'''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


__a: str          =   False



class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self	,   __lowerCAmelCase=32 )  ->  List[Any]:
        set_seed(0 )
        lowercase__				:    Union[str, Any]  =					UNetaDModel(sample_size=__lowerCAmelCase	,   in_channels=3	,   out_channels=3 )
        lowercase__				:    Dict  =					torch.optim.SGD(model.parameters()	,   lr=0.0_0_0_1 )
        return model, optimizer


       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    List[str]  =					'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        lowercase__				:    Union[str, Any]  =					DDPMScheduler(
            num_train_timesteps=1000	,   beta_start=0.0_0_0_1	,   beta_end=0.0_2	,   beta_schedule='''linear'''	,   clip_sample=__lowerCAmelCase	,   )
        lowercase__				:    Optional[int]  =					DDIMScheduler(
            num_train_timesteps=1000	,   beta_start=0.0_0_0_1	,   beta_end=0.0_2	,   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__				:    int  =					[torch.randn((4, 3, 32, 32) ).clip(-1	,   1 ).to(__lowerCAmelCase ) for _ in range(4 )]
        lowercase__				:    str  =					[torch.randn((4, 3, 32, 32) ).to(__lowerCAmelCase ) for _ in range(4 )]
        lowercase__				:    Any  =					[torch.randint(0	,   1000	,   (4,) ).long().to(__lowerCAmelCase ) for _ in range(4 )]

        # train with a DDPM scheduler
        lowercase__ ,							lowercase__				:    Optional[int]  =					self.get_model_optimizer(resolution=32 )
        model.train().to(__lowerCAmelCase )
        for i in range(4 ):
         optimizer.zero_grad()
         lowercase__				:    List[Any]  =					ddpm_scheduler.add_noise(clean_images[i]	,   noise[i]	,   timesteps[i] )
         lowercase__				:    int  =					model(__lowerCAmelCase	,   timesteps[i] ).sample
         lowercase__				:    Optional[Any]  =					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__				:    int  =					self.get_model_optimizer(resolution=32 )
        model.train().to(__lowerCAmelCase )
        for i in range(4 ):
         optimizer.zero_grad()
         lowercase__				:    Union[str, Any]  =					ddim_scheduler.add_noise(clean_images[i]	,   noise[i]	,   timesteps[i] )
         lowercase__				:    List[Any]  =					model(__lowerCAmelCase	,   timesteps[i] ).sample
         lowercase__				:    List[str]  =					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 ) )


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

