





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	:	Tuple		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self		,      lowerCAmelCase__=3_2							)   ->						Tuple:



        '''simple docstring'''




        set_seed(0							)
        a__	:					Optional[int]										=UNetaDModel(sample_size=lowerCAmelCase__		,      in_channels=3		,      out_channels=3							)
        a__	:					Optional[int]										=torch.optim.SGD(model.parameters()		,      lr=0.00_01							)
        return model, optimizer







      @slow
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										="cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        a__	:					Optional[Any]										=DDPMScheduler(
            num_train_timesteps=1_0_0_0		,      beta_start=0.00_01		,      beta_end=0.02		,      beta_schedule="linear"		,      clip_sample=lowerCAmelCase__		,      )
        a__	:					Any										=DDIMScheduler(
            num_train_timesteps=1_0_0_0		,      beta_start=0.00_01		,      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							)
        a__	:					Tuple										=[torch.randn((4, 3, 3_2, 3_2)							).clip(-1		,      1							).to(lowerCAmelCase__							) for _ in range(4							)]
        a__	:					Tuple										=[torch.randn((4, 3, 3_2, 3_2)							).to(lowerCAmelCase__							) for _ in range(4							)]
        a__	:					Union[str, Any]										=[torch.randint(0		,      1_0_0_0		,      (4,)							).long().to(lowerCAmelCase__							) for _ in range(4							)]

        # train with a DDPM scheduler
        a__					,							a__	:					Optional[Any]										=self.get_model_optimizer(resolution=3_2							)
        model.train().to(lowerCAmelCase__							)
        for i in range(4							):
          optimizer.zero_grad()
          a__	:					List[Any]										=ddpm_scheduler.add_noise(clean_images[i]		,      noise[i]		,      timesteps[i]							)
          a__	:					Optional[int]										=model(lowerCAmelCase__		,      timesteps[i]							).sample
          a__	:					int										=torch.nn.functional.mse_loss(lowerCAmelCase__		,      noise[i]							)
          loss.backward()
          optimizer.step()
        del model, optimizer

        # recreate the model and optimizer, and retry with DDIM
        a__					,							a__	:					List[str]										=self.get_model_optimizer(resolution=3_2							)
        model.train().to(lowerCAmelCase__							)
        for i in range(4							):
          optimizer.zero_grad()
          a__	:					Union[str, Any]										=ddim_scheduler.add_noise(clean_images[i]		,      noise[i]		,      timesteps[i]							)
          a__	:					Optional[Any]										=model(lowerCAmelCase__		,      timesteps[i]							).sample
          a__	:					List[Any]										=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      (      _SCREAMING_SNAKE_CASE    )	-> list:
       if n_term == "":
              return []
       lowerCamelCase   :      list			 =					[]
       for temp in range(int(_SCREAMING_SNAKE_CASE    )    ):
              series.append(f'''1/{temp + 1}''' if series else "1"    )
       return series


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   input('Enter the last number (nth term) of the Harmonic Series')
       print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
       print(harmonic_series(nth_term))






