
"""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		:       Dict     						=       False







class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=3_2   )		->     str:







         """simple docstring"""



         set_seed(0   )
         a__		=			UNetaDModel(sample_size=__SCREAMING_SNAKE_CASE     ,				in_channels=3     ,				out_channels=3   )
         a__		=			torch.optim.SGD(model.parameters()     ,				lr=0.00_01   )
         return model, optimizer




     @slow
     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         a__		=			DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,				beta_start=0.00_01     ,				beta_end=0.02     ,				beta_schedule='linear'     ,				clip_sample=__SCREAMING_SNAKE_CASE     ,				)
         a__		=			DDIMScheduler(
             num_train_timesteps=1_0_0_0     ,				beta_start=0.00_01     ,				beta_end=0.02     ,				beta_schedule='linear'     ,				clip_sample=__SCREAMING_SNAKE_CASE     ,				)

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

         # shared batches for DDPM and DDIM
         set_seed(0   )
         a__		=			[torch.randn((4, 3, 3_2, 3_2)   ).clip(-1     ,				1   ).to(__SCREAMING_SNAKE_CASE   ) for _ in range(4   )]
         a__		=			[torch.randn((4, 3, 3_2, 3_2)   ).to(__SCREAMING_SNAKE_CASE   ) for _ in range(4   )]
         a__		=			[torch.randint(0     ,				1_0_0_0     ,				(4,)   ).long().to(__SCREAMING_SNAKE_CASE   ) for _ in range(4   )]

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

         # recreate the model and optimizer, and retry with DDIM
         a__	,     a__		=			self.get_model_optimizer(resolution=3_2   )
         model.train().to(__SCREAMING_SNAKE_CASE   )
         for i in range(4   ):
             optimizer.zero_grad()
             a__		=			ddim_scheduler.add_noise(clean_images[i]     ,				noise[i]     ,				timesteps[i]   )
             a__		=			model(__SCREAMING_SNAKE_CASE     ,				timesteps[i]   ).sample
             a__		=			torch.nn.functional.mse_loss(__SCREAMING_SNAKE_CASE     ,				noise[i]   )
             loss.backward()
             optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				atol=1e-5   )   )
         self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				atol=1e-5   )   )




"""simple docstring"""




from math import ceil



def     __magic_name__		(					UpperCamelCase     :		int = 1001     )     ->						int:
    a__		=			1

    for i in range(1							,		int(ceil(n / 2.0     )     )     ):
        a__		=			2 * i + 1
        a__		=			2 * i
        a__		=			total + 4 * odd**2 - 6 * even

    return total


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution())
    else:
        try:
            a		:       Any     						=       int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number')


