'''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


snake_case__					:      List[str]				      =  False






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self				,		snake_case_=3_2						):




       '''simple docstring'''

       set_seed(0						)
       UpperCAmelCase_							:       Union[str, Any]       =				UNetaDModel(sample_size=snake_case_				,		in_channels=3				,		out_channels=3						)
       UpperCAmelCase_							:       str       =				torch.optim.SGD(model.parameters()				,		lr=0.00_01						)
       return model, optimizer






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       UpperCAmelCase_							:       Dict       =				DDPMScheduler(
           num_train_timesteps=1_0_0_0				,		beta_start=0.00_01				,		beta_end=0.02				,		beta_schedule='linear'				,		clip_sample=snake_case_				,		)
       UpperCAmelCase_							:       Optional[Any]       =				DDIMScheduler(
           num_train_timesteps=1_0_0_0				,		beta_start=0.00_01				,		beta_end=0.02				,		beta_schedule='linear'				,		clip_sample=snake_case_				,		)

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

       # shared batches for DDPM and DDIM
       set_seed(0						)
       UpperCAmelCase_							:       Optional[int]       =				[torch.randn((4, 3, 3_2, 3_2)						).clip(-1				,		1						).to(snake_case_						) for _ in range(4						)]
       UpperCAmelCase_							:       Tuple       =				[torch.randn((4, 3, 3_2, 3_2)						).to(snake_case_						) for _ in range(4						)]
       UpperCAmelCase_							:       Tuple       =				[torch.randint(0				,		1_0_0_0				,		(4,)						).long().to(snake_case_						) for _ in range(4						)]

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

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

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







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return int(input_a == input_a == 0       )


def  _lowerCamelCase   (				):



     """simple docstring"""





     print('Truth Table of NOR Gate:'       )
     print('|   Input 1   |  Input 2  | Output |'       )
     print(F'''|      0      |    0      |  {nor_gate(0     ,					0       )}     |'''       )
     print(F'''|      0      |    1      |  {nor_gate(0     ,					1       )}     |'''       )
     print(F'''|      1      |    0      |  {nor_gate(1     ,					0       )}     |'''       )
     print(F'''|      1      |    1      |  {nor_gate(1     ,					1       )}     |'''       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






