






"""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__			:							int					   =   False







class   lowercase__     (				unittest.TestCase  ):


			def       UpperCAmelCase__		( self	:    Dict  ,			snake_case__	:    Union[str, Any]=32   ):
										set_seed(0   )
										lowerCamelCase_			:				int										=UNetaDModel(sample_size=lowercase_  ,			in_channels=3  ,			out_channels=3   )
										lowerCamelCase_			:				Dict										=torch.optim.SGD(model.parameters()  ,			lr=0.0_001   )
										return model, optimizer


			@slow
			def       UpperCAmelCase__		( self	:    Tuple   ):
										lowerCamelCase_			:				Union[str, Any]										="cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
										lowerCamelCase_			:				Optional[int]										=DDPMScheduler(
										    num_train_timesteps=1000  ,			beta_start=0.0_001  ,			beta_end=0.02  ,			beta_schedule="linear"  ,			clip_sample=lowercase_  ,			)
										lowerCamelCase_			:				Dict										=DDIMScheduler(
										    num_train_timesteps=1000  ,			beta_start=0.0_001  ,			beta_end=0.02  ,			beta_schedule="linear"  ,			clip_sample=lowercase_  ,			)

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

										# shared batches for DDPM and DDIM
										set_seed(0   )
										lowerCamelCase_			:				List[str]										=[torch.randn((4, 3, 32, 32)   ).clip(-1  ,			1   ).to(lowercase_   ) for _ in range(4   )]
										lowerCamelCase_			:				str										=[torch.randn((4, 3, 32, 32)   ).to(lowercase_   ) for _ in range(4   )]
										lowerCamelCase_			:				Optional[Any]										=[torch.randint(0  ,			1000  ,			(4,)   ).long().to(lowercase_   ) for _ in range(4   )]

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

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

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





"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       str			)			->   bool:






    """simple docstring"""




    UpperCAmelCase          =       [int(lowerCAmelCase			) for i in ip_va_address.split('.'			) if i.isdigit()]
    return len(lowerCAmelCase			) == 4 and all(0 <= int(lowerCAmelCase			) <= 254 for octet in octets			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    input().strip()
   SCREAMING_SNAKE_CASE_            =    '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
   print(F'{ip} is a {valid_or_invalid} IP v4 address.')



