





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowercase		:  Optional[Any]			       =False






class 						UpperCamelCase_  (  unittest.TestCase							):



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





					@slow
					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     Tuple 						=      'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							lowerCamelCase_	:     Optional[int] 						=      DDPMScheduler(
							    num_train_timesteps=10_00       ,      beta_start=0.0_001       ,      beta_end=0.02       ,      beta_schedule='linear'       ,      clip_sample=lowerCamelCase       ,      )
							lowerCamelCase_	:     Union[str, Any] 						=      DDIMScheduler(
							    num_train_timesteps=10_00       ,      beta_start=0.0_001       ,      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						)
							lowerCamelCase_	:     List[Any] 						=      [torch.randn((4, 3, 32, 32)						).clip(-1       ,      1						).to(lowerCamelCase						) for _ in range(4						)]
							lowerCamelCase_	:     Optional[int] 						=      [torch.randn((4, 3, 32, 32)						).to(lowerCamelCase						) for _ in range(4						)]
							lowerCamelCase_	:     List[str] 						=      [torch.randint(0       ,      10_00       ,      (4,)						).long().to(lowerCamelCase						) for _ in range(4						)]

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







_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


if __name__ == "__main__":
		print(F'''{solution() = }''')
