





'''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   : str             =			False







class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Tuple=32       ):





											'''simple docstring'''





											set_seed(0       )
											_snake_case:   int  					=		UNetaDModel(sample_size=__snake_case       ,     in_channels=3       ,     out_channels=3       )
											_snake_case:   List[Any]  					=		torch.optim.SGD(model.parameters()       ,     lr=0.0_001       )
											return model, optimizer




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





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

											# train with a DDPM scheduler
											_snake_case					,						_snake_case:   str  					=		self.get_model_optimizer(resolution=32       )
											model.train().to(__snake_case       )
											for i in range(4       ):
																	optimizer.zero_grad()
																	_snake_case:   List[Any]  					=		ddpm_scheduler.add_noise(clean_images[i]       ,     noise[i]       ,     timesteps[i]       )
																	_snake_case:   List[Any]  					=		model(__snake_case       ,     timesteps[i]       ).sample
																	_snake_case:   List[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
											_snake_case					,						_snake_case:   List[Any]  					=		self.get_model_optimizer(resolution=32       )
											model.train().to(__snake_case       )
											for i in range(4       ):
																	optimizer.zero_grad()
																	_snake_case:   Optional[Any]  					=		ddim_scheduler.add_noise(clean_images[i]       ,     noise[i]       ,     timesteps[i]       )
																	_snake_case:   str  					=		model(__snake_case       ,     timesteps[i]       ).sample
																	_snake_case:   int  					=		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       lowercase_      (		lowercase__ = 50						)   ->int:
						_snake_case:   Union[str, Any]  					=		[[0] * 3 for _ in range(length + 1						)]

						for row_length in range(length + 1						):
												for tile_length in range(2				,  5						):
																		for tile_start in range(row_length - tile_length + 1						):
																								different_colour_ways_number[row_length][tile_length - 2] += (
																								    different_colour_ways_number[row_length - tile_start - tile_length][
																								        tile_length - 2
																								    ]
																								    + 1
																								)

						return sum(different_colour_ways_number[length]						)


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





