






'''simple docstring'''







from heapq import heappop, heappush

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> tuple[float | int, list[tuple[int, int]]]:
							lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= grid.shape
							lowerCamelCase__      :			str 						= [-1, 1, 0, 0]
							lowerCamelCase__      :			Optional[int] 						= [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= [(0, source)], set()
							lowerCamelCase__      :			Optional[Any] 						= np.full((rows, cols)       ,    np.inf   )
							lowerCamelCase__      :			List[str] 						= 0
							lowerCamelCase__      :			Optional[Any] 						= np.empty((rows, cols)       ,    dtype=UpperCamelCase   )
							lowerCamelCase__      :			str 						= None

							while queue:
														((lowerCamelCase__)						,	(lowerCamelCase__))      :			Any 						= heappop(UpperCamelCase   )
														if (x, y) in visited:
																					continue
														visited.add((x, y)   )

														if (x, y) == destination:
																					lowerCamelCase__      :			Tuple 						= []
																					while (x, y) != source:
																												path.append((x, y)   )
																												lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= predecessors[x, y]
																					path.append(UpperCamelCase   )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(UpperCamelCase   )   ):
																					lowerCamelCase__						,	lowerCamelCase__      :			int 						= x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												lowerCamelCase__      :			Union[str, Any] 						= grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(UpperCamelCase       ,    (dist + 1, (nx, ny))   )
																																			lowerCamelCase__      :			Dict 						= dist + 1
																																			lowerCamelCase__      :			List[Any] 						= (x, y)

							return np.inf, []


if __name__ == "__main__":
			import doctest

			doctest.testmod()









'''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			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




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




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

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

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

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

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

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

