


from heapq import heappop, heappush

import numpy as np

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					tuple[int, int]			,     SCREAMING_SNAKE_CASE__			:					tuple[int, int]			,     SCREAMING_SNAKE_CASE__			:					bool			,     ):
     __UpperCamelCase							,      __UpperCamelCase    							=grid.shape
     __UpperCamelCase    							=[-1, 1, 0, 0]
     __UpperCamelCase    							=[0, 0, -1, 1]
     if allow_diagonal:
          dx += [-1, -1, 1, 1]
          dy += [-1, 1, -1, 1]

     __UpperCamelCase							,      __UpperCamelCase    							=[(0, source)], set()
     __UpperCamelCase    							=np.full((rows, cols)			,     np.inf				)
     __UpperCamelCase    							=0
     __UpperCamelCase    							=np.empty((rows, cols)			,     dtype=SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=None

     while queue:
          ((__UpperCamelCase)							,      (__UpperCamelCase))    							=heappop(SCREAMING_SNAKE_CASE__				)
          if (x, y) in visited:
               continue
          visited.add((x, y)				)

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

          for i in range(len(SCREAMING_SNAKE_CASE__				)				):
               __UpperCamelCase							,      __UpperCamelCase    							=x + dx[i], y + dy[i]
               if 0 <= nx < rows and 0 <= ny < cols:
                    __UpperCamelCase    							=grid[nx][ny]
                    if next_node == 1 and matrix[nx, ny] > dist + 1:
                         heappush(SCREAMING_SNAKE_CASE__			,     (dist + 1, (nx, ny))				)
                         __UpperCamelCase    							=dist + 1
                         __UpperCamelCase    							=(x, y)

     return np.inf, []


if __name__ == "__main__":
   import doctest

   doctest.testmod()







import unittest

import torch

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


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





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


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  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	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

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

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			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	)	)