

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class      lowerCamelCase_      :
	_lowercase		:   int
	_lowercase		:   int






class      lowerCamelCase_      :




	def __init__(     self						:     Tuple					,      __A						:     int							):
		__A :			list[list[Edge]]		    =			[[] for _ in range(__A							)]
		__A :			Optional[int]		    =			size




	def __getitem__(     self						:     Optional[Any]					,      __A						:     int							):
		return iter(self._graph[vertex]							)




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return self._size




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     int					,      __A						:     int					,      __A						:     int							):
		if weight not in (0, 1):
			raise ValueError("""Edge weight must be either 0 or 1."""							)

		if to_vertex < 0 or to_vertex >= self.size:
			raise ValueError("""Vertex indexes must be in [0; size)."""							)

		self._graph[from_vertex].append(Edge(__A					,      __A							)							)




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     int					,      __A						:     int							):
		__A :			Any		    =			deque([start_vertex]							)
		__A :			list[int | None]		    =			[None] * self.size
		__A :			Any		    =			0

		while queue:
			__A :			Dict		    =			queue.popleft()
			__A :			int		    =			distances[current_vertex]
			if current_distance is None:
				continue

			for edge in self[current_vertex]:
				__A :			Union[str, Any]		    =			current_distance + edge.weight
				__A :			int		    =			distances[edge.destination_vertex]
				if (
				    isinstance(__A					,      __A							)
				    and new_distance >= dest_vertex_distance
				):
					continue
				__A :			str		    =			new_distance
				if edge.weight == 0:
					queue.appendleft(edge.destination_vertex							)
				else:
					queue.append(edge.destination_vertex							)

		if distances[finish_vertex] is None:
			raise ValueError("""No path from start_vertex to finish_vertex."""							)

		return distances[finish_vertex]


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


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  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)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

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

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[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))






