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  :      Tuple            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self       ,						lowerCamelCase__=32 ):
       set_seed(0 )
       lowerCAmelCase_:							Optional[Any]       =	UNetaDModel(sample_size=lowerCamelCase__       ,						in_channels=3       ,						out_channels=3 )
       lowerCAmelCase_:							List[Any]       =	torch.optim.SGD(model.parameters()       ,						lr=0.0_0_0_1 )
       return model, optimizer




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       lowerCAmelCase_:							Optional[int]       =	DDPMScheduler(
           num_train_timesteps=1_000       ,						beta_start=0.0_0_0_1       ,						beta_end=0.0_2       ,						beta_schedule="linear"       ,						clip_sample=lowerCamelCase__       ,						)
       lowerCAmelCase_:							int       =	DDIMScheduler(
           num_train_timesteps=1_000       ,						beta_start=0.0_0_0_1       ,						beta_end=0.0_2       ,						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_:							str       =	[torch.randn((4, 3, 32, 32) ).clip(-1       ,						1 ).to(lowerCamelCase__ ) for _ in range(4 )]
       lowerCAmelCase_:							Union[str, Any]       =	[torch.randn((4, 3, 32, 32) ).to(lowerCamelCase__ ) for _ in range(4 )]
       lowerCAmelCase_:							Tuple       =	[torch.randint(0       ,						1_000       ,						(4,) ).long().to(lowerCamelCase__ ) for _ in range(4 )]

       # train with a DDPM scheduler
       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.get_model_optimizer(resolution=32 )
       model.train().to(lowerCamelCase__ )
       for i in range(4 ):
          optimizer.zero_grad()
          lowerCAmelCase_:							Any       =	ddpm_scheduler.add_noise(clean_images[i]       ,						noise[i]       ,						timesteps[i] )
          lowerCAmelCase_:							Any       =	model(lowerCamelCase__       ,						timesteps[i] ).sample
          lowerCAmelCase_:							Union[str, Any]       =	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_:							List[str]       =	self.get_model_optimizer(resolution=32 )
       model.train().to(lowerCamelCase__ )
       for i in range(4 ):
          optimizer.zero_grad()
          lowerCAmelCase_:							Tuple       =	ddim_scheduler.add_noise(clean_images[i]       ,						noise[i]       ,						timesteps[i] )
          lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__       ,						timesteps[i] ).sample
          lowerCAmelCase_:							Union[str, 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 ) )
from __future__ import annotations
def 		snake_case__	(		lowercase							,				lowercase					):
   print(F'''Vertex\tShortest Distance from vertex {src}'''					)
   for i, d in enumerate(lowercase					):
      print(F'''{i}\t\t{d}'''					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for j in range(lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(graph[j][k] for k in ["src", "dst", "weight"])
      if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
         return True
   return False
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	[float("inf"					)] * vertex_count
   lowerCAmelCase_:							Dict       =	0.0

   for _ in range(vertex_count - 1					):
      for j in range(lowercase					):
         lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	(graph[j][k] for k in ["src", "dst", "weight"])

         if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
            lowerCAmelCase_:							List[str]       =	distance[u] + w

   lowerCAmelCase_:							str       =	check_negative_cycle(lowercase							,				lowercase							,				lowercase					)
   if negative_cycle_exists:
      raise Exception("Negative cycle found"					)

   return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a  :      List[str]            =    int(input("""Enter number of vertices: """).strip())
 a  :      Any            =    int(input("""Enter number of edges: """).strip())

 a  :      list[dict[str, int]]            =    [{} for _ in range(E)]

 for i in range(E):
  print("""Edge """, i + 1)
  a     , a     , a  :      Optional[int]            =    (
      int(x)
      for x in input("""Enter source, destination, weight: """).strip().split(""" """)
  )
  a  :      Optional[int]            =    {"""src""": src, """dst""": dest, """weight""": weight}

 a  :      List[Any]            =    int(input("""\nEnter shortest path source:""").strip())
 a  :      Tuple            =    bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)