





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      (UnCLIPScheduler,)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			**UpperCAmelCase_    :       Any)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ={
       "num_train_timesteps": 1_000,
       "variance_type": "fixed_small_log",
       "clip_sample": True,
       "clip_sample_range": 1.0,
       "prediction_type": "epsilon",
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[Any]:



   '''simple docstring'''



   for variance in ["fixed_small_log", "learned_range"]:
     self.check_over_configs(variance_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Any:



   '''simple docstring'''



   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->List[Any]:



   '''simple docstring'''



   for clip_sample_range in [1, 5, 10, 20]:
     self.check_over_configs(clip_sample_range=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->str:



   '''simple docstring'''



   for prediction_type in ["epsilon", "sample"]:
     self.check_over_configs(prediction_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   for time_step in [0, 500, 999]:
     for prev_timestep in [None, 5, 100, 250, 500, 750]:
       if prev_timestep is not None and prev_timestep >= time_step:
         continue

       self.check_over_forward(time_step=UpperCAmelCase_	,			prev_timestep=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.scheduler_classes[0]
   lowerCamelCase__:				Optional[Any]        =self.get_scheduler_config(variance_type="fixed_small_log")
   lowerCamelCase__:				Optional[int]        =scheduler_class(**UpperCAmelCase_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_0_0_0E-1_0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.054_9625)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.999_4987)) < 1E-5

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.scheduler_classes[0]
   lowerCamelCase__:				Dict        =self.get_scheduler_config(variance_type="learned_range")
   lowerCamelCase__:				List[str]        =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:				Dict        =0.5

   assert scheduler._get_variance(1	,			predicted_variance=UpperCAmelCase_) - -10.171_2790 < 1E-5
   assert scheduler._get_variance(487	,			predicted_variance=UpperCAmelCase_) - -5.799_8052 < 1E-5
   assert scheduler._get_variance(999	,			predicted_variance=UpperCAmelCase_) - -0.001_0011 < 1E-5

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =self.scheduler_classes[0]
   lowerCamelCase__:				List[str]        =self.get_scheduler_config()
   lowerCamelCase__:				Dict        =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =scheduler.timesteps

   lowerCamelCase__:				Dict        =self.dummy_model()
   lowerCamelCase__:				List[str]        =self.dummy_sample_deter
   lowerCamelCase__:				Any        =torch.manual_seed(0)

   for i, t in enumerate(UpperCAmelCase_):
     # 1. predict noise residual
     lowerCamelCase__:				Dict        =model(UpperCAmelCase_	,			UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:				Union[str, Any]        =scheduler.step(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:				int        =pred_prev_sample

   lowerCamelCase__:				Dict        =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:				List[Any]        =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 252.268_2495) < 1E-2
   assert abs(result_mean.item() - 0.328_4743) < 1E-3

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.scheduler_classes[0]
   lowerCamelCase__:				str        =self.get_scheduler_config()
   lowerCamelCase__:				Dict        =scheduler_class(**UpperCAmelCase_)

   scheduler.set_timesteps(25)

   lowerCamelCase__:				Tuple        =scheduler.timesteps

   lowerCamelCase__:				Tuple        =self.dummy_model()
   lowerCamelCase__:				Dict        =self.dummy_sample_deter
   lowerCamelCase__:				Tuple        =torch.manual_seed(0)

   for i, t in enumerate(UpperCAmelCase_):
     # 1. predict noise residual
     lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			UpperCAmelCase_)

     if i + 1 == timesteps.shape[0]:
       lowerCamelCase__:				Optional[Any]        =None
     else:
       lowerCamelCase__:				Dict        =timesteps[i + 1]

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:				Any        =scheduler.step(
         UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			prev_timestep=UpperCAmelCase_	,			generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:				str        =pred_prev_sample

   lowerCamelCase__:				Optional[Any]        =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:				Any        =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 258.204_4983) < 1E-2
   assert abs(result_mean.item() - 0.336_2038) < 1E-3

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   pass







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   pass









from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
        lowerCamelCase__:				int        =distance[u] + w

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

     __A     =    [{} for _ in range(E)]

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

     __A     =    int(input("\nEnter shortest path source:").strip())
     __A     =    bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)

