



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 		_snake_case   (      _lowerCAmelCase					):





 """simple docstring"""



 lowerCamelCase_        =      (EulerDiscreteScheduler,)
 lowerCamelCase_        =      1_0
 def lowercase_		(    self						,     **a       )						->				Union[str, Any]:




   """simple docstring"""







   _A							     =     {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a       )
   return config
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=a       )
 def lowercase_		(    self       )						->				Tuple:




   """simple docstring"""







   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,     [0.0_002, 0.002, 0.02]       ):
     self.check_over_configs(beta_start=a						,     beta_end=a       )
 def lowercase_		(    self       )						->				List[Any]:




   """simple docstring"""







   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=a       )
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a       )
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   _A							     =     self.scheduler_classes[0]
   _A							     =     self.get_scheduler_config()
   _A							     =     scheduler_class(**a       )

   scheduler.set_timesteps(self.num_inference_steps       )

   _A							     =     torch.manual_seed(0       )

   _A							     =     self.dummy_model()
   _A							     =     self.dummy_sample_deter * scheduler.init_noise_sigma
   _A							     =     sample.to(a       )

   for i, t in enumerate(scheduler.timesteps       ):
     _A							     =     scheduler.scale_model_input(a						,     a       )

     _A							     =     model(a						,     a       )

     _A							     =     scheduler.step(a						,     a						,     a						,     generator=a       )
     _A							     =     output.prev_sample

   _A							     =     torch.sum(torch.abs(a       )       )
   _A							     =     torch.mean(torch.abs(a       )       )

   assert abs(result_sum.item() - 10.0_807       ) < 1e-2
   assert abs(result_mean.item() - 0.0_131       ) < 1e-3
 def lowercase_		(    self       )						->				Union[str, Any]:




   """simple docstring"""







   _A							     =     self.scheduler_classes[0]
   _A							     =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
   _A							     =     scheduler_class(**a       )

   scheduler.set_timesteps(self.num_inference_steps       )

   _A							     =     torch.manual_seed(0       )

   _A							     =     self.dummy_model()
   _A							     =     self.dummy_sample_deter * scheduler.init_noise_sigma
   _A							     =     sample.to(a       )

   for i, t in enumerate(scheduler.timesteps       ):
     _A							     =     scheduler.scale_model_input(a						,     a       )

     _A							     =     model(a						,     a       )

     _A							     =     scheduler.step(a						,     a						,     a						,     generator=a       )
     _A							     =     output.prev_sample

   _A							     =     torch.sum(torch.abs(a       )       )
   _A							     =     torch.mean(torch.abs(a       )       )

   assert abs(result_sum.item() - 0.0_002       ) < 1e-2
   assert abs(result_mean.item() - 2.2_676e-06       ) < 1e-3
 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







   _A							     =     self.scheduler_classes[0]
   _A							     =     self.get_scheduler_config()
   _A							     =     scheduler_class(**a       )

   scheduler.set_timesteps(self.num_inference_steps						,     device=a       )

   _A							     =     torch.manual_seed(0       )

   _A							     =     self.dummy_model()
   _A							     =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A							     =     sample.to(a       )

   for t in scheduler.timesteps:
     _A							     =     scheduler.scale_model_input(a						,     a       )

     _A							     =     model(a						,     a       )

     _A							     =     scheduler.step(a						,     a						,     a						,     generator=a       )
     _A							     =     output.prev_sample

   _A							     =     torch.sum(torch.abs(a       )       )
   _A							     =     torch.mean(torch.abs(a       )       )

   assert abs(result_sum.item() - 10.0_807       ) < 1e-2
   assert abs(result_mean.item() - 0.0_131       ) < 1e-3



 def lowercase_		(    self       )						->				str:




   """simple docstring"""







   _A							     =     self.scheduler_classes[0]
   _A							     =     self.get_scheduler_config()
   _A							     =     scheduler_class(**a						,     use_karras_sigmas=a       )

   scheduler.set_timesteps(self.num_inference_steps						,     device=a       )

   _A							     =     torch.manual_seed(0       )

   _A							     =     self.dummy_model()
   _A							     =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A							     =     sample.to(a       )

   for t in scheduler.timesteps:
     _A							     =     scheduler.scale_model_input(a						,     a       )

     _A							     =     model(a						,     a       )

     _A							     =     scheduler.step(a						,     a						,     a						,     generator=a       )
     _A							     =     output.prev_sample

   _A							     =     torch.sum(torch.abs(a       )       )
   _A							     =     torch.mean(torch.abs(a       )       )

   assert abs(result_sum.item() - 124.52_299_499_511_719       ) < 1e-2
   assert abs(result_mean.item() - 0.16_213_932_633_399_963       ) < 1e-3







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()