


'''simple docstring'''





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				(HeunDiscreteScheduler,)
 UpperCAmelCase				=				10


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int] ,					**__lowerCamelCase       :  int   ):
    UpperCAmelCase__  :Any            =			{
        '''num_train_timesteps''': 1_1_0_0,
        '''beta_start''': 0.00_01,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**__lowerCamelCase   )
    return config


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,					[0.00_02, 0.0_02, 0.02]   ):
       self.check_over_configs(beta_start=__lowerCamelCase ,					beta_end=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    for schedule in ["linear", "scaled_linear", "exp"]:
       self.check_over_configs(beta_schedule=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    for clip_sample_range in [1.0, 2.0, 3.0]:
       self.check_over_configs(clip_sample_range=__lowerCamelCase ,					clip_sample=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    for prediction_type in ["epsilon", "v_prediction", "sample"]:
       self.check_over_configs(prediction_type=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :Optional[Any]            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Dict            =			self.get_scheduler_config()
    UpperCAmelCase__  :Optional[int]            =			scheduler_class(**__lowerCamelCase   )

    scheduler.set_timesteps(self.num_inference_steps   )

    UpperCAmelCase__  :List[Any]            =			self.dummy_model()
    UpperCAmelCase__  :Tuple            =			self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCAmelCase__  :Tuple            =			sample.to(__lowerCamelCase   )

    for i, t in enumerate(scheduler.timesteps   ):
       UpperCAmelCase__  :Union[str, Any]            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Union[str, Any]            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Optional[Any]            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Optional[Any]            =			output.prev_sample

    UpperCAmelCase__  :List[str]            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :List[str]            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 0.12_33   ) < 1e-2
       assert abs(result_mean.item() - 0.00_02   ) < 1e-3
    else:
       # CUDA
       assert abs(result_sum.item() - 0.12_33   ) < 1e-2
       assert abs(result_mean.item() - 0.00_02   ) < 1e-3


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Tuple            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Tuple            =			self.get_scheduler_config(prediction_type='''v_prediction'''   )
    UpperCAmelCase__  :List[Any]            =			scheduler_class(**__lowerCamelCase   )

    scheduler.set_timesteps(self.num_inference_steps   )

    UpperCAmelCase__  :Any            =			self.dummy_model()
    UpperCAmelCase__  :List[Any]            =			self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCAmelCase__  :str            =			sample.to(__lowerCamelCase   )

    for i, t in enumerate(scheduler.timesteps   ):
       UpperCAmelCase__  :List[Any]            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :List[str]            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :str            =			output.prev_sample

    UpperCAmelCase__  :List[str]            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :str            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 4.69_34e-07   ) < 1e-2
       assert abs(result_mean.item() - 6.11_12e-10   ) < 1e-3
    else:
       # CUDA
       assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07   ) < 1e-2
       assert abs(result_mean.item() - 0.00_02   ) < 1e-3


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    UpperCAmelCase__  :Dict            =			self.scheduler_classes[0]
    UpperCAmelCase__  :Tuple            =			self.get_scheduler_config()
    UpperCAmelCase__  :List[Any]            =			scheduler_class(**__lowerCamelCase   )

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

    UpperCAmelCase__  :List[str]            =			self.dummy_model()
    UpperCAmelCase__  :Optional[int]            =			self.dummy_sample_deter.to(__lowerCamelCase   ) * scheduler.init_noise_sigma

    for t in scheduler.timesteps:
       UpperCAmelCase__  :Tuple            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Optional[Any]            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Optional[Any]            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Dict            =			output.prev_sample

    UpperCAmelCase__  :List[str]            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :Tuple            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    if str(__lowerCamelCase   ).startswith('''cpu'''   ):
       # The following sum varies between 148 and 156 on mps. Why?
       assert abs(result_sum.item() - 0.12_33   ) < 1e-2
       assert abs(result_mean.item() - 0.00_02   ) < 1e-3
    elif str(__lowerCamelCase   ).startswith('''mps'''   ):
       # Larger tolerance on mps
       assert abs(result_mean.item() - 0.00_02   ) < 1e-2
    else:
       # CUDA
       assert abs(result_sum.item() - 0.12_33   ) < 1e-2
       assert abs(result_mean.item() - 0.00_02   ) < 1e-3





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :Any            =			self.scheduler_classes[0]
    UpperCAmelCase__  :int            =			self.get_scheduler_config()
    UpperCAmelCase__  :List[str]            =			scheduler_class(**__lowerCamelCase ,					use_karras_sigmas=__lowerCamelCase   )

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

    UpperCAmelCase__  :List[str]            =			self.dummy_model()
    UpperCAmelCase__  :Any            =			self.dummy_sample_deter.to(__lowerCamelCase   ) * scheduler.init_noise_sigma
    UpperCAmelCase__  :List[Any]            =			sample.to(__lowerCamelCase   )

    for t in scheduler.timesteps:
       UpperCAmelCase__  :int            =			scheduler.scale_model_input(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :Tuple            =			model(__lowerCamelCase ,					__lowerCamelCase   )

       UpperCAmelCase__  :str            =			scheduler.step(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Any            =			output.prev_sample

    UpperCAmelCase__  :Dict            =			torch.sum(torch.abs(__lowerCamelCase   )   )
    UpperCAmelCase__  :str            =			torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 0.0_00_15   ) < 1e-2
    assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07   ) < 1e-2







'''simple docstring'''





import heapq


def 					a__    (				UpperCamelCase_	:			Dict,      UpperCamelCase_	:			Tuple,      UpperCamelCase_	:			Dict	):
   UpperCAmelCase__  :List[Any]            =			[(0, start)]  # cost from start node,end node
   UpperCAmelCase__  :Optional[int]            =			set()
   while heap:
      ((UpperCAmelCase__)      ,	(UpperCAmelCase__))  :Optional[Any]            =			heapq.heappop(UpperCamelCase_	)
      if u in visited:
         continue
      visited.add(UpperCamelCase_	)
      if u == end:
         return cost
      for v, c in graph[u]:
         if v in visited:
            continue
         UpperCAmelCase__  :int            =			cost + c
         heapq.heappush(UpperCamelCase_,      (next_item, v)	)
   return -1


__lowerCamelCase       				=	{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase       				=	{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase       				=	{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase       				=	dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase       				=	dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase       				=	dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
    import doctest

    doctest.testmod()



