

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  (HeunDiscreteScheduler,)
   UpperCamelCase       =  1_0



   def  __magic_name__					(							self							:  Any,	**__A							:  Tuple		):
     UpperCAmelCase :							List[Any]								=				{
         '''num_train_timesteps''': 1_1_0_0,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
     }

     config.update(**__A		)
     return config



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



   def  __magic_name__					(							self							:  Dict		):
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
       self.check_over_configs(beta_start=__A,	beta_end=__A		)



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



   def  __magic_name__					(							self							:  str		):
     for clip_sample_range in [1.0, 2.0, 3.0]:
       self.check_over_configs(clip_sample_range=__A,	clip_sample=__A		)



   def  __magic_name__					(							self							:  Optional[int]		):
     for prediction_type in ["epsilon", "v_prediction", "sample"]:
       self.check_over_configs(prediction_type=__A		)



   def  __magic_name__					(							self							:  Any		):
     UpperCAmelCase :							Optional[Any]								=				self.scheduler_classes[0]
     UpperCAmelCase :							int								=				self.get_scheduler_config()
     UpperCAmelCase :							str								=				scheduler_class(**__A		)

     scheduler.set_timesteps(self.num_inference_steps		)

     UpperCAmelCase :							List[Any]								=				self.dummy_model()
     UpperCAmelCase :							Union[str, Any]								=				self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase :							Any								=				sample.to(__A		)

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							Dict								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							List[str]								=				model(__A,	__A		)

       UpperCAmelCase :							Optional[Any]								=				scheduler.step(__A,	__A,	__A		)
       UpperCAmelCase :							Union[str, Any]								=				output.prev_sample

     UpperCAmelCase :							str								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Tuple								=				torch.mean(torch.abs(__A		)		)

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 0.1_2_3_3		) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-3
     else:
       # CUDA
       assert abs(result_sum.item() - 0.1_2_3_3		) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-3



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Any								=				self.scheduler_classes[0]
     UpperCAmelCase :							Union[str, Any]								=				self.get_scheduler_config(prediction_type='''v_prediction'''		)
     UpperCAmelCase :							Any								=				scheduler_class(**__A		)

     scheduler.set_timesteps(self.num_inference_steps		)

     UpperCAmelCase :							Dict								=				self.dummy_model()
     UpperCAmelCase :							Tuple								=				self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase :							Optional[int]								=				sample.to(__A		)

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							Optional[Any]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							Optional[Any]								=				model(__A,	__A		)

       UpperCAmelCase :							Optional[Any]								=				scheduler.step(__A,	__A,	__A		)
       UpperCAmelCase :							List[str]								=				output.prev_sample

     UpperCAmelCase :							Union[str, Any]								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Any								=				torch.mean(torch.abs(__A		)		)

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 4.6934E-07		) < 1E-2
       assert abs(result_mean.item() - 6.1112E-10		) < 1E-3
     else:
       # CUDA
       assert abs(result_sum.item() - 4.693428650170972E-07		) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-3



   def  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							int								=				self.scheduler_classes[0]
     UpperCAmelCase :							List[str]								=				self.get_scheduler_config()
     UpperCAmelCase :							Optional[int]								=				scheduler_class(**__A		)

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

     UpperCAmelCase :							Any								=				self.dummy_model()
     UpperCAmelCase :							Tuple								=				self.dummy_sample_deter.to(__A		) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
       UpperCAmelCase :							Union[str, Any]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							Optional[int]								=				model(__A,	__A		)

       UpperCAmelCase :							int								=				scheduler.step(__A,	__A,	__A		)
       UpperCAmelCase :							Tuple								=				output.prev_sample

     UpperCAmelCase :							Optional[Any]								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Dict								=				torch.mean(torch.abs(__A		)		)

     if str(__A		).startswith('''cpu'''		):
       # The following sum varies between 148 and 156 on mps. Why?
       assert abs(result_sum.item() - 0.1_2_3_3		) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-3
     elif str(__A		).startswith('''mps'''		):
       # Larger tolerance on mps
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-2
     else:
       # CUDA
       assert abs(result_sum.item() - 0.1_2_3_3		) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2		) < 1E-3





   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase :							Union[str, Any]								=				self.scheduler_classes[0]
     UpperCAmelCase :							str								=				self.get_scheduler_config()
     UpperCAmelCase :							Optional[Any]								=				scheduler_class(**__A,	use_karras_sigmas=__A		)

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

     UpperCAmelCase :							Optional[int]								=				self.dummy_model()
     UpperCAmelCase :							Any								=				self.dummy_sample_deter.to(__A		) * scheduler.init_noise_sigma
     UpperCAmelCase :							Any								=				sample.to(__A		)

     for t in scheduler.timesteps:
       UpperCAmelCase :							Optional[int]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							Optional[int]								=				model(__A,	__A		)

       UpperCAmelCase :							Tuple								=				scheduler.step(__A,	__A,	__A		)
       UpperCAmelCase :							int								=				output.prev_sample

     UpperCAmelCase :							Union[str, Any]								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Dict								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_sum.item() - 0.0_0_0_1_5		) < 1E-2
     assert abs(result_mean.item() - 1.9869554535034695E-07		) < 1E-2








from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       :     Optional[int]           =							{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							Dict								=				{start}
  UpperCAmelCase :							Optional[int]								=				[start]
  UpperCAmelCase :							Queue								=				Queue()
  queue.put(UpperCAmelCase    )
  while not queue.empty():
    UpperCAmelCase :							List[Any]								=				queue.get()
    for w in graph[v]:
      if w not in explored:
        explored.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.put(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							str								=				{start}
  UpperCAmelCase :							List[str]								=				[start]
  UpperCAmelCase :							Union[str, Any]								=				deque([start]    )
  while queue:
    UpperCAmelCase :							Dict								=				queue.popleft()
    for child in graph[v]:
      if child not in visited:
        visited.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.append(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    str    )						->		None:
  UpperCAmelCase :							Optional[int]								=				f'''from __main__ import G, {name}'''
  UpperCAmelCase :							Tuple								=				10_000
  UpperCAmelCase :							Any								=				timeit(f'''{name}(G, \'A\')'''       ,				setup=UpperCAmelCase       ,				number=UpperCAmelCase    )
  print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     benchmark_function("breadth_first_search")
     benchmark_function("breadth_first_search_with_deque")
     # breadth_first_search                finished 10000 runs in 0.20999 seconds
     # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





