
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		__magic_name__				( A_					):


  """simple docstring"""





  _UpperCamelCase						 = (EulerAncestralDiscreteScheduler,)
  _UpperCamelCase						 = 10


  def     _UpperCAmelCase  (					self	,		**a__		):
    _lowerCamelCase      =						{
        '''num_train_timesteps''': 11_00,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**a__		)
    return config


  def     _UpperCAmelCase  (					self		):


    for timesteps in [10, 50, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=a__		)


  def     _UpperCAmelCase  (					self		):


    for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,		[0.0002, 0.002, 0.02]		):
      self.check_over_configs(beta_start=a__	,		beta_end=a__		)


  def     _UpperCAmelCase  (					self		):


    for schedule in ["linear", "scaled_linear"]:
      self.check_over_configs(beta_schedule=a__		)


  def     _UpperCAmelCase  (					self		):


    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=a__		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config()
    _lowerCamelCase      =						scheduler_class(**a__		)

    scheduler.set_timesteps(self.num_inference_steps		)

    _lowerCamelCase      =						torch.manual_seed(0		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _lowerCamelCase      =						sample.to(a__		)

    for i, t in enumerate(scheduler.timesteps		):
      _lowerCamelCase      =						scheduler.scale_model_input(a__	,		a__		)

      _lowerCamelCase      =						model(a__	,		a__		)

      _lowerCamelCase      =						scheduler.step(a__	,		a__	,		a__	,		generator=a__		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(a__		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(a__		)		)

    assert abs(result_sum.item() - 1_52.31_92		) < 1E-2
    assert abs(result_mean.item() - 0.1983		) < 1E-3


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config(prediction_type='''v_prediction'''		)
    _lowerCamelCase      =						scheduler_class(**a__		)

    scheduler.set_timesteps(self.num_inference_steps		)

    _lowerCamelCase      =						torch.manual_seed(0		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma
    _lowerCamelCase      =						sample.to(a__		)

    for i, t in enumerate(scheduler.timesteps		):
      _lowerCamelCase      =						scheduler.scale_model_input(a__	,		a__		)

      _lowerCamelCase      =						model(a__	,		a__		)

      _lowerCamelCase      =						scheduler.step(a__	,		a__	,		a__	,		generator=a__		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(a__		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(a__		)		)

    assert abs(result_sum.item() - 1_08.44_39		) < 1E-2
    assert abs(result_mean.item() - 0.1412		) < 1E-3


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config()
    _lowerCamelCase      =						scheduler_class(**a__		)

    scheduler.set_timesteps(self.num_inference_steps	,		device=a__		)
    _lowerCamelCase      =						torch.manual_seed(0		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _lowerCamelCase      =						sample.to(a__		)

    for t in scheduler.timesteps:
      _lowerCamelCase      =						scheduler.scale_model_input(a__	,		a__		)

      _lowerCamelCase      =						model(a__	,		a__		)

      _lowerCamelCase      =						scheduler.step(a__	,		a__	,		a__	,		generator=a__		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(a__		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(a__		)		)

    assert abs(result_sum.item() - 1_52.31_92		) < 1E-2
    assert abs(result_mean.item() - 0.1983		) < 1E-3









def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									if (
									    (not vset[i])
									    and graph[u][i] != float("""inf"""	)
									    and mdist[u] + graph[u][i] < mdist[i]
									):
												a__					=  mdist[u] + graph[u][i]

			print_dist(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	_lowercase				:					Any     					=int(input("""Enter number of vertices: """).strip())
	_lowercase				:					List[str]     					=int(input("""Enter number of edges: """).strip())

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

	for i in range(E):
		print("""\nEdge """, i + 1)
		_lowercase				:					Dict     					=int(input("""Enter source:""").strip())
		_lowercase				:					Optional[Any]     					=int(input("""Enter destination:""").strip())
		_lowercase				:					List[Any]     					=float(input("""Enter weight:""").strip())
		_lowercase				:					List[str]     					=weight

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

