



from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase								=   TypeVar("""T""")



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 pass







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 pass







class 						_snake_case (     Generic[T]			):





 """simple docstring"""



 def __init__(  self		,      a = 1_0   )		->							int:




   """simple docstring"""







   _A          =      []
   _A          =      limit
 def __bool__(  self   )		->							bool:




   """simple docstring"""







   return bool(self.stack   )
 def __str__(  self   )		->							str:




   """simple docstring"""







   return str(self.stack   )
 def lowercase_						(  self		,      a   )		->							None:




   """simple docstring"""







   if len(self.stack   ) >= self.limit:
     raise StackOverflowError
   self.stack.append(a   )
 def lowercase_						(  self   )		->							T:




   """simple docstring"""







   if not self.stack:
     raise StackUnderflowError
   return self.stack.pop()
 def lowercase_						(  self   )		->							T:




   """simple docstring"""







   if not self.stack:
     raise StackUnderflowError
   return self.stack[-1]
 def lowercase_						(  self   )		->							bool:




   """simple docstring"""







   return not bool(self.stack   )
 def lowercase_						(  self   )		->							bool:




   """simple docstring"""







   return self.size() == self.limit
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   return len(self.stack   )



 def __contains__(  self		,      a   )		->							bool:




   """simple docstring"""







   return item in self.stack







def       UpperCAmelCase__	(							)				->	None:
  _A          =      Stack(10							)
  assert bool(__snake_case							) is False
  assert stack.is_empty() is True
  assert stack.is_full() is False
  assert str(__snake_case							) == "[]"

  try:
    _A          =      stack.pop()
    raise AssertionError  # This should not happen
  except StackUnderflowError:
    assert True  # This should happen

  try:
    _A          =      stack.peek()
    raise AssertionError  # This should not happen
  except StackUnderflowError:
    assert True  # This should happen

  for i in range(10							):
    assert stack.size() == i
    stack.push(__snake_case							)

  assert bool(__snake_case							)
  assert not stack.is_empty()
  assert stack.is_full()
  assert str(__snake_case							) == str(list(range(10							)							)							)
  assert stack.pop() == 9
  assert stack.peek() == 8

  stack.push(100							)
  assert str(__snake_case							) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]							)

  try:
    stack.push(200							)
    raise AssertionError  # This should not happen
  except StackOverflowError:
    assert True  # This should happen

  assert not stack.is_empty()
  assert stack.size() == 10

  assert 5 in stack
  assert 55 not in stack


if __name__ == "__main__":
    test_stack()




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """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   )		->							List[str]:




   """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   )		->							Optional[Any]:




   """simple docstring"""







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




   """simple docstring"""







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




   """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.cpu()
   _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() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """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() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



 def lowercase_						(  self   )		->							Optional[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() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3