



"""simple docstring"""







lowercase__						:							str        =      {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str		)		->  int:

  """simple docstring"""




  return c.isdigit()

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					[]

  # iterate over the string in reverse order
  for c in expression.split()[::-1]:
    # push operand to stack
    if is_operand(__A		):
      stack.append(int(__A		)		)

    else:
      # pop values from stack can calculate the result
      # push the result onto the stack again
      lowerCAmelCase_  :							List[Any]								=					stack.pop()
      lowerCAmelCase_  :							int								=					stack.pop()
      stack.append(calc[c](__A       ,  __A		)		)

  return stack.pop()


# Driver code
if __name__ == "__main__":
     lowercase__						:							str        =      "+ 9 * 2 6"
     print(evaluate(test_expression))

     lowercase__						:							Any        =      "/ * 10 2 + 4 1 "
     print(evaluate(test_expression))













"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											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=_SCREAMING_SNAKE_CASE     ,    beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											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(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											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(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3