code: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      Optional[Any]				=						{
      """+""": lambda x, y: x + y,
      """-""": lambda x, y: x - y,
      """*""": lambda x, y: x * y,
      """/""": lambda x, y: x / y,
  }
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int							)  ->	Optional[int]:
       return c.isdigit()
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]							)  ->	List[str]:
       __lowerCamelCase     :			Tuple              =	[]
  
       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(UpperCAmelCase_							):
                 stack.append(int(UpperCAmelCase_							)							)
  
            else:
                 # pop values from stack can calculate the result
                 # push the result onto the stack again
                 __lowerCamelCase     :			int              =	stack.pop()
                 __lowerCamelCase     :			Union[str, Any]              =	stack.pop()
                 stack.append(calc[c](UpperCAmelCase_   ,					UpperCAmelCase_							)							)
  
       return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
        A__	:      List[str]				=						"""+ 9 * 2 6"""
        print(evaluate(test_expression))
  
        A__	:      str				=						"""/ * 10 2 + 4 1 """
        print(evaluate(test_expression))
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
      lowerCamelCase						:      Tuple							  =    1_0
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Union[str, Any]              =	{
               'num_train_timesteps': 11_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }
  
           config.update(**SCREAMING_SNAKE_CASE_			)
           return config
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							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=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
           __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	output.prev_sample
  
           __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
           __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Tuple              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
           assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			str              =	self.get_scheduler_config()
           __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
           __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
  
           for t in scheduler.timesteps:
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3
  
  
  
  
  
  
  