
"""simple docstring"""
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			(KDPMaDiscreteScheduler,)
     A__      =			10
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]         = {
           "num_train_timesteps": 1100,
           "beta_start": 0.0_001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**snake_case__				)
       return config
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """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=snake_case__  ,     beta_end=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




       """simple docstring"""


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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict         = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				List[Any]         = self.get_scheduler_config(prediction_type="v_prediction"				)
       _SCREAMING_SNAKE_CASE   :				Tuple         = scheduler_class(**snake_case__				)

       scheduler.set_timesteps(self.num_inference_steps				)

       _SCREAMING_SNAKE_CASE   :				str         = self.dummy_model()
       _SCREAMING_SNAKE_CASE   :				List[str]         = self.dummy_sample_deter * scheduler.init_noise_sigma
       _SCREAMING_SNAKE_CASE   :				Dict         = sample.to(snake_case__				)

       for i, t in enumerate(scheduler.timesteps				):
         _SCREAMING_SNAKE_CASE   :				List[str]         = scheduler.scale_model_input(snake_case__  ,     snake_case__				)

         _SCREAMING_SNAKE_CASE   :				List[Any]         = model(snake_case__  ,     snake_case__				)

         _SCREAMING_SNAKE_CASE   :				str         = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]         = output.prev_sample

       _SCREAMING_SNAKE_CASE   :				Tuple         = torch.sum(torch.abs(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]         = torch.mean(torch.abs(snake_case__				)				)

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6_934E-07				) < 1E-2
         assert abs(result_mean.item() - 6.1_112E-10				) < 1E-3
       else:
         # CUDA
         assert abs(result_sum.item() - 4.693_428_650_170_972E-07				) < 1E-2
         assert abs(result_mean.item() - 0.0_002				) < 1E-3
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       if torch_device == "mps":
         return
       _SCREAMING_SNAKE_CASE   :				str         = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				Dict         = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				Optional[Any]         = scheduler_class(**snake_case__				)

       scheduler.set_timesteps(self.num_inference_steps				)

       _SCREAMING_SNAKE_CASE   :				str         = self.dummy_model()
       _SCREAMING_SNAKE_CASE   :				str         = self.dummy_sample_deter * scheduler.init_noise_sigma
       _SCREAMING_SNAKE_CASE   :				Any         = sample.to(snake_case__				)

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

         _SCREAMING_SNAKE_CASE   :				List[str]         = model(snake_case__  ,     snake_case__				)

         _SCREAMING_SNAKE_CASE   :				Optional[int]         = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__				)
         _SCREAMING_SNAKE_CASE   :				int         = output.prev_sample

       _SCREAMING_SNAKE_CASE   :				str         = torch.sum(torch.abs(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Tuple         = torch.mean(torch.abs(snake_case__				)				)

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 20.4_125				) < 1E-2
         assert abs(result_mean.item() - 0.0_266				) < 1E-3
       else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125				) < 1E-2
         assert abs(result_mean.item() - 0.0_266				) < 1E-3




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       if torch_device == "mps":
         return
       _SCREAMING_SNAKE_CASE   :				int         = self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE   :				Tuple         = self.get_scheduler_config()
       _SCREAMING_SNAKE_CASE   :				Optional[int]         = scheduler_class(**snake_case__				)

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

       _SCREAMING_SNAKE_CASE   :				Any         = self.dummy_model()
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]         = self.dummy_sample_deter.to(snake_case__				) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE   :				List[Any]         = scheduler.scale_model_input(snake_case__  ,     snake_case__				)

         _SCREAMING_SNAKE_CASE   :				Tuple         = model(snake_case__  ,     snake_case__				)

         _SCREAMING_SNAKE_CASE   :				List[str]         = scheduler.step(snake_case__  ,     snake_case__  ,     snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]         = output.prev_sample

       _SCREAMING_SNAKE_CASE   :				str         = torch.sum(torch.abs(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]         = torch.mean(torch.abs(snake_case__				)				)

       if str(snake_case__				).startswith("cpu"				):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 20.4_125				) < 1E-2
         assert abs(result_mean.item() - 0.0_266				) < 1E-3
       else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125				) < 1E-2
         assert abs(result_mean.item() - 0.0_266				) < 1E-3





"""simple docstring"""
import operator as op





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  _SCREAMING_SNAKE_CASE   :				str							    = lambda lowerCamelCase__,						lowerCamelCase__				: int(x / y				)  # noqa: E731 integer division operation
  _SCREAMING_SNAKE_CASE   :				Any							    = {
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8				),						"Action".center(1_2				),						"Stack",						sep=" | "				)
  print("-" * (3_0 + len(lowerCamelCase__				))				)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowerCamelCase__				)  # append x to stack
      # output in tabular format
      print(x.rjust(8				),						("push(" + x + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)
    else:
      _SCREAMING_SNAKE_CASE   :				Dict							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      _SCREAMING_SNAKE_CASE   :				Any							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + a + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      stack.append(
          str(opr[x](int(lowerCamelCase__				),						int(lowerCamelCase__				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8				),						("push(" + a + x + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | ",						)

  return int(stack[0]				)


if __name__ == "__main__":
      lowercase_       :						int     					=	input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
      print('''\n\tResult = ''', solve(Postfix))



