




import operator as op

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Any:
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_      		=     lambda __UpperCAmelCase     ,     __UpperCAmelCase				: int(x / y				)  # noqa: E731 integer division operation
      SCREAMING_SNAKE_CASE_      		=     {
          '^': op.pow,
          '*': op.mul,
          '/': div,
          '+': op.add,
          '-': op.sub,
      }  # operators & their respective operation

      # print table header
      print('Symbol'.center(8				)     ,     'Action'.center(12				)     ,     'Stack'     ,     sep=' | '				)
      print('-' * (30 + len(__UpperCAmelCase				))				)

      for x in post_fix:
            if x.isdigit():  # if x in digit
                  stack.append(__UpperCAmelCase				)  # append x to stack
                  # output in tabular format
                  print(x.rjust(8				)     ,     ('push(' + x + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)
            else:
                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + a + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  stack.append(
                      str(opr[x](int(__UpperCAmelCase				)     ,     int(__UpperCAmelCase				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
                  # output in tabular format
                  print(
                      x.rjust(8				)     ,     ('push(' + a + x + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '     ,     )

      return int(stack[0]				)


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





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




