'''simple docstring'''






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class    a (       snake_case__				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    ):

    '''simple docstring'''







    super().__init__()
    __UpperCAmelCase:					int		= value_function
    __UpperCAmelCase:					Tuple		= unet
    __UpperCAmelCase:					Union[str, Any]		= scheduler
    __UpperCAmelCase:					List[str]		= env
    __UpperCAmelCase:					Optional[int]		= env.get_dataset()
    __UpperCAmelCase:					List[Any]		= {}
    for key in self.data.keys():
      try:
        __UpperCAmelCase:					Tuple		= self.data[key].mean()
      except:  # noqa: E722
        pass
    __UpperCAmelCase:					str		= {}
    for key in self.data.keys():
      try:
        __UpperCAmelCase:					str		= self.data[key].std()
      except:  # noqa: E722
        pass
    __UpperCAmelCase:					Union[str, Any]		= env.observation_space.shape[0]
    __UpperCAmelCase:					Union[str, Any]		= env.action_space.shape[0]







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    return (x_in - self.means[key]) / self.stds[key]







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    return x_in * self.stds[key] + self.means[key]







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if type(UpperCAmelCase_					) is dict:
      return {k: self.to_torch(UpperCAmelCase_					) for k, v in x_in.items()}
    elif torch.is_tensor(UpperCAmelCase_					):
      return x_in.to(self.unet.device					)
    return torch.tensor(UpperCAmelCase_   ,    device=self.unet.device					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    for key, val in cond.items():
      __UpperCAmelCase:					Dict		= val.clone()
    return x_in







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= x.shape[0]
    __UpperCAmelCase:					str		= None
    for i in tqdm.tqdm(self.scheduler.timesteps					):
      # create batch of timesteps to pass into model
      __UpperCAmelCase:					int		= torch.full((batch_size,)   ,    UpperCAmelCase_   ,    device=self.unet.device   ,    dtype=torch.long					)
      for _ in range(UpperCAmelCase_					):
        with torch.enable_grad():
          x.requires_grad_()

          # permute to match dimension for pre-trained models
          __UpperCAmelCase:					Any		= self.value_function(x.permute(0   ,    2   ,    1					)   ,    UpperCAmelCase_					).sample
          __UpperCAmelCase:					Any		= torch.autograd.grad([y.sum()]   ,    [x]					)[0]

          __UpperCAmelCase:					Tuple		= self.scheduler._get_variance(UpperCAmelCase_					)
          __UpperCAmelCase:					List[Any]		= torch.exp(0.5 * posterior_variance					)
          __UpperCAmelCase:					str		= model_std * grad

        __UpperCAmelCase:					Optional[Any]		= 0
        __UpperCAmelCase:					Optional[int]		= x.detach()
        __UpperCAmelCase:					List[Any]		= x + scale * grad
        __UpperCAmelCase:					Dict		= self.reset_xa(UpperCAmelCase_   ,    UpperCAmelCase_   ,    self.action_dim					)

      __UpperCAmelCase:					List[str]		= self.unet(x.permute(0   ,    2   ,    1					)   ,    UpperCAmelCase_					).sample.permute(0   ,    2   ,    1					)

      # TODO: verify deprecation of this kwarg
      __UpperCAmelCase:					Tuple		= self.scheduler.step(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    predict_epsilon=UpperCAmelCase_					)["""prev_sample"""]

      # apply conditions to the trajectory (set the initial state)
      __UpperCAmelCase:					Optional[int]		= self.reset_xa(UpperCAmelCase_   ,    UpperCAmelCase_   ,    self.action_dim					)
      __UpperCAmelCase:					Union[str, Any]		= self.to_torch(UpperCAmelCase_					)
    return x, y







  def __call__(							self   ,    snake_case_   ,    snake_case_=64   ,    snake_case_=32   ,    snake_case_=2   ,    snake_case_=0.1					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= self.normalize(UpperCAmelCase_   ,    """observations"""					)
    __UpperCAmelCase:					Union[str, Any]		= obs[None].repeat(UpperCAmelCase_   ,    axis=0					)

    __UpperCAmelCase:					Optional[int]		= {0: self.to_torch(UpperCAmelCase_					)}
    __UpperCAmelCase:					Union[str, Any]		= (batch_size, planning_horizon, self.state_dim + self.action_dim)

    # generate initial noise and apply our conditions (to make the trajectories start at current state)
    __UpperCAmelCase:					int		= randn_tensor(UpperCAmelCase_   ,    device=self.unet.device					)
    __UpperCAmelCase:					Dict		= self.reset_xa(UpperCAmelCase_   ,    UpperCAmelCase_   ,    self.action_dim					)
    __UpperCAmelCase:					Tuple		= self.to_torch(UpperCAmelCase_					)

    # run the diffusion process
    __UpperCAmelCase, __UpperCAmelCase:					Optional[int]		= self.run_diffusion(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_					)

    # sort output trajectories by value
    __UpperCAmelCase:					Any		= y.argsort(0   ,    descending=UpperCAmelCase_					).squeeze()
    __UpperCAmelCase:					Any		= x[sorted_idx]
    __UpperCAmelCase:					List[Any]		= sorted_values[:, :, : self.action_dim]
    __UpperCAmelCase:					Union[str, Any]		= actions.detach().cpu().numpy()
    __UpperCAmelCase:					str		= self.de_normalize(UpperCAmelCase_   ,    key="""actions"""					)

    # select the action with the highest value
    if y is not None:
      __UpperCAmelCase:					Tuple		= 0
    else:
      # if we didn't run value guiding, select a random action
      __UpperCAmelCase:					str		= np.random.randint(0   ,    UpperCAmelCase_					)

    __UpperCAmelCase:					str		= denorm_actions[selected_index, 0]
    return denorm_actions
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= []
  __UpperCAmelCase:					List[Any]		= []
  __UpperCAmelCase:					List[Any]		= {
      """^""": 3,
      """*""": 2,
      """/""": 2,
      """%""": 2,
      """+""": 1,
      """-""": 1,
  }  # Priority of each operator
  __UpperCAmelCase:					int		= len(_lowercase     ) if (len(_lowercase     ) > 7) else 7

  # Print table header for output
  print(
      """Symbol""".center(8     )   ,    """Stack""".center(_lowercase     )   ,    """Postfix""".center(_lowercase     )   ,    sep=""" | """   ,    )
  print("""-""" * (print_width * 3 + 7)     )

  for x in infix:
    if x.isalpha() or x.isdigit():
      post_fix.append(_lowercase     )  # if x is Alphabet / Digit, add it to Postfix
    elif x == "(":
      stack.append(_lowercase     )  # if x is "(" push to Stack
    elif x == ")":  # if x is ")" pop stack until "(" is encountered
      while stack[-1] != "(":
        post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
      stack.pop()
    else:
      if len(_lowercase     ) == 0:
        stack.append(_lowercase     )  # If stack is empty, push x to stack
      else:  # while priority of x is not > priority of element in the stack
        while len(_lowercase     ) > 0 and priority[x] <= priority[stack[-1]]:
          post_fix.append(stack.pop()     )  # pop stack & add to Postfix
        stack.append(_lowercase     )  # push x to stack

    print(
        x.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  while len(_lowercase     ) > 0:  # while stack is not empty
    post_fix.append(stack.pop()     )  # pop stack & add to Postfix
    print(
        """ """.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  return "".join(_lowercase     )  # return Postfix as str






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Tuple:
  __UpperCAmelCase:					Union[str, Any]		= list(infix[::-1]     )  # reverse the infix equation

  for i in range(len(_lowercase     )     ):
    if infix[i] == "(":
      __UpperCAmelCase:					Tuple		= """)"""  # change "(" to ")"
    elif infix[i] == ")":
      __UpperCAmelCase:					Union[str, Any]		= """("""  # change ")" to "("

  return (infix_2_postfix("""""".join(_lowercase     )     ))[
      ::-1
  ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('\nEnter an Infix Equation = ')  # Input an Infix equation
    SCREAMING_SNAKE_CASE_	   =			''.join(Infix.split())  # Remove spaces from the input
    print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')