
"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict[str, torch.Tensor]				)  ->    Dict[str, torch.Tensor]:
  _SCREAMING_SNAKE_CASE   :				Any							    = []
  _SCREAMING_SNAKE_CASE   :				Dict							    = []
  _SCREAMING_SNAKE_CASE   :				List[str]							    = []

  for rt in rc.restypes:
    _SCREAMING_SNAKE_CASE   :				Any							    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
    restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {name: i for i, name in enumerate(lowerCamelCase__				)}
    restype_atomaa_to_atomaa_list.append(
        [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

    restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

  # Add dummy mapping for restype 'UNK'
  restype_atomaa_to_atomaa_list.append([0] * 1_4				)
  restype_atomaa_to_atomaa_list.append([0] * 3_7				)
  restype_atomaa_mask_list.append([0.0] * 1_4				)

  _SCREAMING_SNAKE_CASE   :				str							    = torch.tensor(
      lowerCamelCase__,						dtype=torch.intaa,						device=protein["aatype"].device,						)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = torch.tensor(
      lowerCamelCase__,						dtype=torch.intaa,						device=protein["aatype"].device,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = torch.tensor(
      lowerCamelCase__,						dtype=torch.floataa,						device=protein["aatype"].device,						)
  _SCREAMING_SNAKE_CASE   :				Dict							    = protein["aatype"].to(torch.long				)

  # create the mapping for (residx, atom14) --> atom37, i.e. an array
  # with shape (num_res, 14) containing the atom37 indices for this protein
  _SCREAMING_SNAKE_CASE   :				str							    = restype_atomaa_to_atomaa[protein_aatype]
  _SCREAMING_SNAKE_CASE   :				Tuple							    = restype_atomaa_mask[protein_aatype]

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = residx_atomaa_mask
  _SCREAMING_SNAKE_CASE   :				List[str]							    = residx_atomaa_to_atomaa.long()

  # create the gather indices for mapping back
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = restype_atomaa_to_atomaa[protein_aatype]
  _SCREAMING_SNAKE_CASE   :				Dict							    = residx_atomaa_to_atomaa.long()

  # create the corresponding mask
  _SCREAMING_SNAKE_CASE   :				int							    = torch.zeros([2_1, 3_7],						dtype=torch.floataa,						device=protein["aatype"].device				)
  for restype, restype_letter in enumerate(rc.restypes				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = rc.restype_atoa[restype_letter]
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = rc.residue_atoms[restype_name]
    for atom_name in atom_names:
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = rc.atom_order[atom_name]
      _SCREAMING_SNAKE_CASE   :				Any							    = 1

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = restype_atomaa_mask[protein_aatype]
  _SCREAMING_SNAKE_CASE   :				Dict							    = residx_atomaa_mask

  return protein





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict[str, torch.Tensor]				)  ->    Dict[str, np.ndarray]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = tree_map(lambda lowerCamelCase__				: torch.tensor(lowerCamelCase__,						device=batch["aatype"].device				),						lowerCamelCase__,						np.ndarray				)
  _SCREAMING_SNAKE_CASE   :				int							    = tensor_tree_map(lambda lowerCamelCase__				: np.array(lowerCamelCase__				),						make_atomaa_masks(lowerCamelCase__				)				)
  return out





"""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))



