




'''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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict[str, torch.Tensor]				):




     """simple docstring"""


     a_     :							Dict     						=  []
     a_     :							Tuple     						=  []
     a_     :							Optional[Any]     						=  []

     for rt in rc.restypes:
          a_     :							Dict     						=  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]				)
          a_     :							Optional[int]     						=  {name: i for i, name in enumerate(UpperCamelCase__				)}
          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] * 14				)
     restype_atomaa_to_atomaa_list.append([0] * 37				)
     restype_atomaa_mask_list.append([0.0] * 14				)

     a_     :							Optional[int]     						=  torch.tensor(
         UpperCamelCase__ ,    dtype=torch.intaa ,    device=protein["""aatype"""].device ,    )
     a_     :							Dict     						=  torch.tensor(
         UpperCamelCase__ ,    dtype=torch.intaa ,    device=protein["""aatype"""].device ,    )
     a_     :							Optional[int]     						=  torch.tensor(
         UpperCamelCase__ ,    dtype=torch.floataa ,    device=protein["""aatype"""].device ,    )
     a_     :							Optional[Any]     						=  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
     a_     :							Dict     						=  restype_atomaa_to_atomaa[protein_aatype]
     a_     :							Optional[Any]     						=  restype_atomaa_mask[protein_aatype]

     a_     :							Dict     						=  residx_atomaa_mask
     a_     :							str     						=  residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     a_     :							Tuple     						=  restype_atomaa_to_atomaa[protein_aatype]
     a_     :							Tuple     						=  residx_atomaa_to_atomaa.long()

     # create the corresponding mask
     a_     :							Dict     						=  torch.zeros([21, 37] ,    dtype=torch.floataa ,    device=protein["""aatype"""].device				)
     for restype, restype_letter in enumerate(rc.restypes				):
          a_     :							Optional[int]     						=  rc.restype_atoa[restype_letter]
          a_     :							Any     						=  rc.residue_atoms[restype_name]
          for atom_name in atom_names:
               a_     :							Dict     						=  rc.atom_order[atom_name]
               a_     :							Tuple     						=  1

     a_     :							List[str]     						=  restype_atomaa_mask[protein_aatype]
     a_     :							Optional[Any]     						=  residx_atomaa_mask

     return protein



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict[str, torch.Tensor]				):




     """simple docstring"""


     a_     :							str     						=  tree_map(lambda UpperCamelCase__				: torch.tensor(UpperCamelCase__ ,    device=batch["""aatype"""].device				) ,    UpperCamelCase__ ,    np.ndarray				)
     a_     :							Any     						=  tensor_tree_map(lambda UpperCamelCase__				: np.array(UpperCamelCase__				) ,    make_atomaa_masks(UpperCamelCase__				)				)
     return out






'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
