

def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     if a < 0:
          raise ValueError("Input value must be a positive integer"				)
     elif isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          raise TypeError("Input value must be a 'int' type"				)
     return bin(SCREAMING_SNAKE_CASE				).count("1"				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()









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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					int        =    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]				)
          _lowerCAmelCase   :					str        =    {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE				)}
          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				)

     _lowerCAmelCase   :					List[str]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					str        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.floataa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					List[str]        =    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
     _lowerCAmelCase   :					Dict        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					Optional[Any]        =    restype_atomaa_mask[protein_aatype]

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     _lowerCAmelCase   :					Optional[int]        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					str        =    residx_atomaa_to_atomaa.long()

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

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    tree_map(lambda SCREAMING_SNAKE_CASE				: torch.tensor(SCREAMING_SNAKE_CASE		,							device=batch["aatype"].device				)		,							SCREAMING_SNAKE_CASE		,							np.ndarray				)
     _lowerCAmelCase   :					Optional[int]        =    tensor_tree_map(lambda SCREAMING_SNAKE_CASE				: np.array(SCREAMING_SNAKE_CASE				)		,							make_atomaa_masks(SCREAMING_SNAKE_CASE				)				)
     return out






