


"""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_      (				UpperCamelCase__	: Dict[str, torch.Tensor] ):






 """simple docstring"""







 __lowercase									=	[]
 __lowercase									=	[]
 __lowercase									=	[]

 for rt in rc.restypes:
  __lowercase									=	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] )
  __lowercase									=	{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 )

 __lowercase									=	torch.tensor(
     UpperCamelCase__				,   dtype=torch.intaa				,   device=protein["""aatype"""].device				,   )
 __lowercase									=	torch.tensor(
     UpperCamelCase__				,   dtype=torch.intaa				,   device=protein["""aatype"""].device				,   )
 __lowercase									=	torch.tensor(
     UpperCamelCase__				,   dtype=torch.floataa				,   device=protein["""aatype"""].device				,   )
 __lowercase									=	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
 __lowercase									=	restype_atomaa_to_atomaa[protein_aatype]
 __lowercase									=	restype_atomaa_mask[protein_aatype]

 __lowercase									=	residx_atomaa_mask
 __lowercase									=	residx_atomaa_to_atomaa.long()

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

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

 __lowercase									=	restype_atomaa_mask[protein_aatype]
 __lowercase									=	residx_atomaa_mask

 return protein


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






 """simple docstring"""







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




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: list[float] ):






 """simple docstring"""







 if len(UpperCamelCase__ ) < 2:
  raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
 if any(i <= 0 for i in nums ):
  raise ValueError("""All values must be greater than 0""" )
 __lowercase									=	nums.copy()
 copy_nums.sort()
 return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
