





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 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   int:
	A__							:					Optional[Any]       =	[]
	A__							:					int       =	[]
	A__							:					str       =	[]

	for rt in rc.restypes:
		A__							:					str       =	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__							:					List[Any]       =	{name: i for i, name in enumerate(_lowercase      )}
		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__							:					Dict       =	torch.tensor(
	    _lowercase					,   dtype=torch.intaa					,   device=protein["""aatype"""].device					,   )
	A__							:					int       =	torch.tensor(
	    _lowercase					,   dtype=torch.intaa					,   device=protein["""aatype"""].device					,   )
	A__							:					Optional[Any]       =	torch.tensor(
	    _lowercase					,   dtype=torch.floataa					,   device=protein["""aatype"""].device					,   )
	A__							:					Optional[int]       =	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__							:					Optional[int]       =	restype_atomaa_to_atomaa[protein_aatype]
	A__							:					Optional[int]       =	restype_atomaa_mask[protein_aatype]

	A__							:					List[Any]       =	residx_atomaa_mask
	A__							:					Optional[int]       =	residx_atomaa_to_atomaa.long()

	# create the gather indices for mapping back
	A__							:					List[str]       =	restype_atomaa_to_atomaa[protein_aatype]
	A__							:					Union[str, Any]       =	residx_atomaa_to_atomaa.long()

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

	A__							:					Optional[int]       =	restype_atomaa_mask[protein_aatype]
	A__							:					Union[str, Any]       =	residx_atomaa_mask

	return protein


def 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   str:
	A__							:					Tuple       =	tree_map(lambda lowercase_      : torch.tensor(_lowercase					,   device=batch["""aatype"""].device      )					,   _lowercase					,   np.ndarray      )
	A__							:					Optional[int]       =	tensor_tree_map(lambda lowercase_      : np.array(_lowercase      )					,   make_atomaa_masks(_lowercase      )      )
	return out






def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				int      )  ->   int:
	while second != 0:
		A__							:					int       =	first & second
		first ^= second
		A__							:					int       =	c << 1
	return first


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A_ :				Optional[Any]						 =    int(input('Enter the first number: ').strip())
	A_ :				List[str]						 =    int(input('Enter the second number: ').strip())
	print(f'''{add(first, second) = }''')



