



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 	__a					(   lowerCAmelCase_       : Dict[str, torch.Tensor]    )				->			Dict[str, torch.Tensor]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       []

						for rt in rc.restypes:
												UpperCAmelCase_=       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]    )
												UpperCAmelCase_=       {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] * 14    )
						restype_atomaa_to_atomaa_list.append([0] * 37    )
						restype_atomaa_mask_list.append([0.0] * 14    )

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

						UpperCAmelCase_=       residx_atomaa_mask
						UpperCAmelCase_=       residx_atomaa_to_atomaa.long()

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

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

						UpperCAmelCase_=       restype_atomaa_mask[protein_aatype]
						UpperCAmelCase_=       residx_atomaa_mask

						return protein



def 	__a					(   lowerCAmelCase_       : Dict[str, torch.Tensor]    )				->			Dict[str, np.ndarray]:






						'''simple docstring'''



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









__A       =						6_5521



def 	__a					(   lowerCAmelCase_       : str    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for plain_chr in plain_text:
												UpperCAmelCase_=       (a + ord(lowerCAmelCase_    )) % MOD_ADLER
												UpperCAmelCase_=       (b + a) % MOD_ADLER
						return (b << 16) | a




