





"""simple docstring"""




from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather





@overload
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						...





@overload
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						...





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[str]     =    aatype == rc.restype_order['''G''']
						_UpperCamelCase			:					Union[str, Any]     =    rc.atom_order['''CA''']
						_UpperCamelCase			:					Optional[Any]     =    rc.atom_order['''CB''']
						_UpperCamelCase			:					Any     =    torch.where(
						    is_gly[..., None].expand(*((-1,) * len(is_gly.shape   ))       ,						3   )       ,						all_atom_positions[..., ca_idx, :]       ,						all_atom_positions[..., cb_idx, :]       ,						)

						if all_atom_masks is not None:
												_UpperCamelCase			:					int     =    torch.where(
												    _lowercase       ,						all_atom_masks[..., ca_idx]       ,						all_atom_masks[..., cb_idx]       ,						)
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    batched_gather(
						    _lowercase       ,						batch['''residx_atom37_to_atom14''']       ,						dim=-2       ,						no_batch_dims=len(atomaa.shape[:-2]   )       ,						)

						_UpperCamelCase			:					int     =    atomaa_data * batch['''atom37_atom_exists'''][..., None]

						return atomaa_data





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Any     =    template_feats['''template_aatype''']
						_UpperCamelCase			:					Tuple     =    template_feats['''template_torsion_angles_sin_cos''']
						_UpperCamelCase			:					Optional[int]     =    template_feats['''template_alt_torsion_angles_sin_cos''']
						_UpperCamelCase			:					Dict     =    template_feats['''template_torsion_angles_mask''']
						_UpperCamelCase			:					Tuple     =    torch.cat(
						    [
						        nn.functional.one_hot(_lowercase       ,						22   ),
						        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]       ,						14   ),
						        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]       ,						14   ),
						        torsion_angles_mask,
						    ]       ,						dim=-1       ,						)

						return template_angle_feat





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase = False       ,						_lowercase = 1E-20       ,						_lowercase = 1E8       ,						):
						_UpperCamelCase			:					List[str]     =    batch['''template_pseudo_beta_mask''']
						_UpperCamelCase			:					Union[str, Any]     =    template_mask[..., None] * template_mask[..., None, :]

						# Compute distogram (this seems to differ slightly from Alg. 5)
						_UpperCamelCase			:					str     =    batch['''template_pseudo_beta''']
						_UpperCamelCase			:					Union[str, Any]     =    torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2       ,						dim=-1       ,						keepdim=_lowercase   )
						_UpperCamelCase			:					List[str]     =    torch.linspace(_lowercase       ,						_lowercase       ,						_lowercase       ,						device=tpb.device   ) ** 2
						_UpperCamelCase			:					Dict     =    torch.cat([lower[1:], lower.new_tensor([inf]   )]       ,						dim=-1   )
						_UpperCamelCase			:					Union[str, Any]     =    ((dgram > lower) * (dgram < upper)).type(dgram.dtype   )

						_UpperCamelCase			:					List[Any]     =    [dgram, template_mask_ad[..., None]]

						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(
						    batch['''template_aatype''']       ,						rc.restype_num + 2       ,						)

						_UpperCamelCase			:					Tuple     =    batch['''template_aatype'''].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]       ,						_lowercase       ,						-1       ,						-1   )   )
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]       ,						-1       ,						_lowercase       ,						-1   )   )

						_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					int     =    [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
						_UpperCamelCase			:					int     =    Rigid.make_transform_from_reference(
						    n_xyz=batch['''template_all_atom_positions'''][..., n, :]       ,						ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]       ,						c_xyz=batch['''template_all_atom_positions'''][..., c, :]       ,						eps=_lowercase       ,						)
						_UpperCamelCase			:					int     =    rigids.get_trans()[..., None, :, :]
						_UpperCamelCase			:					Optional[int]     =    rigids[..., None].invert_apply(_lowercase   )

						_UpperCamelCase			:					Union[str, Any]     =    torch.rsqrt(eps + torch.sum(rigid_vec**2       ,						dim=-1   )   )

						_UpperCamelCase			:					Tuple     =    batch['''template_all_atom_mask''']
						_UpperCamelCase			:					List[Any]     =    t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						_UpperCamelCase			:					Optional[int]     =    template_mask[..., None] * template_mask[..., None, :]

						_UpperCamelCase			:					Union[str, Any]     =    inv_distance_scalar * template_mask_ad
						_UpperCamelCase			:					Optional[int]     =    rigid_vec * inv_distance_scalar[..., None]

						if not use_unit_vector:
												_UpperCamelCase			:					List[str]     =    unit_vector * 0.0

						to_concat.extend(torch.unbind(unit_vector[..., None, :]       ,						dim=-1   )   )
						to_concat.append(template_mask_ad[..., None]   )

						_UpperCamelCase			:					Dict     =    torch.cat(_lowercase       ,						dim=-1   )
						_UpperCamelCase			:					List[Any]     =    act * template_mask_ad[..., None]

						return act





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(batch['''extra_msa''']       ,						23   )
						_UpperCamelCase			:					Dict     =    [
						    msa_ahot,
						    batch['''extra_has_deletion'''].unsqueeze(-1   ),
						    batch['''extra_deletion_value'''].unsqueeze(-1   ),
						]
						return torch.cat(_lowercase       ,						dim=-1   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						):
						# [*, N, 8, 4, 4]
						_UpperCamelCase			:					Any     =    rrgdf[aatype, ...]

						# [*, N, 8] transformations, i.e.
						#   One [*, N, 8, 3, 3] rotation matrix and
						#   One [*, N, 8, 3]    translation matrix
						_UpperCamelCase			:					List[str]     =    r.from_tensor_axa(_lowercase   )

						_UpperCamelCase			:					List[Any]     =    alpha.new_zeros((*((1,) * len(alpha.shape[:-1]   )), 2)   )
						_UpperCamelCase			:					Dict     =    1

						# [*, N, 8, 2]
						_UpperCamelCase			:					Optional[int]     =    torch.cat([bb_rot.expand(*alpha.shape[:-2]       ,						-1       ,						-1   ), alpha]       ,						dim=-2   )

						# [*, N, 8, 3, 3]
						# Produces rotation matrices of the form:
						# [
						#   [1, 0  , 0  ],
						#   [0, a_2,-a_1],
						#   [0, a_1, a_2]
						# ]
						# This follows the original code rather than the supplement, which uses
						# different indices.

						_UpperCamelCase			:					Optional[int]     =    alpha.new_zeros(default_r.get_rots().get_rot_mats().shape   )
						_UpperCamelCase			:					Optional[int]     =    1
						_UpperCamelCase			:					List[str]     =    alpha[..., 1]
						_UpperCamelCase			:					int     =    -alpha[..., 0]
						_UpperCamelCase			:					Any     =    alpha

						_UpperCamelCase			:					List[Any]     =    default_r.compose(Rigid(Rotation(rot_mats=_lowercase   )       ,						_lowercase   )   )

						_UpperCamelCase			:					Union[str, Any]     =    all_frames[..., 5]
						_UpperCamelCase			:					Any     =    all_frames[..., 6]
						_UpperCamelCase			:					Union[str, Any]     =    all_frames[..., 7]

						_UpperCamelCase			:					Optional[int]     =    all_frames[..., 4]
						_UpperCamelCase			:					Optional[int]     =    chia_frame_to_bb.compose(_lowercase   )
						_UpperCamelCase			:					int     =    chia_frame_to_bb.compose(_lowercase   )
						_UpperCamelCase			:					List[Any]     =    chia_frame_to_bb.compose(_lowercase   )

						_UpperCamelCase			:					Any     =    Rigid.cat(
						    [
						        all_frames[..., :5],
						        chia_frame_to_bb.unsqueeze(-1   ),
						        chia_frame_to_bb.unsqueeze(-1   ),
						        chia_frame_to_bb.unsqueeze(-1   ),
						    ]       ,						dim=-1       ,						)

						_UpperCamelCase			:					List[Any]     =    r[..., None].compose(_lowercase   )

						return all_frames_to_global






def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						):
						# [*, N, 14]
						_UpperCamelCase			:					List[Any]     =    group_idx[aatype, ...]

						# [*, N, 14, 8]
						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(
						    _lowercase       ,						num_classes=default_frames.shape[-3]       ,						)

						# [*, N, 14, 8]
						_UpperCamelCase			:					Dict     =    r[..., None, :] * group_mask_one_hot

						# [*, N, 14]
						_UpperCamelCase			:					Tuple     =    t_atoms_to_global.map_tensor_fn(lambda _lowercase   : torch.sum(_lowercase       ,						dim=-1   )   )

						# [*, N, 14, 1]
						_UpperCamelCase			:					str     =    atom_mask[aatype, ...].unsqueeze(-1   )

						# [*, N, 14, 3]
						_UpperCamelCase			:					List[str]     =    lit_positions[aatype, ...]
						_UpperCamelCase			:					Optional[int]     =    t_atoms_to_global.apply(_lowercase   )
						_UpperCamelCase			:					Optional[Any]     =    pred_positions * atom_mask

						return pred_positions










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



