




"""simple docstring"""

class  _snake_case				:

    '''simple docstring'''




    def __init__(							self      :  Optional[int]					,							snake_case      :  int     ):
        UpperCAmelCase_					:Optional[int]      			=      data
        UpperCAmelCase_					:int
        UpperCAmelCase_					:Node



def 				a (						__snake_case     :				Node  ):



    '''simple docstring'''





    UpperCAmelCase_					:Dict      			=      0
    UpperCAmelCase_					:Optional[Any]      			=      x



def 				a (						__snake_case     :				Node,    __snake_case     :				Node  ):



    '''simple docstring'''





    UpperCAmelCase_					:Any      			=      find_set(__snake_case  ), find_set(__snake_case  )
    if x == y:
        return

    elif x.rank > y.rank:
        UpperCAmelCase_					:List[Any]      			=      x
    else:
        UpperCAmelCase_					:Optional[int]      			=      y
        if x.rank == y.rank:
            y.rank += 1



def 				a (						__snake_case     :				Node  ):



    '''simple docstring'''





    if x != x.parent:
        UpperCAmelCase_					:str      			=      find_set(x.parent  )
    return x.parent



def 				a (						__snake_case     :				Node  ):



    '''simple docstring'''





    UpperCAmelCase_					:List[Any]      			=      ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    UpperCAmelCase_					:Union[str, Any]      			=      f'{node.data} is not in {sets}'
    raise ValueError(__snake_case  )



def 				a (						):



    '''simple docstring'''





    UpperCAmelCase_					:List[str]      			=      [Node(__snake_case  ) for i in range(6  )]
    for v in vertex:
        make_set(__snake_case  )

    union_set(vertex[0],    vertex[1]  )
    union_set(vertex[1],    vertex[2]  )
    union_set(vertex[3],    vertex[4]  )
    union_set(vertex[3],    vertex[5]  )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(__snake_case  ).isdisjoint(find_python_set(__snake_case  )  ):
                assert find_set(__snake_case  ) != find_set(__snake_case  )
            else:
                assert find_set(__snake_case  ) == find_set(__snake_case  )


if __name__ == "__main__":
      test_disjoint_set()








"""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 (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				None  ):



				'''simple docstring'''





				...



@overload
def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor  ):



				'''simple docstring'''





				...



def 				a (						__snake_case     :				List[str],    __snake_case     :				Optional[int],    __snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      aatype == rc.restype_order['''G''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CA''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CB''']
				UpperCAmelCase_					:Optional[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_					:Any      			=      torch.where(
								    __snake_case,    all_atom_masks[..., ca_idx],    all_atom_masks[..., cb_idx],    )
								return pseudo_beta, pseudo_beta_mask
				else:
								return pseudo_beta



def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      batched_gather(
				    __snake_case,    batch['''residx_atom37_to_atom14'''],    dim=-2,    no_batch_dims=len(atomaa.shape[:-2]  ),    )

				UpperCAmelCase_					:Optional[Any]      			=      atomaa_data * batch['''atom37_atom_exists'''][..., None]

				return atomaa_data



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      template_feats['''template_aatype''']
				UpperCAmelCase_					:Union[str, Any]      			=      template_feats['''template_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_alt_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_torsion_angles_mask''']
				UpperCAmelCase_					:int      			=      torch.cat(
				    [
				        nn.functional.one_hot(__snake_case,    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 (						__snake_case     :				Dict[str, torch.Tensor],    __snake_case     :				torch.types.Number,    __snake_case     :				torch.types.Number,    __snake_case     :				int,    __snake_case     :				bool = False,    __snake_case     :				float = 1E-20,    __snake_case     :				float = 1E8,    ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      batch['''template_pseudo_beta_mask''']
				UpperCAmelCase_					:Any      			=      template_mask[..., None] * template_mask[..., None, :]

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

				UpperCAmelCase_					:List[str]      			=      [dgram, template_mask_ad[..., None]]

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

				UpperCAmelCase_					:Optional[Any]      			=      batch['''template_aatype'''].shape[-1]
				to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2],    __snake_case,    -1,    -1  )  )
				to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2],    -1,    __snake_case,    -1  )  )

				UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
				UpperCAmelCase_					:str      			=      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=__snake_case,    )
				UpperCAmelCase_					:Dict      			=      rigids.get_trans()[..., None, :, :]
				UpperCAmelCase_					:Dict      			=      rigids[..., None].invert_apply(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      torch.rsqrt(eps + torch.sum(rigid_vec**2,    dim=-1  )  )

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

				UpperCAmelCase_					:str      			=      inv_distance_scalar * template_mask_ad
				UpperCAmelCase_					:List[Any]      			=      rigid_vec * inv_distance_scalar[..., None]

				if not use_unit_vector:
								UpperCAmelCase_					:List[Any]      			=      unit_vector * 0.0

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

				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case,    dim=-1  )
				UpperCAmelCase_					:Optional[Any]      			=      act * template_mask_ad[..., None]

				return act



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(batch['''extra_msa'''],    23  )
				UpperCAmelCase_					:Union[str, Any]      			=      [
				    msa_ahot,
				    batch['''extra_has_deletion'''].unsqueeze(-1  ),
				    batch['''extra_deletion_value'''].unsqueeze(-1  ),
				]
				return torch.cat(__snake_case,    dim=-1  )



def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      rrgdf[aatype, ...]

				# [*, N, 8] transformations, i.e.
				#   One [*, N, 8, 3, 3] rotation matrix and
				#   One [*, N, 8, 3]    translation matrix
				UpperCAmelCase_					:Optional[int]      			=      r.from_tensor_axa(__snake_case  )

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

				# [*, N, 8, 2]
				UpperCAmelCase_					:Tuple      			=      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_					:Any      			=      alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
				UpperCAmelCase_					:Optional[Any]      			=      1
				UpperCAmelCase_					:int      			=      alpha[..., 1]
				UpperCAmelCase_					:Optional[int]      			=      -alpha[..., 0]
				UpperCAmelCase_					:int      			=      alpha

				UpperCAmelCase_					:List[str]      			=      default_r.compose(Rigid(Rotation(rot_mats=__snake_case  ),    __snake_case  )  )

				UpperCAmelCase_					:List[str]      			=      all_frames[..., 5]
				UpperCAmelCase_					:str      			=      all_frames[..., 6]
				UpperCAmelCase_					:List[Any]      			=      all_frames[..., 7]

				UpperCAmelCase_					:Tuple      			=      all_frames[..., 4]
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      chia_frame_to_bb.compose(__snake_case  )

				UpperCAmelCase_					:List[str]      			=      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_					:Any      			=      r[..., None].compose(__snake_case  )

				return all_frames_to_global





def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      group_idx[aatype, ...]

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

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

				# [*, N, 14]
				UpperCAmelCase_					:List[Any]      			=      t_atoms_to_global.map_tensor_fn(lambda __snake_case  : torch.sum(__snake_case,    dim=-1  )  )

				# [*, N, 14, 1]
				UpperCAmelCase_					:List[Any]      			=      atom_mask[aatype, ...].unsqueeze(-1  )

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

				return pred_positions


