

'''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	,							snake_case	,							snake_case  )    ->			torch.Tensor:
   ...







@overload
def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			Tuple[torch.Tensor, torch.Tensor]:
   ...







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			Any:
   _lowercase    :   Optional[int]      =     aatype == rc.restype_order["G"]
   _lowercase    :   Any      =     rc.atom_order["CA"]
   _lowercase    :   Optional[Any]      =     rc.atom_order["CB"]
   _lowercase    :   int      =     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:
      _lowercase    :   List[str]      =     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	,							snake_case  )    ->			torch.Tensor:
   _lowercase    :   List[str]      =     batched_gather(
       snake_case	,							batch["residx_atom37_to_atom14"]	,							dim=-2	,							no_batch_dims=len(atomaa.shape[:-2]  )	,							)

   _lowercase    :   str      =     atomaa_data * batch["atom37_atom_exists"][..., None]

   return atomaa_data







def       _A	(			snake_case  )    ->			torch.Tensor:
   _lowercase    :   List[Any]      =     template_feats["template_aatype"]
   _lowercase    :   Any      =     template_feats["template_torsion_angles_sin_cos"]
   _lowercase    :   int      =     template_feats["template_alt_torsion_angles_sin_cos"]
   _lowercase    :   str      =     template_feats["template_torsion_angles_mask"]
   _lowercase    :   str      =     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	,							snake_case	,							snake_case	,							snake_case	,							snake_case = False	,							snake_case = 1E-20	,							snake_case = 1E8	,							)    ->			torch.Tensor:
   _lowercase    :   str      =     batch["template_pseudo_beta_mask"]
   _lowercase    :   Tuple      =     template_mask[..., None] * template_mask[..., None, :]

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

   _lowercase    :   Dict      =     [dgram, template_mask_ad[..., None]]

   _lowercase    :   str      =     nn.functional.one_hot(
       batch["template_aatype"]	,							rc.restype_num + 2	,							)

   _lowercase    :   Tuple      =     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  )  )

   _lowercase  ,   _lowercase  ,   _lowercase    :   List[Any]      =     [rc.atom_order[a] for a in ["N", "CA", "C"]]
   _lowercase    :   Union[str, Any]      =     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	,							)
   _lowercase    :   Dict      =     rigids.get_trans()[..., None, :, :]
   _lowercase    :   int      =     rigids[..., None].invert_apply(snake_case  )

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

   _lowercase    :   Any      =     batch["template_all_atom_mask"]
   _lowercase    :   int      =     t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   _lowercase    :   Dict      =     template_mask[..., None] * template_mask[..., None, :]

   _lowercase    :   List[Any]      =     inv_distance_scalar * template_mask_ad
   _lowercase    :   Dict      =     rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      _lowercase    :   str      =     unit_vector * 0.0

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

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

   return act







def       _A	(			snake_case  )    ->			torch.Tensor:
   _lowercase    :   Optional[int]      =     nn.functional.one_hot(batch["extra_msa"]	,							23  )
   _lowercase    :   Dict      =     [
       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	,							snake_case	,							snake_case	,							snake_case	,							)    ->			Rigid:
   _lowercase    :   Optional[int]      =     rrgdf[aatype, ...]

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

   _lowercase    :   Tuple      =     alpha.new_zeros((*((1,) * len(alpha.shape[:-1]  )), 2)  )
   _lowercase    :   str      =     1

   # [*, N, 8, 2]
   _lowercase    :   List[str]      =     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.

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

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

   _lowercase    :   int      =     all_frames[..., 5]
   _lowercase    :   int      =     all_frames[..., 6]
   _lowercase    :   Optional[Any]      =     all_frames[..., 7]

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

   _lowercase    :   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	,							)

   _lowercase    :   Optional[int]      =     r[..., None].compose(snake_case  )

   return all_frames_to_global







def       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case	,							)    ->			torch.Tensor:
   _lowercase    :   str      =     group_idx[aatype, ...]

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

   # [*, N, 14, 8]
   _lowercase    :   int      =     r[..., None, :] * group_mask_one_hot

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

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

   # [*, N, 14, 3]
   _lowercase    :   Dict      =     lit_positions[aatype, ...]
   _lowercase    :   str      =     t_atoms_to_global.apply(snake_case  )
   _lowercase    :   int      =     pred_positions * atom_mask

   return pred_positions









'''simple docstring'''
def       _A	(			snake_case  )    ->			list:
   _lowercase    :   Optional[Any]      =     1.3
   _lowercase    :   Optional[Any]      =     len(snake_case  )
   _lowercase    :   Optional[int]      =     False

   while not completed:
      # Update the gap value for a next comb
      _lowercase    :   Tuple      =     int(gap / shrink_factor  )
      if gap <= 1:
         _lowercase    :   List[str]      =     True

      _lowercase    :   Optional[int]      =     0
      while index + gap < len(snake_case  ):
         if data[index] > data[index + gap]:
            # Swap values
            _lowercase  ,   _lowercase    :   Any      =     data[index + gap], data[index]
            _lowercase    :   Tuple      =     False
         index += 1

   return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _snake_case     						=						input('Enter numbers separated by a comma:\n').strip()
    _snake_case     						=						[int(item) for item in user_input.split(',')]
    print(comb_sort(unsorted))






