



"""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 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				torch.Tensor:
							...

@overload
def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Tuple[torch.Tensor, torch.Tensor]:
							...

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				str:
							A    =       aatype == rc.restype_order['G']
							A    =       rc.atom_order['CA']
							A    =       rc.atom_order['CB']
							A    =       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:
														A    =       torch.where(
														    lowerCamelCase__					,   all_atom_masks[..., ca_idx]					,   all_atom_masks[..., cb_idx]					,   )
														return pseudo_beta, pseudo_beta_mask
							else:
														return pseudo_beta

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				torch.Tensor:
							A    =       batched_gather(
							    lowerCamelCase__					,   batch['residx_atom37_to_atom14']					,   dim=-2					,   no_batch_dims=len(atomaa.shape[:-2]      )					,   )

							A    =       atomaa_data * batch['atom37_atom_exists'][..., None]

							return atomaa_data

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				torch.Tensor:
							A    =       template_feats['template_aatype']
							A    =       template_feats['template_torsion_angles_sin_cos']
							A    =       template_feats['template_alt_torsion_angles_sin_cos']
							A    =       template_feats['template_torsion_angles_mask']
							A    =       torch.cat(
							    [
							        nn.functional.one_hot(lowerCamelCase__					,   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 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__ = False					,   lowerCamelCase__ = 1E-2_0					,   lowerCamelCase__ = 1E8					,   )			->				torch.Tensor:
							A    =       batch['template_pseudo_beta_mask']
							A    =       template_mask[..., None] * template_mask[..., None, :]

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

							A    =       [dgram, template_mask_ad[..., None]]

							A    =       nn.functional.one_hot(
							    batch['template_aatype']					,   rc.restype_num + 2					,   )

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

							A   ,	A   ,	A    =       [rc.atom_order[a] for a in ['N', 'CA', 'C']]
							A    =       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=lowerCamelCase__					,   )
							A    =       rigids.get_trans()[..., None, :, :]
							A    =       rigids[..., None].invert_apply(lowerCamelCase__      )

							A    =       torch.rsqrt(eps + torch.sum(rigid_vec**2					,   dim=-1      )      )

							A    =       batch['template_all_atom_mask']
							A    =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
							A    =       template_mask[..., None] * template_mask[..., None, :]

							A    =       inv_distance_scalar * template_mask_ad
							A    =       rigid_vec * inv_distance_scalar[..., None]

							if not use_unit_vector:
														A    =       unit_vector * 0.0

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

							A    =       torch.cat(lowerCamelCase__					,   dim=-1      )
							A    =       act * template_mask_ad[..., None]

							return act

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				torch.Tensor:
							A    =       nn.functional.one_hot(batch['extra_msa']					,   23      )
							A    =       [
							    msa_ahot,
							    batch['extra_has_deletion'].unsqueeze(-1      ),
							    batch['extra_deletion_value'].unsqueeze(-1      ),
							]
							return torch.cat(lowerCamelCase__					,   dim=-1      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   )			->				Rigid:
							# [*, N, 8, 4, 4]
							A    =       rrgdf[aatype, ...]

							# [*, N, 8] transformations, i.e.
							#   One [*, N, 8, 3, 3] rotation matrix and
							#   One [*, N, 8, 3]    translation matrix
							A    =       r.from_tensor_axa(lowerCamelCase__      )

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

							# [*, N, 8, 2]
							A    =       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.

							A    =       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape      )
							A    =       1
							A    =       alpha[..., 1]
							A    =       -alpha[..., 0]
							A    =       alpha

							A    =       default_r.compose(Rigid(Rotation(rot_mats=lowerCamelCase__      )					,   lowerCamelCase__      )      )

							A    =       all_frames[..., 5]
							A    =       all_frames[..., 6]
							A    =       all_frames[..., 7]

							A    =       all_frames[..., 4]
							A    =       chia_frame_to_bb.compose(lowerCamelCase__      )
							A    =       chia_frame_to_bb.compose(lowerCamelCase__      )
							A    =       chia_frame_to_bb.compose(lowerCamelCase__      )

							A    =       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					,   )

							A    =       r[..., None].compose(lowerCamelCase__      )

							return all_frames_to_global

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   )			->				torch.Tensor:
							# [*, N, 14]
							A    =       group_idx[aatype, ...]

							# [*, N, 14, 8]
							A    =       nn.functional.one_hot(
							    lowerCamelCase__					,   num_classes=default_frames.shape[-3]					,   )

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

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

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

							# [*, N, 14, 3]
							A    =       lit_positions[aatype, ...]
							A    =       t_atoms_to_global.apply(lowerCamelCase__      )
							A    =       pred_positions * atom_mask

							return pred_positions








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				list:
							for i, _ in enumerate(lowerCamelCase__      ):
														if (i % 2 == 1) == (nums[i - 1] > nums[i]):
																					A   ,	A    =       nums[i], nums[i - 1]

							return nums


if __name__ == "__main__":
							print('Enter the array elements:')
							A     =      list(map(int, input().split()))
							print('The unsorted array is:')
							print(array)
							print('Array after Wiggle sort:')
							print(wiggle_sort(array))



