



'''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 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    ...





@overload
def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    ...





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			aatype == rc.restype_order["G"]
    UpperCamelCase__      =			rc.atom_order["CA"]
    UpperCamelCase__      =			rc.atom_order["CB"]
    UpperCamelCase__      =			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__      =			torch.where(
            lowerCAmelCase_					,  all_atom_masks[..., ca_idx]					,  all_atom_masks[..., cb_idx]					,  )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			batched_gather(
        lowerCAmelCase_					,  batch["residx_atom37_to_atom14"]					,  dim=-2					,  no_batch_dims=len(atomaa.shape[:-2]	)					,  )

    UpperCamelCase__      =			atomaa_data * batch["atom37_atom_exists"][..., None]

    return atomaa_data





def 						snake_case__ (lowerCAmelCase_	):
    UpperCamelCase__      =			template_feats["template_aatype"]
    UpperCamelCase__      =			template_feats["template_torsion_angles_sin_cos"]
    UpperCamelCase__      =			template_feats["template_alt_torsion_angles_sin_cos"]
    UpperCamelCase__      =			template_feats["template_torsion_angles_mask"]
    UpperCamelCase__      =			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 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_ = False					,  lowerCAmelCase_ = 1e-2_0					,  lowerCAmelCase_ = 1e8					,  ):
    UpperCamelCase__      =			batch["template_pseudo_beta_mask"]
    UpperCamelCase__      =			template_mask[..., None] * template_mask[..., None, :]

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

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

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

    UpperCamelCase__      =			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	)	)

    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			[rc.atom_order[a] for a in ["N", "CA", "C"]]
    UpperCamelCase__      =			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_					,  )
    UpperCamelCase__      =			rigids.get_trans()[..., None, :, :]
    UpperCamelCase__      =			rigids[..., None].invert_apply(lowerCAmelCase_	)

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

    UpperCamelCase__      =			batch["template_all_atom_mask"]
    UpperCamelCase__      =			t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    UpperCamelCase__      =			template_mask[..., None] * template_mask[..., None, :]

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

    if not use_unit_vector:
        UpperCamelCase__      =			unit_vector * 0.0

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

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

    return act





def 						snake_case__ (lowerCAmelCase_	):
    UpperCamelCase__      =			nn.functional.one_hot(batch["extra_msa"]					,  23	)
    UpperCamelCase__      =			[
        msa_ahot,
        batch["extra_has_deletion"].unsqueeze(-1	),
        batch["extra_deletion_value"].unsqueeze(-1	),
    ]
    return torch.cat(lowerCAmelCase_					,  dim=-1	)





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  ):
    # [*, N, 8, 4, 4]
    UpperCamelCase__      =			rrgdf[aatype, ...]

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

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

    # [*, N, 8, 2]
    UpperCamelCase__      =			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__      =			alpha.new_zeros(default_r.get_rots().get_rot_mats().shape	)
    UpperCamelCase__      =			1
    UpperCamelCase__      =			alpha[..., 1]
    UpperCamelCase__      =			-alpha[..., 0]
    UpperCamelCase__      =			alpha

    UpperCamelCase__      =			default_r.compose(Rigid(Rotation(rot_mats=lowerCAmelCase_	)					,  lowerCAmelCase_	)	)

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

    UpperCamelCase__      =			all_frames[..., 4]
    UpperCamelCase__      =			chia_frame_to_bb.compose(lowerCAmelCase_	)
    UpperCamelCase__      =			chia_frame_to_bb.compose(lowerCAmelCase_	)
    UpperCamelCase__      =			chia_frame_to_bb.compose(lowerCAmelCase_	)

    UpperCamelCase__      =			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__      =			r[..., None].compose(lowerCAmelCase_	)

    return all_frames_to_global





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  ):
    # [*, N, 14]
    UpperCamelCase__      =			group_idx[aatype, ...]

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

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

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

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

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

    return pred_positions









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


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




