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__      ):
       ...






@overload
def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       ...






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   aatype == rc.restype_order['G']
       lowerCAmelCase__	      =   rc.atom_order['CA']
       lowerCAmelCase__	      =   rc.atom_order['CB']
       lowerCAmelCase__	      =   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:
              lowerCAmelCase__	      =   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__      ):
       lowerCAmelCase__	      =   batched_gather(
           lowerCAmelCase__      ,    batch['residx_atom37_to_atom14']      ,    dim=-2      ,    no_batch_dims=len(atomaa.shape[:-2]      )      ,    )

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

       return atomaa_data






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   template_feats['template_aatype']
       lowerCAmelCase__	      =   template_feats['template_torsion_angles_sin_cos']
       lowerCAmelCase__	      =   template_feats['template_alt_torsion_angles_sin_cos']
       lowerCAmelCase__	      =   template_feats['template_torsion_angles_mask']
       lowerCAmelCase__	      =   torch.cat(
           [
               nn.functional.one_hot(lowerCAmelCase__      ,    2_2      ),
               torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]      ,    1_4      ),
               alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]      ,    1_4      ),
               torsion_angles_mask,
           ]      ,    dim=-1      ,    )

       return template_angle_feat






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = False      ,    lowerCAmelCase__ = 1e-2_0      ,    lowerCAmelCase__ = 1e8      ,    ):
       lowerCAmelCase__	      =   batch['template_pseudo_beta_mask']
       lowerCAmelCase__	      =   template_mask[..., None] * template_mask[..., None, :]

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

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

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

       lowerCAmelCase__	      =   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      )      )

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

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

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

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

       if not use_unit_vector:
              lowerCAmelCase__	      =   unit_vector * 0.0

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

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

       return act






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   nn.functional.one_hot(batch['extra_msa']      ,    2_3      )
       lowerCAmelCase__	      =   [
           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__      ,    ):
       # [*, N, 8, 4, 4]
       lowerCAmelCase__	      =   rrgdf[aatype, ...]

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

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

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

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

       lowerCAmelCase__	      =   default_r.compose(Rigid(Rotation(rot_mats=lowerCAmelCase__      )      ,    lowerCAmelCase__      )      )

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

       lowerCAmelCase__	      =   all_frames[..., 4]
       lowerCAmelCase__	      =   chia_frame_to_bb.compose(lowerCAmelCase__      )
       lowerCAmelCase__	      =   chia_frame_to_bb.compose(lowerCAmelCase__      )
       lowerCAmelCase__	      =   chia_frame_to_bb.compose(lowerCAmelCase__      )

       lowerCAmelCase__	      =   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      ,    )

       lowerCAmelCase__	      =   r[..., None].compose(lowerCAmelCase__      )

       return all_frames_to_global






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    ):
       # [*, N, 14]
       lowerCAmelCase__	      =   group_idx[aatype, ...]

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

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

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

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

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

       return pred_positions

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   ArgumentParser(
           description=(
               'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
           )      )

       # Optional arguments for the launch helper
       parser.add_argument('--num_cores'      ,    type=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    help=(
               'The full path to the single TPU training '
               'program/script to be launched in parallel, '
               'followed by all the arguments for the '
               'training script'
           )      ,    )

       # rest from the training program
       parser.add_argument('training_script_args'      ,    nargs=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

       xmp.spawn(mod._mp_fn      ,    args=()      ,    nprocs=args.num_cores      )


if __name__ == "__main__":
    main()
