




import io
import json
import subprocess


__SCREAMING_SNAKE_CASE      :				int				      =[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

__SCREAMING_SNAKE_CASE      :				List[str]				      =8

def      lowerCAmelCase_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    Optional[int]  )   ->     List[str]:


      '''simple docstring'''




      lowerCamelCase__ 					=			{}
      for src, tgt in pairs:
            lowerCamelCase__ 					=			f"""{src}-{tgt}"""
            lowerCamelCase__ 					=			f"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
            lowerCamelCase__ 					=			subprocess.run(lowerCamelCase       ,			stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
            lowerCamelCase__ 					=			f"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
            lowerCamelCase__ 					=			subprocess.run(lowerCamelCase       ,			stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
            lowerCamelCase__ 					=			{'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
      return text


__SCREAMING_SNAKE_CASE      :				Any				      =get_all_data(pairs, n_objs)
__SCREAMING_SNAKE_CASE      :				Optional[int]				      ="""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
      __SCREAMING_SNAKE_CASE      :				List[str]				      =json.dump(text, f, indent=2, ensure_ascii=False)








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:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    None  )   ->     torch.Tensor:


      '''simple docstring'''




      ...

@overload
def      lowerCAmelCase_     (					lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor  )   ->     Tuple[torch.Tensor, torch.Tensor]:


      '''simple docstring'''




      ...

def      lowerCAmelCase_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Union[str, Any]       ,			lowerCamelCase:    Tuple  )   ->     int:


      '''simple docstring'''




      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:    torch.Tensor       ,			lowerCamelCase:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      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:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      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       ,			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:    Dict[str, torch.Tensor]       ,			lowerCamelCase:    torch.types.Number       ,			lowerCamelCase:    torch.types.Number       ,			lowerCamelCase:    int       ,			lowerCamelCase:    bool = False       ,			lowerCamelCase:    float = 1e-20       ,			lowerCamelCase:    float = 1e8       ,			)   ->     torch.Tensor:


      '''simple docstring'''




      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:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			nn.functional.one_hot(batch['extra_msa']       ,			23  )
      lowerCamelCase__ 					=			[
          msa_ahot,
          batch['extra_has_deletion'].unsqueeze(-1  ),
          batch['extra_deletion_value'].unsqueeze(-1  ),
      ]
      return torch.cat(lowerCamelCase       ,			dim=-1  )

def      lowerCAmelCase_     (					lowerCamelCase:    Rigid       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			)   ->     Rigid:


      '''simple docstring'''




      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:    Rigid       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			)   ->     torch.Tensor:


      '''simple docstring'''




      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