




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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)