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       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			None      )				->   torch.Tensor:







  """simple docstring"""





  ...



@overload
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor      )				->   Tuple[torch.Tensor, torch.Tensor]:







  """simple docstring"""





  ...



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			Any      )				->   List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= aatype == rc.restype_order["""G"""]
  SCREAMING_SNAKE_CASE__     				= rc.atom_order["""CA"""]
  SCREAMING_SNAKE_CASE__     				= rc.atom_order["""CB"""]
  SCREAMING_SNAKE_CASE__     				= 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:
    SCREAMING_SNAKE_CASE__     				= torch.where(
        __UpperCamelCase					,	all_atom_masks[..., ca_idx]					,	all_atom_masks[..., cb_idx]					,	)
    return pseudo_beta, pseudo_beta_mask
  else:
    return pseudo_beta



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			Dict[str, torch.Tensor]      )				->   torch.Tensor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= batched_gather(
      __UpperCamelCase					,	batch["""residx_atom37_to_atom14"""]					,	dim=-2					,	no_batch_dims=len(atomaa.shape[:-2]      )					,	)

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

  return atomaa_data



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict[str, torch.Tensor]      )				->   torch.Tensor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= template_feats["""template_aatype"""]
  SCREAMING_SNAKE_CASE__     				= template_feats["""template_torsion_angles_sin_cos"""]
  SCREAMING_SNAKE_CASE__     				= template_feats["""template_alt_torsion_angles_sin_cos"""]
  SCREAMING_SNAKE_CASE__     				= template_feats["""template_torsion_angles_mask"""]
  SCREAMING_SNAKE_CASE__     				= torch.cat(
      [
          nn.functional.one_hot(__UpperCamelCase					,	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       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict[str, torch.Tensor]					,	__UpperCamelCase       :			torch.types.Number					,	__UpperCamelCase       :			torch.types.Number					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			bool = False					,	__UpperCamelCase       :			float = 1E-20					,	__UpperCamelCase       :			float = 1E8					,	)				->   torch.Tensor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= batch["""template_pseudo_beta_mask"""]
  SCREAMING_SNAKE_CASE__     				= template_mask[..., None] * template_mask[..., None, :]

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

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

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

  SCREAMING_SNAKE_CASE__     				= batch["""template_aatype"""].shape[-1]
  to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]					,	__UpperCamelCase					,	-1					,	-1      )      )
  to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]					,	-1					,	__UpperCamelCase					,	-1      )      )

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

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

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

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

  if not use_unit_vector:
    SCREAMING_SNAKE_CASE__     				= unit_vector * 0.0

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

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

  return act



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict[str, torch.Tensor]      )				->   torch.Tensor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= nn.functional.one_hot(batch["""extra_msa"""]					,	23      )
  SCREAMING_SNAKE_CASE__     				= [
      msa_ahot,
      batch["""extra_has_deletion"""].unsqueeze(-1      ),
      batch["""extra_deletion_value"""].unsqueeze(-1      ),
  ]
  return torch.cat(__UpperCamelCase					,	dim=-1      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Rigid					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	)				->   Rigid:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= rrgdf[aatype, ...]

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

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

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

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

  SCREAMING_SNAKE_CASE__     				= default_r.compose(Rigid(Rotation(rot_mats=__UpperCamelCase      )					,	__UpperCamelCase      )      )

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

  SCREAMING_SNAKE_CASE__     				= all_frames[..., 4]
  SCREAMING_SNAKE_CASE__     				= chia_frame_to_bb.compose(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= chia_frame_to_bb.compose(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= chia_frame_to_bb.compose(__UpperCamelCase      )

  SCREAMING_SNAKE_CASE__     				= 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					,	)

  SCREAMING_SNAKE_CASE__     				= r[..., None].compose(__UpperCamelCase      )

  return all_frames_to_global






def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Rigid					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	__UpperCamelCase       :			torch.Tensor					,	)				->   torch.Tensor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= group_idx[aatype, ...]

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

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

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

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

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

  return pred_positions

from hashlib import mda, shaaaa

__lowerCamelCase     :					Dict				  =						(shaaaa, mda)







class __snake_case							:




   def __init__(       self     :							str			,      _lowercase     :							int = 8 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     SCREAMING_SNAKE_CASE__     				= size




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     self.bitarray |= h




   def       __a		(       self     :							int			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     return (h & self.bitarray) == h




   def __contains__(       self     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.exists(_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(_lowercase )[2:]
     return res.zfill(self.size )




   @property
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.format_bin(self.bitarray )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     for func in HASH_FUNCTIONS:
       SCREAMING_SNAKE_CASE__     				= (
           int.from_bytes(func(value.encode() ).digest()			,      """little""" ) % self.size
       )
       res |= 2**position
     return res




   def       __a		(       self     :							str			,      _lowercase     :							str ):



     """simple docstring"""






     return self.format_bin(self.hash_(_lowercase ) )




   @property
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(self.bitarray ).count("""1""" )
     return (n_ones / self.size) ** len(_lowercase )
