



'''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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     ...




@overload
def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     ...




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  batched_gather(
         __A  ,     batch['''residx_atom37_to_atom14''']  ,     dim=-2  ,     no_batch_dims=len(atomaa.shape[:-2])  ,     )

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

     return atomaa_data




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  template_feats['''template_aatype''']
     _a     =  template_feats['''template_torsion_angles_sin_cos''']
     _a     =  template_feats['''template_alt_torsion_angles_sin_cos''']
     _a     =  template_feats['''template_torsion_angles_mask''']
     _a     =  torch.cat(
         [
             nn.functional.one_hot(__A  ,     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  (__A  ,     __A  ,     __A  ,     __A  ,     __A = False  ,     __A = 1e-20  ,     __A = 1e8  ,     ):



     """simple docstring"""



     _a     =  batch['''template_pseudo_beta_mask''']
     _a     =  template_mask[..., None] * template_mask[..., None, :]

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

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

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

     _a     =  batch['''template_aatype'''].shape[-1]
     to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]  ,     __A  ,     -1  ,     -1))
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]  ,     -1  ,     __A  ,     -1))

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

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

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

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

     if not use_unit_vector:
          _a     =  unit_vector * 0.0

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

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

     return act




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  rrgdf[aatype, ...]

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

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

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

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

     _a     =  default_r.compose(Rigid(Rotation(rot_mats=__A)  ,     __A))

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

     _a     =  all_frames[..., 4]
     _a     =  chia_frame_to_bb.compose(__A)
     _a     =  chia_frame_to_bb.compose(__A)
     _a     =  chia_frame_to_bb.compose(__A)

     _a     =  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  ,     )

     _a     =  r[..., None].compose(__A)

     return all_frames_to_global




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  group_idx[aatype, ...]

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

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

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

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

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

     return pred_positions








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



