


'''simple docstring'''

from __future__ import annotations

from string import ascii_letters



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					str | None = None )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    alphabet or ascii_letters

   # The final result string
   UpperCamelCase__			    =    ""

   for character in input_string:
      if character not in alpha:
         # Append without encryption if character is not in the alphabet
         result += character
      else:
         # Get the index of the new key and make sure it isn't too large
         UpperCamelCase__			    =    (alpha.index(_UpperCamelCase ) + key) % len(_UpperCamelCase )

         # Append the encoded character to the alphabet
         result += alpha[new_key]

   return result



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					str | None = None )		->       str:





   '''simple docstring'''







   key *= -1

   return encrypt(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str | None = None )		->       dict[int, str]:





   '''simple docstring'''







   UpperCamelCase__			    =    alphabet or ascii_letters

   # To store data on all the combinations
   UpperCamelCase__			    =    {}

   # Cycle through each combination
   for key in range(1     ,		len(_UpperCamelCase ) + 1 ):
      # Decrypt the message and store the result in the data
      UpperCamelCase__			    =    decrypt(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )

   return brute_force_data


if __name__ == "__main__":
       while True:
              print(F"""\n{'-' * 10}\n Menu\n{'-' * 10}""")
              print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

              # get user input
              __lowercase:		List[str]    						=  input("\nWhat would you like to do?: ").strip() or "4"

              # run functions based on what the user chose
              if choice not in ("1", "2", "3", "4"):
                     print("Invalid choice, please enter a valid choice")
              elif choice == "1":
                     __lowercase:		Optional[Any]    						=  input("Please enter the string to be encrypted: ")
                     __lowercase:		Tuple    						=  int(input("Please enter off-set: ").strip())

                     print(encrypt(input_string, key))
              elif choice == "2":
                     __lowercase:		Any    						=  input("Please enter the string to be decrypted: ")
                     __lowercase:		Union[str, Any]    						=  int(input("Please enter off-set: ").strip())

                     print(decrypt(input_string, key))
              elif choice == "3":
                     __lowercase:		List[Any]    						=  input("Please enter the string to be decrypted: ")
                     __lowercase:		Optional[Any]    						=  brute_force(input_string)

                     for key, value in brute_force_data.items():
                            print(F"""Key: {key} | Message: {value}""")

              elif choice == "4":
                     print("Goodbye.")
                     break



'''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 							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   :					int     ,		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					str )		->       int:





   '''simple docstring'''







   UpperCamelCase__			    =    aatype == rc.restype_order["G"]
   UpperCamelCase__			    =    rc.atom_order["CA"]
   UpperCamelCase__			    =    rc.atom_order["CB"]
   UpperCamelCase__			    =    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:
      UpperCamelCase__			    =    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'''







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

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

   return atomaa_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    template_feats["template_aatype"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_alt_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_mask"]
   UpperCamelCase__			    =    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'''







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

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

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

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

   UpperCamelCase__			    =    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 ) )

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

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

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

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

   if not use_unit_vector:
      UpperCamelCase__			    =    unit_vector * 0.0

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

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

   return act



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    nn.functional.one_hot(batch["extra_msa"]     ,		23 )
   UpperCamelCase__			    =    [
       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'''







   UpperCamelCase__			    =    rrgdf[aatype, ...]

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

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

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

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

   UpperCamelCase__			    =    default_r.compose(Rigid(Rotation(rot_mats=_UpperCamelCase )     ,		_UpperCamelCase ) )

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

   UpperCamelCase__			    =    all_frames[..., 4]
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )

   UpperCamelCase__			    =    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     ,		)

   UpperCamelCase__			    =    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'''







   UpperCamelCase__			    =    group_idx[aatype, ...]

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

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

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

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

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

   return pred_positions