from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []
  SCREAMING_SNAKE_CASE__    							=   []
  SCREAMING_SNAKE_CASE__    							=   []

  for rt in rc.restypes:
    SCREAMING_SNAKE_CASE__    							=   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
    restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]			)
    SCREAMING_SNAKE_CASE__    							=   {name: i for i, name in enumerate(__UpperCamelCase			)}
    restype_atomaa_to_atomaa_list.append(
        [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]			)

    restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]			)

  # Add dummy mapping for restype 'UNK'
  restype_atomaa_to_atomaa_list.append([0] * 14			)
  restype_atomaa_to_atomaa_list.append([0] * 37			)
  restype_atomaa_mask_list.append([0.0] * 14			)

  SCREAMING_SNAKE_CASE__    							=   torch.tensor(
      __UpperCamelCase				,  dtype=torch.intaa				,  device=protein["""aatype"""].device				,  )
  SCREAMING_SNAKE_CASE__    							=   torch.tensor(
      __UpperCamelCase				,  dtype=torch.intaa				,  device=protein["""aatype"""].device				,  )
  SCREAMING_SNAKE_CASE__    							=   torch.tensor(
      __UpperCamelCase				,  dtype=torch.floataa				,  device=protein["""aatype"""].device				,  )
  SCREAMING_SNAKE_CASE__    							=   protein["""aatype"""].to(torch.long			)

  # create the mapping for (residx, atom14) --> atom37, i.e. an array
  # with shape (num_res, 14) containing the atom37 indices for this protein
  SCREAMING_SNAKE_CASE__    							=   restype_atomaa_to_atomaa[protein_aatype]
  SCREAMING_SNAKE_CASE__    							=   restype_atomaa_mask[protein_aatype]

  SCREAMING_SNAKE_CASE__    							=   residx_atomaa_mask
  SCREAMING_SNAKE_CASE__    							=   residx_atomaa_to_atomaa.long()

  # create the gather indices for mapping back
  SCREAMING_SNAKE_CASE__    							=   restype_atomaa_to_atomaa[protein_aatype]
  SCREAMING_SNAKE_CASE__    							=   residx_atomaa_to_atomaa.long()

  # create the corresponding mask
  SCREAMING_SNAKE_CASE__    							=   torch.zeros([21, 37]				,  dtype=torch.floataa				,  device=protein["""aatype"""].device			)
  for restype, restype_letter in enumerate(rc.restypes			):
    SCREAMING_SNAKE_CASE__    							=   rc.restype_atoa[restype_letter]
    SCREAMING_SNAKE_CASE__    							=   rc.residue_atoms[restype_name]
    for atom_name in atom_names:
      SCREAMING_SNAKE_CASE__    							=   rc.atom_order[atom_name]
      SCREAMING_SNAKE_CASE__    							=   1

  SCREAMING_SNAKE_CASE__    							=   restype_atomaa_mask[protein_aatype]
  SCREAMING_SNAKE_CASE__    							=   residx_atomaa_mask

  return protein






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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   tree_map(lambda __UpperCamelCase			: torch.tensor(__UpperCamelCase				,  device=batch["""aatype"""].device			)				,  __UpperCamelCase				,  np.ndarray			)
  SCREAMING_SNAKE_CASE__    							=   tensor_tree_map(lambda __UpperCamelCase			: np.array(__UpperCamelCase			)				,  make_atomaa_masks(__UpperCamelCase			)			)
  return out

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
