

def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    0
     _lowerCAmelCase   :					Union[str, Any]        =    len(SCREAMING_SNAKE_CASE				)

     for i in range(n - 1				):
          for j in range(i + 1		,							SCREAMING_SNAKE_CASE				):
               if arr[i] > arr[j]:
                    num_inversions += 1

     return num_inversions







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     if len(SCREAMING_SNAKE_CASE				) <= 1:
          return arr, 0
     _lowerCAmelCase   :					List[str]        =    len(SCREAMING_SNAKE_CASE				) // 2
     _lowerCAmelCase   :					str        =    arr[0:mid]
     _lowerCAmelCase   :					Tuple        =    arr[mid:]

     _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    count_inversions_recursive(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    count_inversions_recursive(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    _count_cross_inversions(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					int        =    inversion_p + inversions_q + cross_inversions
     return c, num_inversions







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    []
     _lowerCAmelCase   :					int        =    0
     while i < len(SCREAMING_SNAKE_CASE				) and j < len(SCREAMING_SNAKE_CASE				):
          if p[i] > q[j]:
               # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
               # These are all inversions. The claim emerges from the
               # property that P is sorted.
               num_inversion += len(SCREAMING_SNAKE_CASE				) - i
               r.append(q[j]				)
               j += 1
          else:
               r.append(p[i]				)
               i += 1

     if i < len(SCREAMING_SNAKE_CASE				):
          r.extend(p[i:]				)
     else:
          r.extend(q[j:]				)

     return r, num_inversion







def 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    [10, 2, 1, 5, 5, 2, 11]

     # this arr has 8 inversions:
     # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

     _lowerCAmelCase   :					List[Any]        =    count_inversions_bf(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    count_inversions_recursive(SCREAMING_SNAKE_CASE				)

     assert num_inversions_bf == num_inversions_recursive == 8

     print("number of inversions = "		,							SCREAMING_SNAKE_CASE				)

     # testing an array with zero inversion (a sorted arr_1)

     arr_a.sort()
     _lowerCAmelCase   :					str        =    count_inversions_bf(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    count_inversions_recursive(SCREAMING_SNAKE_CASE				)

     assert num_inversions_bf == num_inversions_recursive == 0
     print("number of inversions = "		,							SCREAMING_SNAKE_CASE				)

     # an empty list should also have zero inversions
     _lowerCAmelCase   :					Optional[Any]        =    []
     _lowerCAmelCase   :					str        =    count_inversions_bf(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    count_inversions_recursive(SCREAMING_SNAKE_CASE				)

     assert num_inversions_bf == num_inversions_recursive == 0
     print("number of inversions = "		,							SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  main()









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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					int        =    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]				)
          _lowerCAmelCase   :					str        =    {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE				)}
          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				)

     _lowerCAmelCase   :					List[str]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					str        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.floataa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					List[str]        =    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
     _lowerCAmelCase   :					Dict        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					Optional[Any]        =    restype_atomaa_mask[protein_aatype]

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     _lowerCAmelCase   :					Optional[int]        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					str        =    residx_atomaa_to_atomaa.long()

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

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    tree_map(lambda SCREAMING_SNAKE_CASE				: torch.tensor(SCREAMING_SNAKE_CASE		,							device=batch["aatype"].device				)		,							SCREAMING_SNAKE_CASE		,							np.ndarray				)
     _lowerCAmelCase   :					Optional[int]        =    tensor_tree_map(lambda SCREAMING_SNAKE_CASE				: np.array(SCREAMING_SNAKE_CASE				)		,							make_atomaa_masks(SCREAMING_SNAKE_CASE				)				)
     return out






