code: 
  
  
  
  
  """simple docstring"""
  
  
  
  SCREAMING_SNAKE_CASE       = [
      ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
       "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
      ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
       "ef-e3", "eg-e2", "fg-e6"],
      ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
       "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
      ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
      ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
       "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
  ]
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       set()
   for row in edge_array:
    for item in row:
     distinct_edge.add(item[0]		)
   return list(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Dict:
   A__ =       ["0"] * len(lowercase_		)
   for i, row in enumerate(lowercase_		):
    for item in row:
     if distinct_edge in item[0]:
      A__ =       "1"
      break
   return "".join(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
   A__ =       get_distinct_edge(lowercase_		)
   A__ =       {}
  
   for item in distinct_edge:
    A__ =       get_bitcode(lowercase_    ,				lowercase_		)
    # print('bit',bit)
    # bt=''.join(bit)
    A__ =       bit.count("1"		)
    A__ =       [s, bit]
   # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
   A__ =       [
       [k, v[0], v[1]]
       for k, v in sorted(frequency_table.items()    ,				key=lambda lowercase_		: v[1][0]    ,				reverse=lowercase_		)
   ]
   return sorted_frequency_table
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
   A__ =       {}
   for _, item in enumerate(lowercase_		):
    nodes.setdefault(item[2]    ,				[]		).append(item[0]		)
   return nodes
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       {}
   for key, value in nodes.items():
    A__ =       value
   return cluster
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   return [i * 1_00 / len(lowercase_		) for i in cluster]
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	None:
   print("\nNodes\n"		)
   for key, value in nodes.items():
    print(lowercase_    ,				lowercase_		)
   print("\nSupport\n"		)
   print(lowercase_		)
   print("\n Cluster \n"		)
   for key, value in sorted(cluster.items()    ,				reverse=lowercase_		):
    print(lowercase_    ,				lowercase_		)
   print("\n Graph\n"		)
   for key, value in graph.items():
    print(lowercase_    ,				lowercase_		)
   print("\n Edge List of Frequent subgraphs \n"		)
   for edge_list in freq_subgraph_edge_list:
    print(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[Any]:
   for i in cluster[ca]:
    A__ =       0
    A__ =       ca + 1
    while ca < max(cluster.keys()		):
     for j in cluster[ca]:
      if int(lowercase_    ,				2		) & int(lowercase_    ,				2		) == int(lowercase_    ,				2		):
       if tuple(nodes[i]		) in graph:
        graph[tuple(nodes[i]		)].append(nodes[j]		)
       else:
        A__ =       [nodes[j]]
       count += 1
     if count == 0:
      A__ =       ca + 1
     else:
      break
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
   A__ =       cluster[max(cluster.keys()		)]
   A__ =       "Header"
   A__ =       {}
   for i in x:
    if (["Header"],) in graph:
     graph[(["Header"],)].append(x[i]		)
    else:
     A__ =       [x[i]]
   for i in x:
    A__ =       [["Header"]]
   A__ =       1
   while i < max(lowercase_		) - 1:
    create_edge(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)
    A__ =       i + 1
   return graph
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_=None		)		->	List[str]:
   A__ =       (path or []) + [start]
   if start == end:
    paths.append(lowercase_		)
   for node in graph[start]:
    if tuple(lowercase_		) not in path:
     my_dfs(lowercase_    ,				tuple(lowercase_		)    ,				lowercase_    ,				lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Dict:
   A__ =       int(s / 1_00 * (len(lowercase_		) - 1)		)
   for i in cluster[k]:
    my_dfs(lowercase_    ,				tuple(cluster[k][i]		)    ,				(["Header"],)		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
   A__ =       []
   for edges in paths:
    A__ =       []
    for j in range(len(lowercase_		) - 1		):
     A__ =       list(edges[j]		)
     for e in temp:
      A__ =       (e[0], e[1])
      el.append(lowercase_		)
    freq_sub_el.append(lowercase_		)
   return freq_sub_el
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	int:
   for i in range(len(lowercase_		)		):
    for j in range(len(edge_array[i]		)		):
     A__ =       edge_array[i][j].split("-"		)
     A__ =       t
  
  
  if __name__ == "__main__":
        preprocess(edge_array)
        SCREAMING_SNAKE_CASE       = get_frequency_table(edge_array)
        SCREAMING_SNAKE_CASE       = get_nodes(frequency_table)
        SCREAMING_SNAKE_CASE       = get_cluster(nodes)
        SCREAMING_SNAKE_CASE       = get_support(cluster)
        SCREAMING_SNAKE_CASE       = construct_graph(cluster, nodes)
        find_freq_subgraph_given_support(60, cluster, graph)
        SCREAMING_SNAKE_CASE       = []
        SCREAMING_SNAKE_CASE       = freq_subgraphs_edge_list(paths)
        print_all()
  
  
  
  
  
  
style_context: 
  
  
  
  
  """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     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	torch.Tensor:
   ...
  
  
  
  
  
  
  
  @overload
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Tuple[torch.Tensor, torch.Tensor]:
   ...
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Dict:
   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(
        lowercase_    ,				all_atom_masks[..., ca_idx]    ,				all_atom_masks[..., cb_idx]    ,				)
    return pseudo_beta, pseudo_beta_mask
   else:
    return pseudo_beta
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	torch.Tensor:
   A__ =       batched_gather(
       lowercase_    ,				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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	torch.Tensor:
   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(lowercase_    ,				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     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_ = False    ,				lowercase_ = 1E-20    ,				lowercase_ = 1E8    ,				)		->	torch.Tensor:
   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=lowercase_		)
   A__ =       torch.linspace(lowercase_    ,				lowercase_    ,				lowercase_    ,				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]    ,				lowercase_    ,				-1    ,				-1		)		)
   to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]    ,				-1    ,				lowercase_    ,				-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=lowercase_    ,				)
   A__ =       rigids.get_trans()[..., None, :, :]
   A__ =       rigids[..., None].invert_apply(lowercase_		)
  
   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(lowercase_    ,				dim=-1		)
   A__ =       act * template_mask_ad[..., None]
  
   return act
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	torch.Tensor:
   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(lowercase_    ,				dim=-1		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				)		->	Rigid:
   # [*, N, 8, 4, 4]
   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(lowercase_		)
  
   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=lowercase_		)    ,				lowercase_		)		)
  
   A__ =       all_frames[..., 5]
   A__ =       all_frames[..., 6]
   A__ =       all_frames[..., 7]
  
   A__ =       all_frames[..., 4]
   A__ =       chia_frame_to_bb.compose(lowercase_		)
   A__ =       chia_frame_to_bb.compose(lowercase_		)
   A__ =       chia_frame_to_bb.compose(lowercase_		)
  
   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(lowercase_		)
  
   return all_frames_to_global
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				)		->	torch.Tensor:
   # [*, N, 14]
   A__ =       group_idx[aatype, ...]
  
   # [*, N, 14, 8]
   A__ =       nn.functional.one_hot(
       lowercase_    ,				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 lowercase_		: torch.sum(lowercase_    ,				dim=-1		)		)
  
   # [*, N, 14, 1]
   A__ =       atom_mask[aatype, ...].unsqueeze(-1		)
  
   # [*, N, 14, 3]
   A__ =       lit_positions[aatype, ...]
   A__ =       t_atoms_to_global.apply(lowercase_		)
   A__ =       pred_positions * atom_mask
  
   return pred_positions
  
  
  
  
  
  