
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        A__					(						lowerCamelCase						)  ->	Dict[str, torch.Tensor]:
				UpperCamelCase_:							str	=						[]
				UpperCamelCase_:							Optional[int]	=						[]
				UpperCamelCase_:							List[str]	=						[]

				for rt in rc.restypes:
								UpperCamelCase_:							Any	=						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]						)
								UpperCamelCase_:							Any	=						{name: i for i, name in enumerate(lowerCamelCase						)}
								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						)

				UpperCamelCase_:							Dict	=						torch.tensor(
				    lowerCamelCase     ,    dtype=torch.intaa     ,    device=protein["""aatype"""].device     ,    )
				UpperCamelCase_:							Union[str, Any]	=						torch.tensor(
				    lowerCamelCase     ,    dtype=torch.intaa     ,    device=protein["""aatype"""].device     ,    )
				UpperCamelCase_:							List[Any]	=						torch.tensor(
				    lowerCamelCase     ,    dtype=torch.floataa     ,    device=protein["""aatype"""].device     ,    )
				UpperCamelCase_:							int	=						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
				UpperCamelCase_:							Union[str, Any]	=						restype_atomaa_to_atomaa[protein_aatype]
				UpperCamelCase_:							Union[str, Any]	=						restype_atomaa_mask[protein_aatype]

				UpperCamelCase_:							str	=						residx_atomaa_mask
				UpperCamelCase_:							List[str]	=						residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				UpperCamelCase_:							Optional[Any]	=						restype_atomaa_to_atomaa[protein_aatype]
				UpperCamelCase_:							Tuple	=						residx_atomaa_to_atomaa.long()

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

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

				return protein







def        A__					(						lowerCamelCase						)  ->	Dict[str, np.ndarray]:
				UpperCamelCase_:							Optional[Any]	=						tree_map(lambda lowerCamelCase						: torch.tensor(lowerCamelCase     ,    device=batch["""aatype"""].device						)     ,    lowerCamelCase     ,    np.ndarray						)
				UpperCamelCase_:							Dict	=						tensor_tree_map(lambda lowerCamelCase						: np.array(lowerCamelCase						)     ,    make_atomaa_masks(lowerCamelCase						)						)
				return out








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






