
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      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict[str, torch.Tensor]       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					[]
							lowerCamelCase_							  =					[]
							lowerCamelCase_							  =					[]

							for rt in rc.restypes:
														lowerCamelCase_							  =					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_							  =					{name: i for i, name in enumerate(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] * 1_4       )
							restype_atomaa_to_atomaa_list.append([0] * 3_7       )
							restype_atomaa_mask_list.append([0.0] * 1_4       )

							lowerCamelCase_							  =					torch.tensor(
							    snake_case_			,				dtype=torch.intaa			,				device=protein["""aatype"""].device			,				)
							lowerCamelCase_							  =					torch.tensor(
							    snake_case_			,				dtype=torch.intaa			,				device=protein["""aatype"""].device			,				)
							lowerCamelCase_							  =					torch.tensor(
							    snake_case_			,				dtype=torch.floataa			,				device=protein["""aatype"""].device			,				)
							lowerCamelCase_							  =					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_							  =					restype_atomaa_to_atomaa[protein_aatype]
							lowerCamelCase_							  =					restype_atomaa_mask[protein_aatype]

							lowerCamelCase_							  =					residx_atomaa_mask
							lowerCamelCase_							  =					residx_atomaa_to_atomaa.long()

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

							# create the corresponding mask
							lowerCamelCase_							  =					torch.zeros([2_1, 3_7]			,				dtype=torch.floataa			,				device=protein["""aatype"""].device       )
							for restype, restype_letter in enumerate(rc.restypes       ):
														lowerCamelCase_							  =					rc.restype_atoa[restype_letter]
														lowerCamelCase_							  =					rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					lowerCamelCase_							  =					rc.atom_order[atom_name]
																					lowerCamelCase_							  =					1

							lowerCamelCase_							  =					restype_atomaa_mask[protein_aatype]
							lowerCamelCase_							  =					residx_atomaa_mask

							return protein



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict[str, torch.Tensor]       )  ->    Dict:
							lowerCamelCase_							  =					tree_map(lambda UpperCAmelCase__       : torch.tensor(snake_case_			,				device=batch["""aatype"""].device       )			,				snake_case_			,				np.ndarray       )
							lowerCamelCase_							  =					tensor_tree_map(lambda UpperCAmelCase__       : np.array(snake_case_       )			,				make_atomaa_masks(snake_case_       )       )
							return out







from abc import ABC, abstractmethod
from argparse import ArgumentParser





class       __A(				UpperCAmelCase       ):

					@staticmethod
					@abstractmethod
					def    lowercase__      (	__UpperCamelCase					:     ArgumentParser   ):
												raise NotImplementedError()



					@abstractmethod
					def    lowercase__      (	self					:     Any   ):
												raise NotImplementedError()





