





"""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        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						...





@overload
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						...





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[str]     =    aatype == rc.restype_order['''G''']
						_UpperCamelCase			:					Union[str, Any]     =    rc.atom_order['''CA''']
						_UpperCamelCase			:					Optional[Any]     =    rc.atom_order['''CB''']
						_UpperCamelCase			:					Any     =    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:
												_UpperCamelCase			:					int     =    torch.where(
												    _lowercase       ,						all_atom_masks[..., ca_idx]       ,						all_atom_masks[..., cb_idx]       ,						)
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    batched_gather(
						    _lowercase       ,						batch['''residx_atom37_to_atom14''']       ,						dim=-2       ,						no_batch_dims=len(atomaa.shape[:-2]   )       ,						)

						_UpperCamelCase			:					int     =    atomaa_data * batch['''atom37_atom_exists'''][..., None]

						return atomaa_data





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Any     =    template_feats['''template_aatype''']
						_UpperCamelCase			:					Tuple     =    template_feats['''template_torsion_angles_sin_cos''']
						_UpperCamelCase			:					Optional[int]     =    template_feats['''template_alt_torsion_angles_sin_cos''']
						_UpperCamelCase			:					Dict     =    template_feats['''template_torsion_angles_mask''']
						_UpperCamelCase			:					Tuple     =    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        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase = False       ,						_lowercase = 1E-20       ,						_lowercase = 1E8       ,						):
						_UpperCamelCase			:					List[str]     =    batch['''template_pseudo_beta_mask''']
						_UpperCamelCase			:					Union[str, Any]     =    template_mask[..., None] * template_mask[..., None, :]

						# Compute distogram (this seems to differ slightly from Alg. 5)
						_UpperCamelCase			:					str     =    batch['''template_pseudo_beta''']
						_UpperCamelCase			:					Union[str, Any]     =    torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2       ,						dim=-1       ,						keepdim=_lowercase   )
						_UpperCamelCase			:					List[str]     =    torch.linspace(_lowercase       ,						_lowercase       ,						_lowercase       ,						device=tpb.device   ) ** 2
						_UpperCamelCase			:					Dict     =    torch.cat([lower[1:], lower.new_tensor([inf]   )]       ,						dim=-1   )
						_UpperCamelCase			:					Union[str, Any]     =    ((dgram > lower) * (dgram < upper)).type(dgram.dtype   )

						_UpperCamelCase			:					List[Any]     =    [dgram, template_mask_ad[..., None]]

						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(
						    batch['''template_aatype''']       ,						rc.restype_num + 2       ,						)

						_UpperCamelCase			:					Tuple     =    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   )   )

						_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					int     =    [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
						_UpperCamelCase			:					int     =    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       ,						)
						_UpperCamelCase			:					int     =    rigids.get_trans()[..., None, :, :]
						_UpperCamelCase			:					Optional[int]     =    rigids[..., None].invert_apply(_lowercase   )

						_UpperCamelCase			:					Union[str, Any]     =    torch.rsqrt(eps + torch.sum(rigid_vec**2       ,						dim=-1   )   )

						_UpperCamelCase			:					Tuple     =    batch['''template_all_atom_mask''']
						_UpperCamelCase			:					List[Any]     =    t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						_UpperCamelCase			:					Optional[int]     =    template_mask[..., None] * template_mask[..., None, :]

						_UpperCamelCase			:					Union[str, Any]     =    inv_distance_scalar * template_mask_ad
						_UpperCamelCase			:					Optional[int]     =    rigid_vec * inv_distance_scalar[..., None]

						if not use_unit_vector:
												_UpperCamelCase			:					List[str]     =    unit_vector * 0.0

						to_concat.extend(torch.unbind(unit_vector[..., None, :]       ,						dim=-1   )   )
						to_concat.append(template_mask_ad[..., None]   )

						_UpperCamelCase			:					Dict     =    torch.cat(_lowercase       ,						dim=-1   )
						_UpperCamelCase			:					List[Any]     =    act * template_mask_ad[..., None]

						return act





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(batch['''extra_msa''']       ,						23   )
						_UpperCamelCase			:					Dict     =    [
						    msa_ahot,
						    batch['''extra_has_deletion'''].unsqueeze(-1   ),
						    batch['''extra_deletion_value'''].unsqueeze(-1   ),
						]
						return torch.cat(_lowercase       ,						dim=-1   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						):
						# [*, N, 8, 4, 4]
						_UpperCamelCase			:					Any     =    rrgdf[aatype, ...]

						# [*, N, 8] transformations, i.e.
						#   One [*, N, 8, 3, 3] rotation matrix and
						#   One [*, N, 8, 3]    translation matrix
						_UpperCamelCase			:					List[str]     =    r.from_tensor_axa(_lowercase   )

						_UpperCamelCase			:					List[Any]     =    alpha.new_zeros((*((1,) * len(alpha.shape[:-1]   )), 2)   )
						_UpperCamelCase			:					Dict     =    1

						# [*, N, 8, 2]
						_UpperCamelCase			:					Optional[int]     =    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.

						_UpperCamelCase			:					Optional[int]     =    alpha.new_zeros(default_r.get_rots().get_rot_mats().shape   )
						_UpperCamelCase			:					Optional[int]     =    1
						_UpperCamelCase			:					List[str]     =    alpha[..., 1]
						_UpperCamelCase			:					int     =    -alpha[..., 0]
						_UpperCamelCase			:					Any     =    alpha

						_UpperCamelCase			:					List[Any]     =    default_r.compose(Rigid(Rotation(rot_mats=_lowercase   )       ,						_lowercase   )   )

						_UpperCamelCase			:					Union[str, Any]     =    all_frames[..., 5]
						_UpperCamelCase			:					Any     =    all_frames[..., 6]
						_UpperCamelCase			:					Union[str, Any]     =    all_frames[..., 7]

						_UpperCamelCase			:					Optional[int]     =    all_frames[..., 4]
						_UpperCamelCase			:					Optional[int]     =    chia_frame_to_bb.compose(_lowercase   )
						_UpperCamelCase			:					int     =    chia_frame_to_bb.compose(_lowercase   )
						_UpperCamelCase			:					List[Any]     =    chia_frame_to_bb.compose(_lowercase   )

						_UpperCamelCase			:					Any     =    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       ,						)

						_UpperCamelCase			:					List[Any]     =    r[..., None].compose(_lowercase   )

						return all_frames_to_global






def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						):
						# [*, N, 14]
						_UpperCamelCase			:					List[Any]     =    group_idx[aatype, ...]

						# [*, N, 14, 8]
						_UpperCamelCase			:					torch.LongTensor     =    nn.functional.one_hot(
						    _lowercase       ,						num_classes=default_frames.shape[-3]       ,						)

						# [*, N, 14, 8]
						_UpperCamelCase			:					Dict     =    r[..., None, :] * group_mask_one_hot

						# [*, N, 14]
						_UpperCamelCase			:					Tuple     =    t_atoms_to_global.map_tensor_fn(lambda _lowercase   : torch.sum(_lowercase       ,						dim=-1   )   )

						# [*, N, 14, 1]
						_UpperCamelCase			:					str     =    atom_mask[aatype, ...].unsqueeze(-1   )

						# [*, N, 14, 3]
						_UpperCamelCase			:					List[str]     =    lit_positions[aatype, ...]
						_UpperCamelCase			:					Optional[int]     =    t_atoms_to_global.apply(_lowercase   )
						_UpperCamelCase			:					Optional[Any]     =    pred_positions * atom_mask

						return pred_positions




from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




