

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_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):

						'''simple docstring'''





						...







@overload
def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):

						'''simple docstring'''





						...







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				int							= aatype == rc.restype_order["G"]
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= rc.atom_order["CA"]
						SCREAMING_SNAKE_CASE_:				List[str]							= rc.atom_order["CB"]
						SCREAMING_SNAKE_CASE_:				List[str]							= 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:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.where(
												    _UpperCAmelCase       ,			all_atom_masks[..., ca_idx]       ,			all_atom_masks[..., cb_idx]       ,			)
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				str							= batched_gather(
						    _UpperCAmelCase       ,			batch["residx_atom37_to_atom14"]       ,			dim=-2       ,			no_batch_dims=len(atomaa.shape[:-2]					)       ,			)

						SCREAMING_SNAKE_CASE_:				Any							= atomaa_data * batch["atom37_atom_exists"][..., None]

						return atomaa_data







def     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Optional[Any]							= template_feats["template_aatype"]
						SCREAMING_SNAKE_CASE_:				Dict							= template_feats["template_torsion_angles_sin_cos"]
						SCREAMING_SNAKE_CASE_:				str							= template_feats["template_alt_torsion_angles_sin_cos"]
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= template_feats["template_torsion_angles_mask"]
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.cat(
						    [
						        nn.functional.one_hot(_UpperCAmelCase       ,			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_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = False       ,			_UpperCAmelCase = 1e-20       ,			_UpperCAmelCase = 1e8       ,			):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				int							= batch["template_pseudo_beta_mask"]
						SCREAMING_SNAKE_CASE_:				Any							= template_mask[..., None] * template_mask[..., None, :]

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

						SCREAMING_SNAKE_CASE_:				int							= [dgram, template_mask_ad[..., None]]

						SCREAMING_SNAKE_CASE_:				Optional[Any]							= nn.functional.one_hot(
						    batch["template_aatype"]       ,			rc.restype_num + 2       ,			)

						SCREAMING_SNAKE_CASE_:				List[str]							= batch["template_aatype"].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]       ,			_UpperCAmelCase       ,			-1       ,			-1					)					)
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]       ,			-1       ,			_UpperCAmelCase       ,			-1					)					)

						SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Tuple							= [rc.atom_order[a] for a in ["N", "CA", "C"]]
						SCREAMING_SNAKE_CASE_:				Optional[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=_UpperCAmelCase       ,			)
						SCREAMING_SNAKE_CASE_:				Any							= rigids.get_trans()[..., None, :, :]
						SCREAMING_SNAKE_CASE_:				List[str]							= rigids[..., None].invert_apply(_UpperCAmelCase					)

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

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

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

						if not use_unit_vector:
												SCREAMING_SNAKE_CASE_:				str							= unit_vector * 0.0

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

						SCREAMING_SNAKE_CASE_:				str							= torch.cat(_UpperCAmelCase       ,			dim=-1					)
						SCREAMING_SNAKE_CASE_:				int							= act * template_mask_ad[..., None]

						return act







def     A_   (					_UpperCAmelCase					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				List[Any]							= nn.functional.one_hot(batch["extra_msa"]       ,			23					)
						SCREAMING_SNAKE_CASE_:				Dict							= [
						    msa_ahot,
						    batch["extra_has_deletion"].unsqueeze(-1					),
						    batch["extra_deletion_value"].unsqueeze(-1					),
						]
						return torch.cat(_UpperCAmelCase       ,			dim=-1					)







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Optional[int]							= rrgdf[aatype, ...]

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

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

						# [*, N, 8, 2]
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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.

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

						SCREAMING_SNAKE_CASE_:				List[str]							= default_r.compose(Rigid(Rotation(rot_mats=_UpperCAmelCase					)       ,			_UpperCAmelCase					)					)

						SCREAMING_SNAKE_CASE_:				int							= all_frames[..., 5]
						SCREAMING_SNAKE_CASE_:				int							= all_frames[..., 6]
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= all_frames[..., 7]

						SCREAMING_SNAKE_CASE_:				int							= all_frames[..., 4]
						SCREAMING_SNAKE_CASE_:				List[Any]							= chia_frame_to_bb.compose(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				str							= chia_frame_to_bb.compose(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= chia_frame_to_bb.compose(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				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       ,			)

						SCREAMING_SNAKE_CASE_:				Any							= r[..., None].compose(_UpperCAmelCase					)

						return all_frames_to_global







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				Any							= group_idx[aatype, ...]

						# [*, N, 14, 8]
						SCREAMING_SNAKE_CASE_:				List[str]							= nn.functional.one_hot(
						    _UpperCAmelCase       ,			num_classes=default_frames.shape[-3]       ,			)

						# [*, N, 14, 8]
						SCREAMING_SNAKE_CASE_:				Any							= r[..., None, :] * group_mask_one_hot

						# [*, N, 14]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= t_atoms_to_global.map_tensor_fn(lambda _UpperCAmelCase					: torch.sum(_UpperCAmelCase       ,			dim=-1					)					)

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

						# [*, N, 14, 3]
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= lit_positions[aatype, ...]
						SCREAMING_SNAKE_CASE_:				Tuple							= t_atoms_to_global.apply(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				int							= pred_positions * atom_mask

						return pred_positions









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






