



'''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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   torch.Tensor:
		...



@overload
def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Tuple[torch.Tensor, torch.Tensor]:
		...



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							aatype == rc.restype_order['G']
		_a  :   Tuple				 =							rc.atom_order['CA']
		_a  :   Optional[Any]				 =							rc.atom_order['CB']
		_a  :   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:
				_a  :   Any				 =							torch.where(
				    lowerCAmelCase_		,    all_atom_masks[..., ca_idx]		,    all_atom_masks[..., cb_idx]		,    )
				return pseudo_beta, pseudo_beta_mask
		else:
				return pseudo_beta



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   torch.Tensor:
		_a  :   Optional[int]				 =							batched_gather(
		    lowerCAmelCase_		,    batch['residx_atom37_to_atom14']		,    dim=-2		,    no_batch_dims=len(atomaa.shape[:-2]						)		,    )

		_a  :   Optional[Any]				 =							atomaa_data * batch['atom37_atom_exists'][..., None]

		return atomaa_data



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   torch.Tensor:
		_a  :   Dict				 =							template_feats['template_aatype']
		_a  :   Dict				 =							template_feats['template_torsion_angles_sin_cos']
		_a  :   str				 =							template_feats['template_alt_torsion_angles_sin_cos']
		_a  :   Optional[Any]				 =							template_feats['template_torsion_angles_mask']
		_a  :   Dict				 =							torch.cat(
		    [
		        nn.functional.one_hot(lowerCAmelCase_		,    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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_ = False		,    lowerCAmelCase_ = 1E-20		,    lowerCAmelCase_ = 1E8		,    )    ->   torch.Tensor:
		_a  :   Dict				 =							batch['template_pseudo_beta_mask']
		_a  :   Optional[int]				 =							template_mask[..., None] * template_mask[..., None, :]

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

		_a  :   Optional[Any]				 =							[dgram, template_mask_ad[..., None]]

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

		_a  :   str				 =							batch['template_aatype'].shape[-1]
		to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]		,    lowerCAmelCase_		,    -1		,    -1						)						)
		to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]		,    -1		,    lowerCAmelCase_		,    -1						)						)

		_a ,      _a ,      _a  :   str				 =							[rc.atom_order[a] for a in ['N', 'CA', 'C']]
		_a  :   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=lowerCAmelCase_		,    )
		_a  :   Optional[int]				 =							rigids.get_trans()[..., None, :, :]
		_a  :   Optional[Any]				 =							rigids[..., None].invert_apply(lowerCAmelCase_						)

		_a  :   Optional[int]				 =							torch.rsqrt(eps + torch.sum(rigid_vec**2		,    dim=-1						)						)

		_a  :   str				 =							batch['template_all_atom_mask']
		_a  :   Union[str, Any]				 =							t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
		_a  :   List[Any]				 =							template_mask[..., None] * template_mask[..., None, :]

		_a  :   Any				 =							inv_distance_scalar * template_mask_ad
		_a  :   Union[str, Any]				 =							rigid_vec * inv_distance_scalar[..., None]

		if not use_unit_vector:
				_a  :   Tuple				 =							unit_vector * 0.0

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

		_a  :   Any				 =							torch.cat(lowerCAmelCase_		,    dim=-1						)
		_a  :   Tuple				 =							act * template_mask_ad[..., None]

		return act



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   torch.Tensor:
		_a  :   torch.LongTensor				 =							nn.functional.one_hot(batch['extra_msa']		,    23						)
		_a  :   Any				 =							[
		    msa_ahot,
		    batch['extra_has_deletion'].unsqueeze(-1						),
		    batch['extra_deletion_value'].unsqueeze(-1						),
		]
		return torch.cat(lowerCAmelCase_		,    dim=-1						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    )    ->   Rigid:
		# [*, N, 8, 4, 4]
		_a  :   List[Any]				 =							rrgdf[aatype, ...]

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

		_a  :   Tuple				 =							alpha.new_zeros((*((1,) * len(alpha.shape[:-1]						)), 2)						)
		_a  :   Optional[int]				 =							1

		# [*, N, 8, 2]
		_a  :   Optional[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.

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

		_a  :   Optional[int]				 =							default_r.compose(Rigid(Rotation(rot_mats=lowerCAmelCase_						)		,    lowerCAmelCase_						)						)

		_a  :   List[str]				 =							all_frames[..., 5]
		_a  :   Optional[Any]				 =							all_frames[..., 6]
		_a  :   List[Any]				 =							all_frames[..., 7]

		_a  :   Any				 =							all_frames[..., 4]
		_a  :   Union[str, Any]				 =							chia_frame_to_bb.compose(lowerCAmelCase_						)
		_a  :   Union[str, Any]				 =							chia_frame_to_bb.compose(lowerCAmelCase_						)
		_a  :   Dict				 =							chia_frame_to_bb.compose(lowerCAmelCase_						)

		_a  :   Tuple				 =							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  :   Dict				 =							r[..., None].compose(lowerCAmelCase_						)

		return all_frames_to_global



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    )    ->   torch.Tensor:
		# [*, N, 14]
		_a  :   Union[str, Any]				 =							group_idx[aatype, ...]

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

		# [*, N, 14, 8]
		_a  :   int				 =							r[..., None, :] * group_mask_one_hot

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

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

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

		return pred_positions









'''simple docstring'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									if node is None:
											return

									if path_sum == self.target:
											self.paths += 1

									if node.left:
											self.depth_first_search(node.left			,path_sum + node.left.value	)
									if node.right:
											self.depth_first_search(node.right			,path_sum + node.right.value	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   Any				 =							target

									self.depth_first_search(_UpperCAmelCase			,node.value	)
									self.path_sum(node.left	)
									self.path_sum(node.right	)

									return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()




