
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     UpperCAmelCase__							(       __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     None  ):


					'''simple docstring'''

					...
@overload
def     UpperCAmelCase__							(       __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor  ):


					'''simple docstring'''

					...
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Any							,  __magic_name__						:     Any  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     aatype == rc.restype_order['G']
					lowerCAmelCase		:					Optional[int]  	=     rc.atom_order['CA']
					lowerCAmelCase		:					int  	=     rc.atom_order['CB']
					lowerCAmelCase		:					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:
										lowerCAmelCase		:					List[Any]  	=     torch.where(
										    __magic_name__							,  all_atom_masks[..., ca_idx]							,  all_atom_masks[..., cb_idx]							,  )
										return pseudo_beta, pseudo_beta_mask
					else:
										return pseudo_beta
def     UpperCAmelCase__							(       __magic_name__						:     torch.Tensor							,  __magic_name__						:     Dict[str, torch.Tensor]  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     batched_gather(
					    __magic_name__							,  batch['''residx_atom37_to_atom14''']							,  dim=-2							,  no_batch_dims=len(atomaa.shape[:-2]  )							,  )

					lowerCAmelCase		:					Tuple  	=     atomaa_data * batch['atom37_atom_exists'][..., None]

					return atomaa_data
def     UpperCAmelCase__							(       __magic_name__						:     Dict[str, torch.Tensor]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[Any]  	=     template_feats['template_aatype']
					lowerCAmelCase		:					Union[str, Any]  	=     template_feats['template_torsion_angles_sin_cos']
					lowerCAmelCase		:					int  	=     template_feats['template_alt_torsion_angles_sin_cos']
					lowerCAmelCase		:					List[str]  	=     template_feats['template_torsion_angles_mask']
					lowerCAmelCase		:					int  	=     torch.cat(
					    [
					        nn.functional.one_hot(__magic_name__							,  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     UpperCAmelCase__							(       __magic_name__						:     Dict[str, torch.Tensor]							,  __magic_name__						:     torch.types.Number							,  __magic_name__						:     torch.types.Number							,  __magic_name__						:     int							,  __magic_name__						:     bool = False							,  __magic_name__						:     float = 1e-2_0							,  __magic_name__						:     float = 1e8							,  ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     batch['template_pseudo_beta_mask']
					lowerCAmelCase		:					List[str]  	=     template_mask[..., None] * template_mask[..., None, :]

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

					lowerCAmelCase		:					str  	=     [dgram, template_mask_ad[..., None]]

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

					lowerCAmelCase		:					List[Any]  	=     batch['template_aatype'].shape[-1]
					to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]							,  __magic_name__							,  -1							,  -1  )  )
					to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]							,  -1							,  __magic_name__							,  -1  )  )

					lowerCAmelCase		:					List[str]  	=     [rc.atom_order[a] for a in ['N', 'CA', 'C']]
					lowerCAmelCase		:					Dict  	=     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=__magic_name__							,  )
					lowerCAmelCase		:					int  	=     rigids.get_trans()[..., None, :, :]
					lowerCAmelCase		:					List[str]  	=     rigids[..., None].invert_apply(__magic_name__  )

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

					lowerCAmelCase		:					Tuple  	=     batch['template_all_atom_mask']
					lowerCAmelCase		:					Union[str, Any]  	=     t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
					lowerCAmelCase		:					Dict  	=     template_mask[..., None] * template_mask[..., None, :]

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

					if not use_unit_vector:
										lowerCAmelCase		:					Union[str, Any]  	=     unit_vector * 0.0

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

					lowerCAmelCase		:					Optional[int]  	=     torch.cat(__magic_name__							,  dim=-1  )
					lowerCAmelCase		:					Tuple  	=     act * template_mask_ad[..., None]

					return act
def     UpperCAmelCase__							(       __magic_name__						:     Dict[str, torch.Tensor]  ):


					'''simple docstring'''

					lowerCAmelCase		:					torch.LongTensor  	=     nn.functional.one_hot(batch['''extra_msa''']							,  23  )
					lowerCAmelCase		:					str  	=     [
					    msa_ahot,
					    batch['extra_has_deletion'].unsqueeze(-1  ),
					    batch['extra_deletion_value'].unsqueeze(-1  ),
					]
					return torch.cat(__magic_name__							,  dim=-1  )
def     UpperCAmelCase__							(       __magic_name__						:     Rigid							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  ):


					'''simple docstring'''

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

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

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

					# [*, N, 8, 2]
					lowerCAmelCase		:					List[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.

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

					lowerCAmelCase		:					Optional[Any]  	=     default_r.compose(Rigid(Rotation(rot_mats=__magic_name__  )							,  __magic_name__  )  )

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

					lowerCAmelCase		:					Optional[Any]  	=     all_frames[..., 4]
					lowerCAmelCase		:					Optional[int]  	=     chia_frame_to_bb.compose(__magic_name__  )
					lowerCAmelCase		:					Tuple  	=     chia_frame_to_bb.compose(__magic_name__  )
					lowerCAmelCase		:					Union[str, Any]  	=     chia_frame_to_bb.compose(__magic_name__  )

					lowerCAmelCase		:					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							,  )

					lowerCAmelCase		:					Union[str, Any]  	=     r[..., None].compose(__magic_name__  )

					return all_frames_to_global
def     UpperCAmelCase__							(       __magic_name__						:     Rigid							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  __magic_name__						:     torch.Tensor							,  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[Any]  	=     group_idx[aatype, ...]

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

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

					# [*, N, 14]
					lowerCAmelCase		:					List[Any]  	=     t_atoms_to_global.map_tensor_fn(lambda __magic_name__  : torch.sum(__magic_name__							,  dim=-1  )  )

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

					# [*, N, 14, 3]
					lowerCAmelCase		:					Dict  	=     lit_positions[aatype, ...]
					lowerCAmelCase		:					Optional[Any]  	=     t_atoms_to_global.apply(__magic_name__  )
					lowerCAmelCase		:					Union[str, Any]  	=     pred_positions * atom_mask

					return pred_positions


def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     int							,  __magic_name__						:     Dict							,  __magic_name__						:     str  ):


					'''simple docstring'''

					if start == end:
										return start if lst[start] > item else start + 1
					if start > end:
										return start

					lowerCAmelCase		:					Optional[Any]  	=     (start + end) // 2
					if lst[mid] < item:
										return binary_search(__magic_name__							,  __magic_name__							,  mid + 1							,  __magic_name__  )
					elif lst[mid] > item:
										return binary_search(__magic_name__							,  __magic_name__							,  __magic_name__							,  mid - 1  )
					else:
										return mid
def     UpperCAmelCase__							(       __magic_name__						:     List[Any]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     len(__magic_name__  )

					for index in range(1							,  __magic_name__  ):
										lowerCAmelCase		:					Optional[int]  	=     lst[index]
										lowerCAmelCase		:					Dict  	=     binary_search(__magic_name__							,  __magic_name__							,  0							,  index - 1  )
										lowerCAmelCase		:					Union[str, Any]  	=     lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

					return lst
def     UpperCAmelCase__							(       __magic_name__						:     Optional[int]							,  __magic_name__						:     List[str]  ):


					'''simple docstring'''

					if not left:
										return right

					if not right:
										return left

					if left[0] < right[0]:
										return [left[0], *merge(left[1:]							,  __magic_name__  )]

					return [right[0], *merge(__magic_name__							,  right[1:]  )]
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     len(__magic_name__  )
					lowerCAmelCase	,							lowerCAmelCase		:					Union[str, Any]  	=     [], []
					lowerCAmelCase		:					Union[str, Any]  	=     [lst[0]]
					lowerCAmelCase		:					Optional[Any]  	=     []
					lowerCAmelCase		:					List[Any]  	=     1
					while i < length:
										if lst[i] < lst[i - 1]:
															runs.append(__magic_name__  )
															lowerCAmelCase		:					Any  	=     [lst[i]]
										else:
															new_run.append(lst[i]  )
										i += 1
					runs.append(__magic_name__  )

					for run in runs:
										sorted_runs.append(insertion_sort(__magic_name__  )  )
					for run in sorted_runs:
										lowerCAmelCase		:					int  	=     merge(__magic_name__							,  __magic_name__  )

					return sorted_array
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
					lowerCAmelCase		:					Optional[Any]  	=     tim_sort(__magic_name__  )
					print(__magic_name__  )


if __name__ == "__main__":
		main()
