
"""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:


					'''simple docstring'''
					...


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


					'''simple docstring'''
					...


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Any:


					'''simple docstring'''
					__a:						str		    =					aatype == rc.restype_order["""G"""]
					__a:						Optional[Any]		    =					rc.atom_order["""CA"""]
					__a:						List[Any]		    =					rc.atom_order["""CB"""]
					__a:						List[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:
										__a:						str		    =					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:


					'''simple docstring'''
					__a:						Optional[Any]		    =					batched_gather(
					    lowerCamelCase_	,				batch["""residx_atom37_to_atom14"""]	,				dim=-2	,				no_batch_dims=len(atomaa.shape[:-2] )	,				)

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

					return atomaa_data


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			torch.Tensor:


					'''simple docstring'''
					__a:						str		    =					template_feats["""template_aatype"""]
					__a:						Tuple		    =					template_feats["""template_torsion_angles_sin_cos"""]
					__a:						int		    =					template_feats["""template_alt_torsion_angles_sin_cos"""]
					__a:						str		    =					template_feats["""template_torsion_angles_mask"""]
					__a:						Optional[Any]		    =					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:


					'''simple docstring'''
					__a:						Optional[Any]		    =					batch["""template_pseudo_beta_mask"""]
					__a:						Any		    =					template_mask[..., None] * template_mask[..., None, :]

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

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

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

					__a:						Optional[int]		    =					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:						List[str]		    =					[rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
					__a:						str		    =					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:						str		    =					rigids.get_trans()[..., None, :, :]
					__a:						Dict		    =					rigids[..., None].invert_apply(lowerCamelCase_ )

					__a:						int		    =					torch.rsqrt(eps + torch.sum(rigid_vec**2	,				dim=-1 ) )

					__a:						Optional[Any]		    =					batch["""template_all_atom_mask"""]
					__a:						List[str]		    =					t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
					__a:						Dict		    =					template_mask[..., None] * template_mask[..., None, :]

					__a:						str		    =					inv_distance_scalar * template_mask_ad
					__a:						List[str]		    =					rigid_vec * inv_distance_scalar[..., None]

					if not use_unit_vector:
										__a:						Optional[int]		    =					unit_vector * 0.0

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

					__a:						List[str]		    =					torch.cat(lowerCamelCase_	,				dim=-1 )
					__a:						Dict		    =					act * template_mask_ad[..., None]

					return act


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			torch.Tensor:


					'''simple docstring'''
					__a:						Optional[Any]		    =					nn.functional.one_hot(batch["""extra_msa"""]	,				23 )
					__a:						Optional[int]		    =					[
					    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:


					'''simple docstring'''
					__a:						str		    =					rrgdf[aatype, ...]

					# [*, N, 8] transformations, i.e.
					#   One [*, N, 8, 3, 3] rotation matrix and
					#   One [*, N, 8, 3]    translation matrix
					__a:						Tuple		    =					r.from_tensor_axa(lowerCamelCase_ )

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

					# [*, N, 8, 2]
					__a:						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.

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

					__a:						Any		    =					default_r.compose(Rigid(Rotation(rot_mats=lowerCamelCase_ )	,				lowerCamelCase_ ) )

					__a:						Optional[Any]		    =					all_frames[..., 5]
					__a:						Optional[Any]		    =					all_frames[..., 6]
					__a:						Tuple		    =					all_frames[..., 7]

					__a:						Tuple		    =					all_frames[..., 4]
					__a:						Optional[Any]		    =					chia_frame_to_bb.compose(lowerCamelCase_ )
					__a:						Tuple		    =					chia_frame_to_bb.compose(lowerCamelCase_ )
					__a:						Tuple		    =					chia_frame_to_bb.compose(lowerCamelCase_ )

					__a:						List[str]		    =					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:						Optional[int]		    =					r[..., None].compose(lowerCamelCase_ )

					return all_frames_to_global




def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				)     ->			torch.Tensor:


					'''simple docstring'''
					__a:						List[str]		    =					group_idx[aatype, ...]

					# [*, N, 14, 8]
					__a:						int		    =					nn.functional.one_hot(
					    lowerCamelCase_	,				num_classes=default_frames.shape[-3]	,				)

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

					# [*, N, 14]
					__a:						Optional[Any]		    =					t_atoms_to_global.map_tensor_fn(lambda lowerCamelCase_ : torch.sum(lowerCamelCase_	,				dim=-1 ) )

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

					# [*, N, 14, 3]
					__a:						List[Any]		    =					lit_positions[aatype, ...]
					__a:						Any		    =					t_atoms_to_global.apply(lowerCamelCase_ )
					__a:						Any		    =					pred_positions * atom_mask

					return pred_positions


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
