

'''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					...





@overload
def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					...





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							int        =       aatype == rc.restype_order["G"]
					UpperCAmelCase		:							List[Any]        =       rc.atom_order["CA"]
					UpperCAmelCase		:							Optional[Any]        =       rc.atom_order["CB"]
					UpperCAmelCase		:							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:
										UpperCAmelCase		:							List[str]        =       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 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]        =       batched_gather(
					    __magic_name__			,   batch["residx_atom37_to_atom14"]			,   dim=-2			,   no_batch_dims=len(atomaa.shape[:-2]							)			,   )

					UpperCAmelCase		:							Tuple        =       atomaa_data * batch["atom37_atom_exists"][..., None]

					return atomaa_data





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							str        =       template_feats["template_aatype"]
					UpperCAmelCase		:							List[str]        =       template_feats["template_torsion_angles_sin_cos"]
					UpperCAmelCase		:							int        =       template_feats["template_alt_torsion_angles_sin_cos"]
					UpperCAmelCase		:							Tuple        =       template_feats["template_torsion_angles_mask"]
					UpperCAmelCase		:							Optional[Any]        =       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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__ = False			,   __magic_name__ = 1e-20			,   __magic_name__ = 1e8			,   ):



					'''simple docstring'''







					UpperCAmelCase		:							int        =       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		:							Optional[Any]        =       batch["template_pseudo_beta"]
					UpperCAmelCase		:							Optional[int]        =       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2			,   dim=-1			,   keepdim=__magic_name__							)
					UpperCAmelCase		:							List[str]        =       torch.linspace(__magic_name__			,   __magic_name__			,   __magic_name__			,   device=tpb.device							) ** 2
					UpperCAmelCase		:							Optional[int]        =       torch.cat([lower[1:], lower.new_tensor([inf]							)]			,   dim=-1							)
					UpperCAmelCase		:							Optional[Any]        =       ((dgram > lower) * (dgram < upper)).type(dgram.dtype							)

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

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

					UpperCAmelCase		:							str        =       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							)							)

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

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

					UpperCAmelCase		:							int        =       batch["template_all_atom_mask"]
					UpperCAmelCase		:							Any        =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
					UpperCAmelCase		:							str        =       template_mask[..., None] * template_mask[..., None, :]

					UpperCAmelCase		:							str        =       inv_distance_scalar * template_mask_ad
					UpperCAmelCase		:							int        =       rigid_vec * inv_distance_scalar[..., None]

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

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

					UpperCAmelCase		:							List[Any]        =       torch.cat(__magic_name__			,   dim=-1							)
					UpperCAmelCase		:							int        =       act * template_mask_ad[..., None]

					return act





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							torch.LongTensor        =       nn.functional.one_hot(batch["extra_msa"]			,   23							)
					UpperCAmelCase		:							int        =       [
					    msa_ahot,
					    batch["extra_has_deletion"].unsqueeze(-1							),
					    batch["extra_deletion_value"].unsqueeze(-1							),
					]
					return torch.cat(__magic_name__			,   dim=-1							)





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   ):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]        =       rrgdf[aatype, ...]

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

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

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

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

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

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

					UpperCAmelCase		:							List[str]        =       all_frames[..., 4]
					UpperCAmelCase		:							Any        =       chia_frame_to_bb.compose(__magic_name__							)
					UpperCAmelCase		:							Tuple        =       chia_frame_to_bb.compose(__magic_name__							)
					UpperCAmelCase		:							Union[str, Any]        =       chia_frame_to_bb.compose(__magic_name__							)

					UpperCAmelCase		:							Optional[int]        =       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[str]        =       r[..., None].compose(__magic_name__							)

					return all_frames_to_global





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   ):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       group_idx[aatype, ...]

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

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

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

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

					# [*, N, 14, 3]
					UpperCAmelCase		:							str        =       lit_positions[aatype, ...]
					UpperCAmelCase		:							Optional[int]        =       t_atoms_to_global.apply(__magic_name__							)
					UpperCAmelCase		:							int        =       pred_positions * atom_mask

					return pred_positions



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
