"""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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	None						)  ->							torch.Tensor:
   ...



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



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



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					batched_gather(
       lowercase_      , batch['residx_atom37_to_atom14']      , dim=-2      , no_batch_dims=len(atomaa.shape[:-2]						)      , )

   __lowerCamelCase    		=					atomaa_data * batch['atom37_atom_exists'][..., None]

   return atomaa_data



def       A__    (	lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					template_feats['template_aatype']
   __lowerCamelCase    		=					template_feats['template_torsion_angles_sin_cos']
   __lowerCamelCase    		=					template_feats['template_alt_torsion_angles_sin_cos']
   __lowerCamelCase    		=					template_feats['template_torsion_angles_mask']
   __lowerCamelCase    		=					torch.cat(
       [
           nn.functional.one_hot(lowercase_      , 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__    (	lowercase_     :	Dict[str, torch.Tensor]      , lowercase_     :	torch.types.Number      , lowercase_     :	torch.types.Number      , lowercase_     :	int      , lowercase_     :	bool = False      , lowercase_     :	float = 1e-20      , lowercase_     :	float = 1e8      , )  ->							torch.Tensor:
   __lowerCamelCase    		=					batch['template_pseudo_beta_mask']
   __lowerCamelCase    		=					template_mask[..., None] * template_mask[..., None, :]

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

   __lowerCamelCase    		=					[dgram, template_mask_ad[..., None]]

   __lowerCamelCase    		=					nn.functional.one_hot(
       batch['template_aatype']      , rc.restype_num + 2      , )

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

   __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					[rc.atom_order[a] for a in ['N', 'CA', 'C']]
   __lowerCamelCase    		=					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=lowercase_      , )
   __lowerCamelCase    		=					rigids.get_trans()[..., None, :, :]
   __lowerCamelCase    		=					rigids[..., None].invert_apply(lowercase_						)

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

   __lowerCamelCase    		=					batch['template_all_atom_mask']
   __lowerCamelCase    		=					t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   __lowerCamelCase    		=					template_mask[..., None] * template_mask[..., None, :]

   __lowerCamelCase    		=					inv_distance_scalar * template_mask_ad
   __lowerCamelCase    		=					rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      __lowerCamelCase    		=					unit_vector * 0.0

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

   __lowerCamelCase    		=					torch.cat(lowercase_      , dim=-1						)
   __lowerCamelCase    		=					act * template_mask_ad[..., None]

   return act



def       A__    (	lowercase_     :	Dict[str, torch.Tensor]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					nn.functional.one_hot(batch['extra_msa']      , 23						)
   __lowerCamelCase    		=					[
       msa_ahot,
       batch['extra_has_deletion'].unsqueeze(-1						),
       batch['extra_deletion_value'].unsqueeze(-1						),
   ]
   return torch.cat(lowercase_      , dim=-1						)



def       A__    (	lowercase_     :	Rigid      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , )  ->							Rigid:
   # [*, N, 8, 4, 4]
   __lowerCamelCase    		=					rrgdf[aatype, ...]

   # [*, N, 8] transformations, i.e.
   #   One [*, N, 8, 3, 3] rotation matrix and
   #   One [*, N, 8, 3]    translation matrix
   __lowerCamelCase    		=					r.from_tensor_axa(lowercase_						)

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

   # [*, N, 8, 2]
   __lowerCamelCase    		=					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    		=					alpha.new_zeros(default_r.get_rots().get_rot_mats().shape						)
   __lowerCamelCase    		=					1
   __lowerCamelCase    		=					alpha[..., 1]
   __lowerCamelCase    		=					-alpha[..., 0]
   __lowerCamelCase    		=					alpha

   __lowerCamelCase    		=					default_r.compose(Rigid(Rotation(rot_mats=lowercase_						)      , lowercase_						)						)

   __lowerCamelCase    		=					all_frames[..., 5]
   __lowerCamelCase    		=					all_frames[..., 6]
   __lowerCamelCase    		=					all_frames[..., 7]

   __lowerCamelCase    		=					all_frames[..., 4]
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)
   __lowerCamelCase    		=					chia_frame_to_bb.compose(lowercase_						)

   __lowerCamelCase    		=					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    		=					r[..., None].compose(lowercase_						)

   return all_frames_to_global



def       A__    (	lowercase_     :	Rigid      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , )  ->							torch.Tensor:
   # [*, N, 14]
   __lowerCamelCase    		=					group_idx[aatype, ...]

   # [*, N, 14, 8]
   __lowerCamelCase    		=					nn.functional.one_hot(
       lowercase_      , num_classes=default_frames.shape[-3]      , )

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

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

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

   # [*, N, 14, 3]
   __lowerCamelCase    		=					lit_positions[aatype, ...]
   __lowerCamelCase    		=					t_atoms_to_global.apply(lowercase_						)
   __lowerCamelCase    		=					pred_positions * atom_mask

   return pred_positions







'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				Union[str, Any]														= """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,				_KWARGS_DESCRIPTION     )
class    lowerCamelCase__  (   datasets.Metric     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        return datasets.MetricInfo(
            description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(self._get_feature_types()     )					,      reference_urls=[
                """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
            ]					,      )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        if self.config_name == "multilist":
             return {
                 "predictions": datasets.Sequence(datasets.Value("""float"""     )     ),
                 "references": datasets.Sequence(datasets.Value("""float"""     )     ),
             }
        else:
             return {
                 "predictions": datasets.Value("""float"""     ),
                 "references": datasets.Value("""float"""     ),
             }




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}