'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : None							):
     ...





@overload
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor							):
     ...





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict      ,		__snake_case       : Dict							):
     _A								=     aatype == rc.restype_order['G']
     _A								=     rc.atom_order['CA']
     _A								=     rc.atom_order['CB']
     _A								=     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								=     torch.where(
              __snake_case      ,		all_atom_masks[..., ca_idx]      ,		all_atom_masks[..., cb_idx]      ,		)
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : torch.Tensor      ,		__snake_case       : Dict[str, torch.Tensor]							):
     _A								=     batched_gather(
         __snake_case      ,		batch['residx_atom37_to_atom14']      ,		dim=-2      ,		no_batch_dims=len(atomaa.shape[:-2]							)      ,		)

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

     return atomaa_data





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict[str, torch.Tensor]							):
     _A								=     template_feats['template_aatype']
     _A								=     template_feats['template_torsion_angles_sin_cos']
     _A								=     template_feats['template_alt_torsion_angles_sin_cos']
     _A								=     template_feats['template_torsion_angles_mask']
     _A								=     torch.cat(
         [
             nn.functional.one_hot(__snake_case      ,		2_2							),
             torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]      ,		1_4							),
             alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]      ,		1_4							),
             torsion_angles_mask,
         ]      ,		dim=-1      ,		)

     return template_angle_feat





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict[str, torch.Tensor]      ,		__snake_case       : torch.types.Number      ,		__snake_case       : torch.types.Number      ,		__snake_case       : int      ,		__snake_case       : bool = False      ,		__snake_case       : float = 1e-20      ,		__snake_case       : float = 1e8      ,		):
     _A								=     batch['template_pseudo_beta_mask']
     _A								=     template_mask[..., None] * template_mask[..., None, :]

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

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

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

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

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

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

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

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

     if not use_unit_vector:
          _A								=     unit_vector * 0.0

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

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

     return act





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict[str, torch.Tensor]							):
     _A								=     nn.functional.one_hot(batch['extra_msa']      ,		2_3							)
     _A								=     [
         msa_ahot,
         batch['extra_has_deletion'].unsqueeze(-1							),
         batch['extra_deletion_value'].unsqueeze(-1							),
     ]
     return torch.cat(__snake_case      ,		dim=-1							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Rigid      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		):
     # [*, N, 8, 4, 4]
     _A								=     rrgdf[aatype, ...]

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

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

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

     _A								=     default_r.compose(Rigid(Rotation(rot_mats=__snake_case							)      ,		__snake_case							)							)

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

     _A								=     all_frames[..., 4]
     _A								=     chia_frame_to_bb.compose(__snake_case							)
     _A								=     chia_frame_to_bb.compose(__snake_case							)
     _A								=     chia_frame_to_bb.compose(__snake_case							)

     _A								=     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								=     r[..., None].compose(__snake_case							)

     return all_frames_to_global





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Rigid      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		__snake_case       : torch.Tensor      ,		):
     # [*, N, 14]
     _A								=     group_idx[aatype, ...]

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

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

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

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

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

     return pred_positions




'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     assert dataset.num_rows == 4
     assert dataset.num_columns == 3
     assert dataset.column_names == ["col_1", "col_2", "col_3"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : List[str]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader(__snake_case      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : str      ,		__snake_case       : int							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		split=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     for split in splits:
          _A								=     dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 3
          assert dataset.column_names == ["col_1", "col_2", "col_3"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]      ,		__snake_case       : str							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader({'train': csv_path}      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : int							):
     if split:
          _A								=     {split: csv_path}
     else:
          _A								=     {'train': csv_path, 'test': csv_path}
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)



