code: 
  
  
  """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__   :Optional[int], lowercase__   :str, lowercase__   :Any    ):
  			...
  
  
  
  
  
  @overload
  def 			a_   (  lowercase__   :Any, lowercase__   :Dict, lowercase__   :str    ):
  			...
  
  
  
  
  
  def 			a_   (  lowercase__   :Union[str, Any], lowercase__   :Dict, lowercase__   :Tuple    ):
  			__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(
  						    UpperCamelCase_, all_atom_masks[..., ca_idx], all_atom_masks[..., cb_idx], )
  						return pseudo_beta, pseudo_beta_mask
  			else:
  						return pseudo_beta
  
  
  
  
  
  def 			a_   (  lowercase__   :List[Any], lowercase__   :List[str]    ):
  			__lowerCamelCase        =			batched_gather(
  			    UpperCamelCase_, 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__   :List[Any]    ):
  			__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(UpperCamelCase_, 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__   :List[str], lowercase__   :int, lowercase__   :Optional[int], lowercase__   :List[Any], lowercase__   :List[str] = False, lowercase__   :Dict = 1e-20, lowercase__   :Dict = 1e8, ):
  			__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=UpperCamelCase_    )
  			__lowerCamelCase        =			torch.linspace(UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, 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], UpperCamelCase_, -1, -1    )    )
  			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, UpperCamelCase_, -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=UpperCamelCase_, )
  			__lowerCamelCase        =			rigids.get_trans()[..., None, :, :]
  			__lowerCamelCase        =			rigids[..., None].invert_apply(UpperCamelCase_    )
  
  			__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(UpperCamelCase_, dim=-1    )
  			__lowerCamelCase        =			act * template_mask_ad[..., None]
  
  			return act
  
  
  
  
  
  def 			a_   (  lowercase__   :Union[str, Any]    ):
  			__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(UpperCamelCase_, dim=-1    )
  
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :str, lowercase__   :Optional[int], lowercase__   :List[str], ):
  			__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(UpperCamelCase_    )
  
  			__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=UpperCamelCase_    ), UpperCamelCase_    )    )
  
  			__lowerCamelCase        =			all_frames[..., 5]
  			__lowerCamelCase        =			all_frames[..., 6]
  			__lowerCamelCase        =			all_frames[..., 7]
  
  			__lowerCamelCase        =			all_frames[..., 4]
  			__lowerCamelCase        =			chia_frame_to_bb.compose(UpperCamelCase_    )
  			__lowerCamelCase        =			chia_frame_to_bb.compose(UpperCamelCase_    )
  			__lowerCamelCase        =			chia_frame_to_bb.compose(UpperCamelCase_    )
  
  			__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(UpperCamelCase_    )
  
  			return all_frames_to_global
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[int], lowercase__   :Optional[int], lowercase__   :Any, lowercase__   :Any, lowercase__   :Tuple, lowercase__   :Any, ):
  			__lowerCamelCase        =			group_idx[aatype, ...]
  
  			# [*, N, 14, 8]
  			__lowerCamelCase        =			nn.functional.one_hot(
  			    UpperCamelCase_, 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(UpperCamelCase_, dim=-1    )    )
  
  			# [*, N, 14, 1]
  			__lowerCamelCase        =			atom_mask[aatype, ...].unsqueeze(-1    )
  
  			# [*, N, 14, 3]
  			__lowerCamelCase        =			lit_positions[aatype, ...]
  			__lowerCamelCase        =			t_atoms_to_global.apply(UpperCamelCase_    )
  			__lowerCamelCase        =			pred_positions * atom_mask
  
  			return pred_positions
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  