code: 
  
  
  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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       ...
  @overload
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       ...
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       aatype == rc.restype_order['''G''']
       snake_case					=       rc.atom_order['''CA''']
       snake_case					=       rc.atom_order['''CB''']
       snake_case					=       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:
            snake_case					=       torch.where(
                UpperCamelCase_							,all_atom_masks[..., ca_idx]							,all_atom_masks[..., cb_idx]							,)
            return pseudo_beta, pseudo_beta_mask
       else:
            return pseudo_beta
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       batched_gather(
           UpperCamelCase_							,batch['''residx_atom37_to_atom14''']							,dim=-2							,no_batch_dims=len(atomaa.shape[:-2]       )							,)
  
       snake_case					=       atomaa_data * batch['''atom37_atom_exists'''][..., None]
  
       return atomaa_data
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       template_feats['''template_aatype''']
       snake_case					=       template_feats['''template_torsion_angles_sin_cos''']
       snake_case					=       template_feats['''template_alt_torsion_angles_sin_cos''']
       snake_case					=       template_feats['''template_torsion_angles_mask''']
       snake_case					=       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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = False							,UpperCamelCase_ = 1e-2_0							,UpperCamelCase_ = 1e8							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       batch['''template_pseudo_beta_mask''']
       snake_case					=       template_mask[..., None] * template_mask[..., None, :]
  
       # Compute distogram (this seems to differ slightly from Alg. 5)
       snake_case					=       batch['''template_pseudo_beta''']
       snake_case					=       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2							,dim=-1							,keepdim=UpperCamelCase_       )
       snake_case					=       torch.linspace(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,device=tpb.device       ) ** 2
       snake_case					=       torch.cat([lower[1:], lower.new_tensor([inf]       )]							,dim=-1       )
       snake_case					=       ((dgram > lower) * (dgram < upper)).type(dgram.dtype       )
  
       snake_case					=       [dgram, template_mask_ad[..., None]]
  
       snake_case					=       nn.functional.one_hot(
           batch['''template_aatype''']							,rc.restype_num + 2							,)
  
       snake_case					=       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       )       )
  
       snake_case				,						snake_case				,						snake_case					=       [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
       snake_case					=       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_							,)
       snake_case					=       rigids.get_trans()[..., None, :, :]
       snake_case					=       rigids[..., None].invert_apply(UpperCamelCase_       )
  
       snake_case					=       torch.rsqrt(eps + torch.sum(rigid_vec**2							,dim=-1       )       )
  
       snake_case					=       batch['''template_all_atom_mask''']
       snake_case					=       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
       snake_case					=       template_mask[..., None] * template_mask[..., None, :]
  
       snake_case					=       inv_distance_scalar * template_mask_ad
       snake_case					=       rigid_vec * inv_distance_scalar[..., None]
  
       if not use_unit_vector:
            snake_case					=       unit_vector * 0.0
  
       to_concat.extend(torch.unbind(unit_vector[..., None, :]							,dim=-1       )       )
       to_concat.append(template_mask_ad[..., None]       )
  
       snake_case					=       torch.cat(UpperCamelCase_							,dim=-1       )
       snake_case					=       act * template_mask_ad[..., None]
  
       return act
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       nn.functional.one_hot(batch['''extra_msa''']							,23       )
       snake_case					=       [
           msa_ahot,
           batch['''extra_has_deletion'''].unsqueeze(-1       ),
           batch['''extra_deletion_value'''].unsqueeze(-1       ),
       ]
       return torch.cat(UpperCamelCase_							,dim=-1       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       rrgdf[aatype, ...]
  
       # [*, N, 8] transformations, i.e.
       #   One [*, N, 8, 3, 3] rotation matrix and
       #   One [*, N, 8, 3]    translation matrix
       snake_case					=       r.from_tensor_axa(UpperCamelCase_       )
  
       snake_case					=       alpha.new_zeros((*((1,) * len(alpha.shape[:-1]       )), 2)       )
       snake_case					=       1
  
       # [*, N, 8, 2]
       snake_case					=       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.
  
       snake_case					=       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape       )
       snake_case					=       1
       snake_case					=       alpha[..., 1]
       snake_case					=       -alpha[..., 0]
       snake_case					=       alpha
  
       snake_case					=       default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase_       )							,UpperCamelCase_       )       )
  
       snake_case					=       all_frames[..., 5]
       snake_case					=       all_frames[..., 6]
       snake_case					=       all_frames[..., 7]
  
       snake_case					=       all_frames[..., 4]
       snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )
       snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )
       snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )
  
       snake_case					=       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							,)
  
       snake_case					=       r[..., None].compose(UpperCamelCase_       )
  
       return all_frames_to_global
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       group_idx[aatype, ...]
  
       # [*, N, 14, 8]
       snake_case					=       nn.functional.one_hot(
           UpperCamelCase_							,num_classes=default_frames.shape[-3]							,)
  
       # [*, N, 14, 8]
       snake_case					=       r[..., None, :] * group_mask_one_hot
  
       # [*, N, 14]
       snake_case					=       t_atoms_to_global.map_tensor_fn(lambda UpperCamelCase_       : torch.sum(UpperCamelCase_							,dim=-1       )       )
  
       # [*, N, 14, 1]
       snake_case					=       atom_mask[aatype, ...].unsqueeze(-1       )
  
       # [*, N, 14, 3]
       snake_case					=       lit_positions[aatype, ...]
       snake_case					=       t_atoms_to_global.apply(UpperCamelCase_       )
       snake_case					=       pred_positions * atom_mask
  
       return pred_positions
  
  
  
  
  
style_context: 
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)
  
  @dataclass
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=snake_case__			)
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)					) + "\n"
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           if len(self.get_labels()					) > 1:
                snake_case					=       self.get_labels()[int(example.label					)]
           return example
  
  
  
  
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
           with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
                return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)
  
  
  
  
  
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
           snake_case					=       [] if labels is None else labels
           snake_case					=       [] if examples is None else examples
           snake_case					=       mode
           snake_case					=       verbose
      def __len__( self					):
           return len(self.examples					)
      def __getitem__( self    ,      __snake_case					):
           if isinstance(__snake_case    ,      __snake_case					):
                return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
           return self.examples[idx]
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples_from_csv(
               __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
           return processor
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples(__snake_case    ,      labels=__snake_case					)
           return processor
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
           snake_case					=       self._read_tsv(__snake_case					)
           if skip_first_row:
                snake_case					=       lines[1:]
           snake_case					=       []
           snake_case					=       []
           snake_case					=       []
           for i, line in enumerate(__snake_case					):
                texts.append(line[column_text]					)
                labels.append(line[column_label]					)
                if column_id is not None:
                     ids.append(line[column_id]					)
                else:
                     snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                     ids.append(__snake_case					)
  
           return self.add_examples(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
           if labels is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is None:
                snake_case					=       [None] * len(__snake_case					)
           if labels is None:
                snake_case					=       [None] * len(__snake_case					)
           snake_case					=       []
           snake_case					=       set()
           for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
                if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                     snake_case				,						snake_case					=       text_or_text_and_label
                else:
                     snake_case					=       text_or_text_and_label
                added_labels.add(__snake_case					)
                examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)
  
           # Update examples
           if overwrite_examples:
                snake_case					=       examples
           else:
                self.examples.extend(__snake_case					)
  
           # Update labels
           if overwrite_labels:
                snake_case					=       list(__snake_case					)
           else:
                snake_case					=       list(set(self.labels					).union(__snake_case					)					)
  
           return self.examples
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
           if max_length is None:
                snake_case					=       tokenizer.max_len
  
           snake_case					=       {label: i for i, label in enumerate(self.labels					)}
  
           snake_case					=       []
           for ex_index, example in enumerate(self.examples					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Tokenizing example {ex_index}'''					)
  
                snake_case					=       tokenizer.encode(
                    example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
                all_input_ids.append(__snake_case					)
  
           snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)
  
           snake_case					=       []
           for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)
  
                # Zero-pad up to the sequence length.
                snake_case					=       batch_length - len(__snake_case					)
                if pad_on_left:
                     snake_case					=       ([pad_token] * padding_length) + input_ids
                     snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                else:
                     snake_case					=       input_ids + ([pad_token] * padding_length)
                     snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
  
                if self.mode == "classification":
                     snake_case					=       label_map[example.label]
                elif self.mode == "regression":
                     snake_case					=       float(example.label					)
                else:
                     raise ValueError(self.mode					)
  
                if ex_index < 5 and self.verbose:
                     logger.info('''*** Example ***'''					)
                     logger.info(F'''guid: {example.guid}'''					)
                     logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                     logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                     logger.info(F'''label: {example.label} (id = {label})'''					)
  
                features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)
  
           if return_tensors is None:
                return features
           elif return_tensors == "tf":
                if not is_tf_available():
                     raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
                import tensorflow as tf
  
                def gen():
                     for ex in features:
                          yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
                snake_case					=       tf.data.Dataset.from_generator(
                    __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
                return dataset
           elif return_tensors == "pt":
                if not is_torch_available():
                     raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
                import torch
                from torch.utils.data import TensorDataset
  
                snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
                snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
                if self.mode == "classification":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
                elif self.mode == "regression":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)
  
                snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
                return dataset
           else:
                raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)
  
  
  
  
  