code: from typing import Any, Dict, List, Mapping
  
  import numpy as np
  import torch
  
  from ...utils import is_cython_available, requires_backends
  
  
  if is_cython_available():
   import pyximport
  
   pyximport.install(setup_args={'''include_dirs''': np.get_include()})
   from . import algos_graphormer  # noqa E402
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int = 512				):
         _UpperCamelCase     :       Any  						=  x.shape[1] if len(x.shape				) > 1 else 1
         _UpperCamelCase     :       Tuple  						=  1 + np.arange(0		,						feature_num * offset		,						lowercase_		,						dtype=np.intaa				)
         _UpperCamelCase     :       Dict  						=  x + feature_offset
         return x
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Optional[Any]=True				):
         requires_backends(lowercase_		,						['cython']				)
  
         if keep_features and "edge_attr" in item.keys():  # edge_attr
                _UpperCamelCase     :       Dict  						=  np.asarray(item['edge_attr']		,						dtype=np.intaa				)
         else:
                _UpperCamelCase     :       Optional[Any]  						=  np.ones((len(item['edge_index'][0]				), 1)		,						dtype=np.intaa				)  # same embedding for all
  
         if keep_features and "node_feat" in item.keys():  # input_nodes
                _UpperCamelCase     :       Tuple  						=  np.asarray(item['node_feat']		,						dtype=np.intaa				)
         else:
                _UpperCamelCase     :       Union[str, Any]  						=  np.ones((item['num_nodes'], 1)		,						dtype=np.intaa				)  # same embedding for all
  
         _UpperCamelCase     :       Optional[Any]  						=  np.asarray(item['edge_index']		,						dtype=np.intaa				)
  
         _UpperCamelCase     :       Union[str, Any]  						=  convert_to_single_emb(lowercase_				) + 1
         _UpperCamelCase     :       List[Any]  						=  item['num_nodes']
  
         if len(edge_attr.shape				) == 1:
                _UpperCamelCase     :       Optional[int]  						=  edge_attr[:, None]
         _UpperCamelCase     :       str  						=  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,						dtype=np.intaa				)
         _UpperCamelCase     :       int  						=  convert_to_single_emb(lowercase_				) + 1
  
         # node adj matrix [num_nodes, num_nodes] bool
         _UpperCamelCase     :       List[str]  						=  np.zeros([num_nodes, num_nodes]		,						dtype=lowercase_				)
         _UpperCamelCase     :       Any  						=  True
  
         _UpperCamelCase						,      _UpperCamelCase     :       Tuple  						=  algos_graphormer.floyd_warshall(lowercase_				)
         _UpperCamelCase     :       str  						=  np.amax(lowercase_				)
  
         _UpperCamelCase     :       List[str]  						=  algos_graphormer.gen_edge_input(lowercase_		,						lowercase_		,						lowercase_				)
         _UpperCamelCase     :       List[Any]  						=  np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single				)  # with graph token
  
         # combine
         _UpperCamelCase     :       Dict  						=  input_nodes + 1  # we shift all indices by one for padding
         _UpperCamelCase     :       Any  						=  attn_bias
         _UpperCamelCase     :       Any  						=  attn_edge_type
         _UpperCamelCase     :       Optional[int]  						=  shortest_path_result.astype(np.intaa				) + 1  # we shift all indices by one for padding
         _UpperCamelCase     :       Optional[int]  						=  np.sum(lowercase_		,						axis=1				).reshape(-1				) + 1  # we shift all indices by one for padding
         _UpperCamelCase     :       Optional[Any]  						=  item['in_degree']  # for undirected graph
         _UpperCamelCase     :       Union[str, Any]  						=  input_edges + 1  # we shift all indices by one for padding
         if "labels" not in item:
                _UpperCamelCase     :       Dict  						=  item['y']
  
         return item
  
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		str       ,			lowerCamelCase       :		Optional[int]=2_0       ,			lowerCamelCase       :		Tuple=False  ) ->							str:
          if not is_cython_available():
                 raise ImportError('Graphormer preprocessing needs Cython (pyximport)'  )
  
          _UpperCamelCase     :       Optional[Any]  						=  spatial_pos_max
          _UpperCamelCase     :       Any  						=  on_the_fly_processing
  
   def __call__(			self       :		Optional[int]       ,			lowerCamelCase       :		List[dict]  ) ->							Dict[str, Any]:
          if self.on_the_fly_processing:
                 _UpperCamelCase     :       Dict  						=  [preprocess_item(lowerCamelCase  ) for i in features]
  
          if not isinstance(features[0]       ,			lowerCamelCase  ):
                 _UpperCamelCase     :       Dict  						=  [vars(lowerCamelCase  ) for f in features]
          _UpperCamelCase     :       Dict  						=  {}
  
          _UpperCamelCase     :       Optional[Any]  						=  max(len(i['input_nodes']  ) for i in features  )
          _UpperCamelCase     :       List[str]  						=  len(features[0]['input_nodes'][0]  )
          _UpperCamelCase     :       Tuple  						=  len(features[0]['attn_edge_type'][0][0]  )
          _UpperCamelCase     :       str  						=  max(len(i['input_edges'][0][0]  ) for i in features  )
          _UpperCamelCase     :       Any  						=  len(features[0]['input_edges'][0][0][0]  )
          _UpperCamelCase     :       Union[str, Any]  						=  len(lowerCamelCase  )
  
          _UpperCamelCase     :       List[Any]  						=  torch.zeros(lowerCamelCase       ,			max_node_num + 1       ,			max_node_num + 1       ,			dtype=torch.float  )
          _UpperCamelCase     :       List[str]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
          _UpperCamelCase     :       Optional[Any]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
          _UpperCamelCase     :       Union[str, Any]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
          _UpperCamelCase     :       Tuple  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
          _UpperCamelCase     :       Optional[Any]  						=  torch.zeros(
              lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
  
          for ix, f in enumerate(lowerCamelCase  ):
                 for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                        _UpperCamelCase     :       int  						=  torch.tensor(f[k]  )
  
                 if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]  ) > 0:
                        _UpperCamelCase     :       int  						=  float('-inf'  )
  
                 _UpperCamelCase     :       List[Any]  						=  f['attn_bias']
                 _UpperCamelCase     :       Optional[int]  						=  f[
                     'attn_edge_type'
                 ]
                 _UpperCamelCase     :       Union[str, Any]  						=  f['spatial_pos']
                 _UpperCamelCase     :       List[str]  						=  f['in_degree']
                 _UpperCamelCase     :       List[Any]  						=  f['input_nodes']
                 _UpperCamelCase     :       List[str]  						=  f['input_edges']
  
          _UpperCamelCase     :       List[str]  						=  batch['in_degree']
  
          _UpperCamelCase     :       Dict  						=  features[0]['labels']
          if len(lowerCamelCase  ) == 1:  # one task
                 if isinstance(sample[0]       ,			lowerCamelCase  ):  # regression
                        _UpperCamelCase     :       List[str]  						=  torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
                 else:  # binary classification
                        _UpperCamelCase     :       Any  						=  torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
          else:  # multi task classification, left to float to keep the NaNs
                 _UpperCamelCase     :       List[str]  						=  torch.from_numpy(np.stack([i['labels'] for i in features]       ,			axis=0  )  )
  
          return batch
  
  
  
  
style_context: from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  
  