




'''simple docstring'''





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 UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_ = 512						):
    _a				:							Optional[int]							       =      x.shape[1] if len(x.shape						) > 1 else 1
    _a				:							Any							       =      1 + np.arange(0   ,	feature_num * offset   ,	UpperCAmelCase_   ,	dtype=np.intaa						)
    _a				:							str							       =      x + feature_offset
    return x





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=True						):
    requires_backends(UpperCAmelCase_   ,	["""cython"""]						)

    if keep_features and "edge_attr" in item.keys():  # edge_attr
        _a				:							Optional[Any]							       =      np.asarray(item["""edge_attr"""]   ,	dtype=np.intaa						)
    else:
        _a				:							str							       =      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
        _a				:							str							       =      np.asarray(item["""node_feat"""]   ,	dtype=np.intaa						)
    else:
        _a				:							str							       =      np.ones((item["""num_nodes"""], 1)   ,	dtype=np.intaa						)  # same embedding for all

    _a				:							Dict							       =      np.asarray(item["""edge_index"""]   ,	dtype=np.intaa						)

    _a				:							Union[str, Any]							       =      convert_to_single_emb(UpperCAmelCase_						) + 1
    _a				:							List[Any]							       =      item["""num_nodes"""]

    if len(edge_attr.shape						) == 1:
        _a				:							Any							       =      edge_attr[:, None]
    _a				:							List[Any]							       =      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]   ,	dtype=np.intaa						)
    _a				:							str							       =      convert_to_single_emb(UpperCAmelCase_						) + 1

    # node adj matrix [num_nodes, num_nodes] bool
    _a				:							Any							       =      np.zeros([num_nodes, num_nodes]   ,	dtype=UpperCAmelCase_						)
    _a				:							Dict							       =      True

    _a       , _a				:							Dict							       =      algos_graphormer.floyd_warshall(UpperCAmelCase_						)
    _a				:							Tuple							       =      np.amax(UpperCAmelCase_						)

    _a				:							Union[str, Any]							       =      algos_graphormer.gen_edge_input(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
    _a				:							Dict							       =      np.zeros([num_nodes + 1, num_nodes + 1]   ,	dtype=np.single						)  # with graph token

    # combine
    _a				:							str							       =      input_nodes + 1  # we shift all indices by one for padding
    _a				:							Union[str, Any]							       =      attn_bias
    _a				:							str							       =      attn_edge_type
    _a				:							Union[str, Any]							       =      shortest_path_result.astype(np.intaa						) + 1  # we shift all indices by one for padding
    _a				:							List[Any]							       =      np.sum(UpperCAmelCase_   ,	axis=1						).reshape(-1						) + 1  # we shift all indices by one for padding
    _a				:							Optional[int]							       =      item["""in_degree"""]  # for undirected graph
    _a				:							int							       =      input_edges + 1  # we shift all indices by one for padding
    if "labels" not in item:
        _a				:							str							       =      item["""y"""]

    return item





class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase=20		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if not is_cython_available():
          raise ImportError("""Graphormer preprocessing needs Cython (pyximport)""" )

      _a				:							str							       =      spatial_pos_max
      _a				:							List[Any]							       =      on_the_fly_processing



  def __call__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if self.on_the_fly_processing:
          _a				:							Optional[int]							       =      [preprocess_item(__lowerCAmelCase ) for i in features]

      if not isinstance(features[0]		,     __lowerCAmelCase ):
          _a				:							Union[str, Any]							       =      [vars(__lowerCAmelCase ) for f in features]
      _a				:							Optional[Any]							       =      {}

      _a				:							List[str]							       =      max(len(i["""input_nodes"""] ) for i in features )
      _a				:							Dict							       =      len(features[0]["""input_nodes"""][0] )
      _a				:							Optional[int]							       =      len(features[0]["""attn_edge_type"""][0][0] )
      _a				:							List[Any]							       =      max(len(i["""input_edges"""][0][0] ) for i in features )
      _a				:							Dict							       =      len(features[0]["""input_edges"""][0][0][0] )
      _a				:							Union[str, Any]							       =      len(__lowerCAmelCase )

      _a				:							str							       =      torch.zeros(__lowerCAmelCase		,     max_node_num + 1		,     max_node_num + 1		,     dtype=torch.float )
      _a				:							List[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Optional[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							List[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Tuple							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Tuple							       =      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"]:
              _a				:							Tuple							       =      torch.tensor(f[k] )

          if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max] ) > 0:
              _a				:							List[str]							       =      float("""-inf""" )

          _a				:							List[str]							       =      f["""attn_bias"""]
          _a				:							Any							       =      f[
              """attn_edge_type"""
          ]
          _a				:							Dict							       =      f["""spatial_pos"""]
          _a				:							Dict							       =      f["""in_degree"""]
          _a				:							str							       =      f["""input_nodes"""]
          _a				:							List[Any]							       =      f["""input_edges"""]

      _a				:							Union[str, Any]							       =      batch["""in_degree"""]

      _a				:							Optional[Any]							       =      features[0]["""labels"""]
      if len(__lowerCAmelCase ) == 1:  # one task
          if isinstance(sample[0]		,     __lowerCAmelCase ):  # regression
              _a				:							List[str]							       =      torch.from_numpy(np.concatenate([i["""labels"""] for i in features] ) )
          else:  # binary classification
              _a				:							Any							       =      torch.from_numpy(np.concatenate([i["""labels"""] for i in features] ) )
      else:  # multi task classification, left to float to keep the NaNs
          _a				:							int							       =      torch.from_numpy(np.stack([i["""labels"""] for i in features]		,     axis=0 ) )

      return batch



def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  if not isinstance(lowerCamelCase__			,					lowerCamelCase__       ):
    raise ValueError("double_factorial() only accepts integral values"       )
  if n < 0:
    raise ValueError("double_factorial() not defined for negative values"       )
  return 1 if n <= 1 else n * double_factorial(n - 2       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

