





"""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   ):






  """simple docstring"""





  A_    :			int									=    x.shape[1] if len(x.shape   ) > 1 else 1
  A_    :			Union[str, Any]									=    1 + np.arange(0					,   feature_num * offset					,   _UpperCAmelCase					,   dtype=np.intaa   )
  A_    :			Dict									=    x + feature_offset
  return x





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase=True   ):






  """simple docstring"""





  requires_backends(_UpperCAmelCase					,   ['cython']   )

  if keep_features and "edge_attr" in item.keys():  # edge_attr
    A_    :			Tuple									=    np.asarray(item['edge_attr']					,   dtype=np.intaa   )
  else:
    A_    :			List[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_    :			Tuple									=    np.asarray(item['node_feat']					,   dtype=np.intaa   )
  else:
    A_    :			Any									=    np.ones((item['num_nodes'], 1)					,   dtype=np.intaa   )  # same embedding for all

  A_    :			Union[str, Any]									=    np.asarray(item['edge_index']					,   dtype=np.intaa   )

  A_    :			Optional[int]									=    convert_to_single_emb(_UpperCAmelCase   ) + 1
  A_    :			Any									=    item['num_nodes']

  if len(edge_attr.shape   ) == 1:
    A_    :			str									=    edge_attr[:, None]
  A_    :			Optional[Any]									=    np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa   )
  A_    :			int									=    convert_to_single_emb(_UpperCAmelCase   ) + 1

  # node adj matrix [num_nodes, num_nodes] bool
  A_    :			int									=    np.zeros([num_nodes, num_nodes]					,   dtype=_UpperCAmelCase   )
  A_    :			str									=    True

  A_		,			A_    :			Union[str, Any]									=    algos_graphormer.floyd_warshall(_UpperCAmelCase   )
  A_    :			Optional[int]									=    np.amax(_UpperCAmelCase   )

  A_    :			Optional[Any]									=    algos_graphormer.gen_edge_input(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Tuple									=    np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single   )  # with graph token

  # combine
  A_    :			int									=    input_nodes + 1  # we shift all indices by one for padding
  A_    :			Union[str, Any]									=    attn_bias
  A_    :			Tuple									=    attn_edge_type
  A_    :			List[str]									=    shortest_path_result.astype(np.intaa   ) + 1  # we shift all indices by one for padding
  A_    :			Tuple									=    np.sum(_UpperCAmelCase					,   axis=1   ).reshape(-1   ) + 1  # we shift all indices by one for padding
  A_    :			Dict									=    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_    :			Optional[Any]									=    item['y']

  return item





class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_=2_0   , snake_case_=False    ):

        """simple docstring"""

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

        A_    :			Any									=    spatial_pos_max
        A_    :			Tuple									=    on_the_fly_processing

      def __call__(	self   , snake_case_    ):

        """simple docstring"""

        if self.on_the_fly_processing:
          A_    :			Optional[Any]									=    [preprocess_item(snake_case_    ) for i in features]

        if not isinstance(features[0]   , snake_case_    ):
          A_    :			Optional[Any]									=    [vars(snake_case_    ) for f in features]
        A_    :			Optional[int]									=    {}

        A_    :			int									=    max(len(i['input_nodes']    ) for i in features    )
        A_    :			int									=    len(features[0]['input_nodes'][0]    )
        A_    :			Optional[Any]									=    len(features[0]['attn_edge_type'][0][0]    )
        A_    :			int									=    max(len(i['input_edges'][0][0]    ) for i in features    )
        A_    :			str									=    len(features[0]['input_edges'][0][0][0]    )
        A_    :			str									=    len(snake_case_    )

        A_    :			List[str]									=    torch.zeros(snake_case_   , max_node_num + 1   , max_node_num + 1   , dtype=torch.float    )
        A_    :			List[Any]									=    torch.zeros(snake_case_   , snake_case_   , snake_case_   , snake_case_   , dtype=torch.long    )
        A_    :			Dict									=    torch.zeros(snake_case_   , snake_case_   , snake_case_   , dtype=torch.long    )
        A_    :			int									=    torch.zeros(snake_case_   , snake_case_   , dtype=torch.long    )
        A_    :			Any									=    torch.zeros(snake_case_   , snake_case_   , snake_case_   , dtype=torch.long    )
        A_    :			Dict									=    torch.zeros(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , dtype=torch.long    )

        for ix, f in enumerate(snake_case_    ):
          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_    :			Union[str, Any]									=    float('-inf'    )

          A_    :			List[Any]									=    f['attn_bias']
          A_    :			int									=    f[
              'attn_edge_type'
          ]
          A_    :			Any									=    f['spatial_pos']
          A_    :			List[Any]									=    f['in_degree']
          A_    :			Dict									=    f['input_nodes']
          A_    :			Optional[Any]									=    f['input_edges']

        A_    :			List[Any]									=    batch['in_degree']

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

        return batch






"""simple docstring"""




def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    (boundary[1] - boundary[0]) / steps
  A_    :			Optional[int]									=    boundary[0]
  A_    :			Optional[int]									=    boundary[1]
  A_    :			Any									=    make_points(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Dict									=    0.0
  y += (h / 3.0) * f(_UpperCAmelCase   )
  A_    :			Optional[int]									=    2
  for i in x_i:
    y += (h / 3) * (4 - 2 * (cnt % 2)) * f(_UpperCAmelCase   )
    cnt += 1
  y += (h / 3.0) * f(_UpperCAmelCase   )
  return y





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    a + h
  while x < (b - h):
    yield x
    A_    :			Dict									=    x + h





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):  # enter your function here






  """simple docstring"""





  A_    :			List[Any]									=    (x - 0) * (x - 0)
  return y





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    0.0  # Lower bound of integration
  A_    :			int									=    1.0  # Upper bound of integration
  A_    :			Tuple									=    10.0  # define number of steps or resolution
  A_    :			List[str]									=    [a, b]  # define boundary of integration
  A_    :			Optional[Any]									=    method_a(_UpperCAmelCase					,   _UpperCAmelCase   )
  print(f"""y = {y}"""   )


if __name__ == "__main__":
   main()