





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      __snake_case						(					_lowerCAmelCase : Tuple				,				_lowerCAmelCase : int = 512	) ->						Optional[int]:
   A_					:				int          =			x.shape[1] if len(x.shape	) > 1 else 1
   A_					:				Union[str, Any]          =			1 + np.arange(0				,				feature_num * offset				,				_lowerCAmelCase				,				dtype=np.intaa	)
   A_					:				Union[str, Any]          =			x + feature_offset
   return x

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Union[str, Any]=True	) ->						Optional[Any]:
   requires_backends(_lowerCAmelCase				,				["cython"]	)

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

   A_					:				Tuple          =			np.asarray(item["edge_index"]				,				dtype=np.intaa	)

   A_					:				Dict          =			convert_to_single_emb(_lowerCAmelCase	) + 1
   A_					:				Optional[Any]          =			item["num_nodes"]

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

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

   A_					:				str          =			algos_graphormer.floyd_warshall(_lowerCAmelCase	)
   A_					:				Optional[Any]          =			np.amax(_lowerCAmelCase	)

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

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

   return item





class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :str			,      snake_case       :Dict=20			,      snake_case       :Dict=False  ):





        '''simple docstring'''




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

        A_					:				Union[str, Any]          =			spatial_pos_max
        A_					:				Union[str, Any]          =			on_the_fly_processing





     def __call__(							self       :List[str]			,      snake_case       :List[dict]  ):





        '''simple docstring'''




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

        if not isinstance(features[0]			,      snake_case  ):
           A_					:				Tuple          =			[vars(snake_case  ) for f in features]
        A_					:				List[str]          =			{}

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

        A_					:				List[str]          =			torch.zeros(snake_case			,      max_node_num + 1			,      max_node_num + 1			,      dtype=torch.float  )
        A_					:				Optional[int]          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[str]          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[str]          =			torch.zeros(snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				Any          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[Any]          =			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_					:				List[Any]          =			torch.tensor(f[k]  )

           if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]  ) > 0:
              A_					:				Optional[int]          =			float("-inf"  )

           A_					:				Tuple          =			f["attn_bias"]
           A_					:				List[str]          =			f[
               "attn_edge_type"
           ]
           A_					:				str          =			f["spatial_pos"]
           A_					:				Optional[int]          =			f["in_degree"]
           A_					:				Union[str, Any]          =			f["input_nodes"]
           A_					:				str          =			f["input_edges"]

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

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

        return batch








from __future__ import annotations

def      __snake_case						(					_lowerCAmelCase : int	) ->						list[int]:
   assert (
       isinstance(_lowerCAmelCase				,				_lowerCAmelCase	) and mask > 0
   ), f"mask needs to be positive integer, your input {mask}"
   A_					:				Optional[Any]          =			[]
   A_					:				Optional[int]          =			mask

   while submask:
      all_submasks.append(_lowerCAmelCase	)
      A_					:				List[str]          =			(submask - 1) & mask

   return all_submasks


if __name__ == "__main__":
 import doctest

 doctest.testmod()
