





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							(     __UpperCamelCase	,      __UpperCamelCase = 5_12)    ->  List[Any]:
     a											=   x.shape[1] if len(x.shape) > 1 else 1
     a											=   1 + np.arange(0	,      feature_num * offset	,      lowercase_	,      dtype=np.intaa)
     a											=   x + feature_offset
     return x

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase=True)    ->  Tuple:
     requires_backends(lowercase_	,      ["cython"])

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

     a											=   np.asarray(item["edge_index"]	,      dtype=np.intaa)

     a											=   convert_to_single_emb(lowercase_) + 1
     a											=   item['num_nodes']

     if len(edge_attr.shape) == 1:
          a											=   edge_attr[:, None]
     a											=   np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]	,      dtype=np.intaa)
     a											=   convert_to_single_emb(lowercase_) + 1

     # node adj matrix [num_nodes, num_nodes] bool
     a											=   np.zeros([num_nodes, num_nodes]	,      dtype=lowercase_)
     a											=   True

     a											=   algos_graphormer.floyd_warshall(lowercase_)
     a											=   np.amax(lowercase_)

     a											=   algos_graphormer.gen_edge_input(lowercase_	,      lowercase_	,      lowercase_)
     a											=   np.zeros([num_nodes + 1, num_nodes + 1]	,      dtype=np.single)  # with graph token

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

     return item





class     a__ :







     def __init__(			self						,	A=20						,	A=False				)						->					str:



          '''simple docstring'''



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

          a											=   spatial_pos_max
          a											=   on_the_fly_processing







     def __call__(			self						,	A				)						->					Dict[str, Any]:



          '''simple docstring'''



          if self.on_the_fly_processing:
               a											=   [preprocess_item(A				) for i in features]

          if not isinstance(features[0]						,	A				):
               a											=   [vars(A				) for f in features]
          a											=   {}

          a											=   max(len(i["input_nodes"]				) for i in features				)
          a											=   len(features[0]["input_nodes"][0]				)
          a											=   len(features[0]["attn_edge_type"][0][0]				)
          a											=   max(len(i["input_edges"][0][0]				) for i in features				)
          a											=   len(features[0]["input_edges"][0][0][0]				)
          a											=   len(A				)

          a											=   torch.zeros(A						,	max_node_num + 1						,	max_node_num + 1						,	dtype=torch.float				)
          a											=   torch.zeros(A						,	A						,	A						,	A						,	dtype=torch.long				)
          a											=   torch.zeros(A						,	A						,	A						,	dtype=torch.long				)
          a											=   torch.zeros(A						,	A						,	dtype=torch.long				)
          a											=   torch.zeros(A						,	A						,	A						,	dtype=torch.long				)
          a											=   torch.zeros(
              A						,	A						,	A						,	A						,	A						,	dtype=torch.long				)

          for ix, f in enumerate(A				):
               for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                    a											=   torch.tensor(f[k]				)

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

               a											=   f['attn_bias']
               a											=   f[
                   'attn_edge_type'
               ]
               a											=   f['spatial_pos']
               a											=   f['in_degree']
               a											=   f['input_nodes']
               a											=   f['input_edges']

          a											=   batch['in_degree']

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

          return batch







import safs




class     a__ (      safs.SaFileSystem   ):
     pass

