




'''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						(	snake_case						,  snake_case = 5_12 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					str							    =			x.shape[1] if len(x.shape ) > 1 else 1
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			1 + np.arange(0						,  feature_num * offset						,  snake_case						,  dtype=np.intaa )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			x + feature_offset
   return x


def 					UpperCamelCase						(	snake_case						,  snake_case=True ):




   '''simple docstring'''

   requires_backends(snake_case						,  ['''cython'''] )

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

   SCREAMING_SNAKE_CASE__      :					int							    =			np.asarray(item['''edge_index''']						,  dtype=np.intaa )

   SCREAMING_SNAKE_CASE__      :					Any							    =			convert_to_single_emb(snake_case ) + 1
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			item['''num_nodes''']

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

   # node adj matrix [num_nodes, num_nodes] bool
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			np.zeros([num_nodes, num_nodes]						,  dtype=snake_case )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			True

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			algos_graphormer.floyd_warshall(snake_case )
   SCREAMING_SNAKE_CASE__      :					str							    =			np.amax(snake_case )

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			algos_graphormer.gen_edge_input(snake_case						,  snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			np.zeros([num_nodes + 1, num_nodes + 1]						,  dtype=np.single )  # with graph token

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

   return item







class     lowercase  :






      def __init__(self			,	a__=2_0			,	a__=False       )							-> List[str]:
         if not is_cython_available():
            raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			spatial_pos_max
         SCREAMING_SNAKE_CASE__      :					Any							    =			on_the_fly_processing






      def __call__(self			,	a__       )							-> Dict[str, Any]:
         if self.on_the_fly_processing:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[preprocess_item(a__       ) for i in features]

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

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			max(len(i['''input_nodes''']       ) for i in features       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			len(features[0]['''input_nodes'''][0]       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			len(features[0]['''attn_edge_type'''][0][0]       )
         SCREAMING_SNAKE_CASE__      :					str							    =			max(len(i['''input_edges'''][0][0]       ) for i in features       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(features[0]['''input_edges'''][0][0][0]       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			len(a__       )

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

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

            SCREAMING_SNAKE_CASE__      :					int							    =			f['''attn_bias''']
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			f[
                '''attn_edge_type'''
            ]
            SCREAMING_SNAKE_CASE__      :					int							    =			f['''spatial_pos''']
            SCREAMING_SNAKE_CASE__      :					Any							    =			f['''in_degree''']
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			f['''input_nodes''']
            SCREAMING_SNAKE_CASE__      :					int							    =			f['''input_edges''']

         SCREAMING_SNAKE_CASE__      :					Dict							    =			batch['''in_degree''']

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

         return batch







'''simple docstring'''





lowercase__								=						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]






class     lowercase  :






      def __init__(self			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			name
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]






      def  A__(self			,	a__       )							-> Dict:
         self.inbound.append(a__       )






      def  A__(self			,	a__       )							-> Any:
         self.outbound.append(a__       )






      def __repr__(self       )							-> List[str]:
         return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 					UpperCamelCase						(	snake_case						,  snake_case=3						,  snake_case=0.85 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			1

   SCREAMING_SNAKE_CASE__      :					int							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(node.outbound )

   for i in range(snake_case ):
      print(f"""======= Iteration {i + 1} =======""" )
      for _, node in enumerate(snake_case ):
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound )
      print(snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			list(input('''Enter Names of the Nodes: ''' ).split() )

   SCREAMING_SNAKE_CASE__      :					str							    =			[Node(snake_case ) for name in names]

   for ri, row in enumerate(snake_case ):
      for ci, col in enumerate(snake_case ):
         if col == 1:
            nodes[ci].add_inbound(names[ri] )
            nodes[ri].add_outbound(names[ci] )

   print('''======= Nodes =======''' )
   for node in nodes:
      print(snake_case )

   page_rank(snake_case )


if __name__ == "__main__":
       main()

