



"""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 					_lowerCAmelCase (  lowercase_						,      lowercase_ = 512		):
						UpperCAmelCase			 =					x.shape[1] if len(x.shape		) > 1 else 1
						UpperCAmelCase			 =					1 + np.arange(0						,      feature_num * offset						,      lowercase_						,      dtype=np.intaa		)
						UpperCAmelCase			 =					x + feature_offset
						return x

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=True		):
						requires_backends(lowercase_						,      ['cython']		)

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

						UpperCAmelCase			 =					np.asarray(item['edge_index']						,      dtype=np.intaa		)

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

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

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

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

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

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

						return item







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Any  ,    lowercase_					:Tuple=20  ,    lowercase_					:List[Any]=False   )							->     List[str]:
												if not is_cython_available():
																		raise ImportError('Graphormer preprocessing needs Cython (pyximport)'   )

												UpperCAmelCase			 =					spatial_pos_max
												UpperCAmelCase			 =					on_the_fly_processing






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

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

												UpperCAmelCase			 =					max(len(i['input_nodes']   ) for i in features   )
												UpperCAmelCase			 =					len(features[0]['input_nodes'][0]   )
												UpperCAmelCase			 =					len(features[0]['attn_edge_type'][0][0]   )
												UpperCAmelCase			 =					max(len(i['input_edges'][0][0]   ) for i in features   )
												UpperCAmelCase			 =					len(features[0]['input_edges'][0][0][0]   )
												UpperCAmelCase			 =					len(a__   )

												UpperCAmelCase			 =					torch.zeros(a__  ,    max_node_num + 1  ,    max_node_num + 1  ,    dtype=torch.float   )
												UpperCAmelCase			 =					torch.zeros(a__  ,    a__  ,    a__  ,    a__  ,    dtype=torch.long   )
												UpperCAmelCase			 =					torch.zeros(a__  ,    a__  ,    a__  ,    dtype=torch.long   )
												UpperCAmelCase			 =					torch.zeros(a__  ,    a__  ,    dtype=torch.long   )
												UpperCAmelCase			 =					torch.zeros(a__  ,    a__  ,    a__  ,    dtype=torch.long   )
												UpperCAmelCase			 =					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"]:
																								UpperCAmelCase			 =					torch.tensor(f[k]   )

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

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

												UpperCAmelCase			 =					batch['''in_degree''']

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

