



'''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 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str] ,       snake_case_    :     int = 5_12  )		->Union[str, Any]:






							'''simple docstring'''
							_lowercase							:		int				=   x.shape[1] if len(x.shape  ) > 1 else 1
							_lowercase							:		List[str]				=   1 + np.arange(0 ,       feature_num * offset ,       snake_case_ ,       dtype=np.intaa  )
							_lowercase							:		Any				=   x + feature_offset
							return x





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str] ,       snake_case_    :     Union[str, Any]=True  )		->Tuple:






							'''simple docstring'''
							requires_backends(snake_case_ ,       ['''cython''']  )

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

							_lowercase							:		List[str]				=   np.asarray(item['''edge_index'''] ,       dtype=np.intaa  )

							_lowercase							:		Dict				=   convert_to_single_emb(snake_case_  ) + 1
							_lowercase							:		List[Any]				=   item['''num_nodes''']

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

							# node adj matrix [num_nodes, num_nodes] bool
							_lowercase							:		Union[str, Any]				=   np.zeros([num_nodes, num_nodes] ,       dtype=snake_case_  )
							_lowercase							:		Optional[Any]				=   True

							_lowercase			, _lowercase							:		Union[str, Any]				=   algos_graphormer.floyd_warshall(snake_case_  )
							_lowercase							:		Dict				=   np.amax(snake_case_  )

							_lowercase							:		List[str]				=   algos_graphormer.gen_edge_input(snake_case_ ,       snake_case_ ,       snake_case_  )
							_lowercase							:		Tuple				=   np.zeros([num_nodes + 1, num_nodes + 1] ,       dtype=np.single  )  # with graph token

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

							return item




class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int=20      ,		UpperCamelCase_							:       Optional[int]=False		)      ->			List[Any]:







									'''simple docstring'''





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

									_lowercase							:		Optional[Any]				=   spatial_pos_max
									_lowercase							:		Tuple				=   on_the_fly_processing





		def __call__(						self							:       List[Any]      ,		UpperCamelCase_							:       List[dict]		)      ->			Dict[str, Any]:







									'''simple docstring'''





									if self.on_the_fly_processing:
																_lowercase							:		List[str]				=   [preprocess_item(UpperCamelCase_		) for i in features]

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

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

									_lowercase							:		List[Any]				=   torch.zeros(UpperCamelCase_      ,		max_node_num + 1      ,		max_node_num + 1      ,		dtype=torch.float		)
									_lowercase							:		int				=   torch.zeros(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		dtype=torch.long		)
									_lowercase							:		Tuple				=   torch.zeros(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		dtype=torch.long		)
									_lowercase							:		Tuple				=   torch.zeros(UpperCamelCase_      ,		UpperCamelCase_      ,		dtype=torch.long		)
									_lowercase							:		str				=   torch.zeros(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		dtype=torch.long		)
									_lowercase							:		Tuple				=   torch.zeros(
									    UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		dtype=torch.long		)

									for ix, f in enumerate(UpperCamelCase_		):
																for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																							_lowercase							:		Union[str, Any]				=   torch.tensor(f[k]		)

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

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

									_lowercase							:		int				=   batch['''in_degree''']

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

									return batch








'''simple docstring'''






class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int		)      ->			None:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   data
									_lowercase							:		int
									_lowercase							:		Node





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase							:		str				=   0
							_lowercase							:		List[Any]				=   x





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node ,       snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Union[str, Any]				=   find_set(snake_case_  ), find_set(snake_case_  )
							if x == y:
														return

							elif x.rank > y.rank:
														_lowercase							:		Optional[Any]				=   x
							else:
														_lowercase							:		Optional[int]				=   y
														if x.rank == y.rank:
																					y.rank += 1





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->Node:






							'''simple docstring'''
							if x != x.parent:
														_lowercase							:		str				=   find_set(x.parent  )
							return x.parent





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->set:






							'''simple docstring'''
							_lowercase							:		str				=   ({0, 1, 2}, {3, 4, 5})
							for s in sets:
														if node.data in s:
																					return s
							_lowercase							:		Optional[Any]				=   F"{node.data} is not in {sets}"
							raise ValueError(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					)		->None:






							'''simple docstring'''
							_lowercase							:		Any				=   [Node(snake_case_  ) for i in range(6  )]
							for v in vertex:
														make_set(snake_case_  )

							union_set(vertex[0] ,       vertex[1]  )
							union_set(vertex[1] ,       vertex[2]  )
							union_set(vertex[3] ,       vertex[4]  )
							union_set(vertex[3] ,       vertex[5]  )

							for nodea in vertex:
														for nodea in vertex:
																					if find_python_set(snake_case_  ).isdisjoint(find_python_set(snake_case_  )  ):
																												assert find_set(snake_case_  ) != find_set(snake_case_  )
																					else:
																												assert find_set(snake_case_  ) == find_set(snake_case_  )


if __name__ == "__main__":
						test_disjoint_set()



