

"""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 		A    (       snake_case__						,			snake_case__ = 5_12      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= x.shape[1] if len(x.shape      ) > 1 else 1
			SCREAMING_SNAKE_CASE__									= 1 + np.arange(0						,			feature_num * offset						,			snake_case__						,			dtype=np.intaa      )
			SCREAMING_SNAKE_CASE__									= x + feature_offset
			return x

def 		A    (       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__									= np.asarray(item["""edge_attr"""]						,			dtype=np.intaa      )
			else:
						SCREAMING_SNAKE_CASE__									= 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__									= np.asarray(item["""node_feat"""]						,			dtype=np.intaa      )
			else:
						SCREAMING_SNAKE_CASE__									= np.ones((item["""num_nodes"""], 1)						,			dtype=np.intaa      )  # same embedding for all

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

			SCREAMING_SNAKE_CASE__									= convert_to_single_emb(snake_case__      ) + 1
			SCREAMING_SNAKE_CASE__									= item["""num_nodes"""]

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

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

			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= algos_graphormer.floyd_warshall(snake_case__      )
			SCREAMING_SNAKE_CASE__									= np.amax(snake_case__      )

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

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

			return item





class 				lowerCamelCase  :

			def __init__(   self			:  str      ,			__UpperCAmelCase			:  List[Any]=2_0      ,			__UpperCAmelCase			:  Union[str, Any]=False    )				->      Dict:
						if not is_cython_available():
									raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""    )

						SCREAMING_SNAKE_CASE__									= spatial_pos_max
						SCREAMING_SNAKE_CASE__									= on_the_fly_processing





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

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

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

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

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

									SCREAMING_SNAKE_CASE__									= f["""attn_bias"""]
									SCREAMING_SNAKE_CASE__									= f[
									    """attn_edge_type"""
									]
									SCREAMING_SNAKE_CASE__									= f["""spatial_pos"""]
									SCREAMING_SNAKE_CASE__									= f["""in_degree"""]
									SCREAMING_SNAKE_CASE__									= f["""input_nodes"""]
									SCREAMING_SNAKE_CASE__									= f["""input_edges"""]

						SCREAMING_SNAKE_CASE__									= batch["""in_degree"""]

						SCREAMING_SNAKE_CASE__									= features[0]["""labels"""]
						if len(__UpperCAmelCase    ) == 1:  # one task
									if isinstance(sample[0]      ,			__UpperCAmelCase    ):  # regression
												SCREAMING_SNAKE_CASE__									= torch.from_numpy(np.concatenate([i["""labels"""] for i in features]    )    )
									else:  # binary classification
												SCREAMING_SNAKE_CASE__									= 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__									= torch.from_numpy(np.stack([i["""labels"""] for i in features]      ,			axis=0    )    )

						return batch





"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			return a + b


if __name__ == "__main__":
						A_							:     List[str]     =	5
						A_							:     Optional[int]     =	6
						print(F'The sum of {a} + {b} is {add(a, b)}')


