


'''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 					_snake_case   (     A ,    A = 512       )						->    Union[str, Any]:
							lowerCAmelCase__          =	x.shape[1] if len(x.shape       ) > 1 else 1
							lowerCAmelCase__          =	1 + np.arange(0 ,    feature_num * offset ,    A ,    dtype=np.intaa       )
							lowerCAmelCase__          =	x + feature_offset
							return x


def 					_snake_case   (     A ,    A=True       )						->    int:
							requires_backends(A ,    ['''cython''']       )

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

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

							lowerCAmelCase__          =	convert_to_single_emb(A       ) + 1
							lowerCAmelCase__          =	item['''num_nodes''']

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

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

							lowerCAmelCase__   ,					lowerCAmelCase__          =	algos_graphormer.floyd_warshall(A       )
							lowerCAmelCase__          =	np.amax(A       )

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

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

							return item






class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_=20					,    lowerCamelCase_=False   )       ->							str:
														if not is_cython_available():
																					raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''   )

														lowerCAmelCase__          =	spatial_pos_max
														lowerCAmelCase__          =	on_the_fly_processing







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

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

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

														lowerCAmelCase__          =	torch.zeros(lowerCamelCase_					,    max_node_num + 1					,    max_node_num + 1					,    dtype=torch.float   )
														lowerCAmelCase__          =	torch.zeros(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.long   )
														lowerCAmelCase__          =	torch.zeros(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.long   )
														lowerCAmelCase__          =	torch.zeros(lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.long   )
														lowerCAmelCase__          =	torch.zeros(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.long   )
														lowerCAmelCase__          =	torch.zeros(
														    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.long   )

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

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

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

														lowerCAmelCase__          =	batch['''in_degree''']

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

														return batch



'''simple docstring'''






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
														    self.key					,    self.val					,    self.freq					,    self.next is not None					,    self.prev is not None   )




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







							def __repr__(		self   )       ->							str:
														lowerCAmelCase__          =	['''DoubleLinkedList''']
														lowerCAmelCase__          =	self.head
														while node.next is not None:
																					rep.append(str(lowerCamelCase_   )   )
																					lowerCAmelCase__          =	node.next
														rep.append(str(self.rear   )   )
														return ",\n    ".join(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

														while node.prev is not None and node.prev.freq > node.freq:
																					# swap node with previous node
																					lowerCAmelCase__          =	node.prev

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

														if node.prev is None or node.next is None:
																					return None

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert self.list.remove(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
																					self.list.add(self.cache[key]   )
																					self.num_keys += 1

														else:
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node is not None  # node guaranteed to be in list
																					lowerCAmelCase__          =	value
																					self.list.add(lowerCamelCase_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

														def cache_decorator_inner(lowerCamelCase_   ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCamelCase_   ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			lowerCAmelCase__          =	LFUCache(lowerCamelCase_   )

																												lowerCAmelCase__          =	cls.decorator_function_to_instance_map[func].get(args[0]   )
																												if result is None:
																																			lowerCAmelCase__          =	func(*lowerCamelCase_   )
																																			cls.decorator_function_to_instance_map[func].put(args[0]					,    lowerCamelCase_   )
																												return result

																					def cache_info() -> LFUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()