



'''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       (				lowerCamelCase :    Tuple					,		lowerCamelCase :    int = 5_12       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    Tuple=True       ):






	'''simple docstring'''




	requires_backends(lowerCamelCase					,		["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(lowerCamelCase       ) + 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(lowerCamelCase       ) + 1

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

	__lowerCAmelCase				,							__lowerCAmelCase       				=	algos_graphormer.floyd_warshall(lowerCamelCase       )
	__lowerCAmelCase       				=	np.amax(lowerCamelCase       )

	__lowerCAmelCase       				=	algos_graphormer.gen_edge_input(lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )
	__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(lowerCamelCase					,		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        UpperCAmelCase__						:



					def __init__(     self      ,					UpperCamelCase=20      ,					UpperCamelCase=False  )  ->      Tuple:
						if not is_cython_available():
							raise ImportError("Graphormer preprocessing needs Cython (pyximport)"  )

						__lowerCAmelCase       				=	spatial_pos_max
						__lowerCAmelCase       				=	on_the_fly_processing



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

						if not isinstance(features[0]      ,					UpperCamelCase  ):
							__lowerCAmelCase       				=	[vars(UpperCamelCase  ) 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(UpperCamelCase  )

						__lowerCAmelCase       				=	torch.zeros(UpperCamelCase      ,					max_node_num + 1      ,					max_node_num + 1      ,					dtype=torch.float  )
						__lowerCAmelCase       				=	torch.zeros(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					dtype=torch.long  )
						__lowerCAmelCase       				=	torch.zeros(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					dtype=torch.long  )
						__lowerCAmelCase       				=	torch.zeros(UpperCamelCase      ,					UpperCamelCase      ,					dtype=torch.long  )
						__lowerCAmelCase       				=	torch.zeros(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					dtype=torch.long  )
						__lowerCAmelCase       				=	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"]:
								__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(UpperCamelCase  ) == 1:  # one task
							if isinstance(sample[0]      ,					UpperCamelCase  ):  # 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'''



def 	__lowerCAmelCase       (				lowerCamelCase :    list[list[int]]					,		lowerCamelCase :    list[list[int]]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	len(lowerCamelCase       )
	for i in range(lowerCamelCase       ):
		__lowerCAmelCase       				=	[]
		for j in range(lowerCamelCase       ):
			__lowerCAmelCase       				=	0
			for k in range(lowerCamelCase       ):
				__lowerCAmelCase       				=	val + matrix_a[i][k] * matrix_b[k][j]
			list_a.append(lowerCamelCase       )
		matrix_c.append(lowerCamelCase       )
	return matrix_c



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	return [[int(row == column       ) for column in range(lowerCamelCase       )] for row in range(lowerCamelCase       )]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	identity(2       )
	__lowerCAmelCase       				=	[[1, 1], [1, 0]]
	__lowerCAmelCase       				=	n - 1
	while n > 0:
		if n % 2 == 1:
			__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	int(n / 2       )
	return res_matrix[0][0]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	0
	__lowerCAmelCase       				=	1
	for _ in range(2					,		n + 1       ):
		__lowerCAmelCase				,							__lowerCAmelCase       				=	fiba, fiba + fiba
	return fiba



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
		__lowerCAmelCase       				=	int("".join(c for c in ordinal if c in "0123456789"       )       )  # 1000th --> 1000
		print(
		    f'''{ordinal} fibonacci number using matrix exponentiation is '''
		    f'''{nth_fibonacci_matrix(lowerCamelCase       )} and using bruteforce is '''
		    f'''{nth_fibonacci_bruteforce(lowerCamelCase       )}\n'''       )
	# from timeit import timeit
	# print(timeit("nth_fibonacci_matrix(1000000)",
	#              "from main import nth_fibonacci_matrix", number=5))
	# print(timeit("nth_fibonacci_bruteforce(1000000)",
	#              "from main import nth_fibonacci_bruteforce", number=5))
	# 2.3342058970001744
	# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()