

"""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__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 5_1_2				)			->			Union[str, Any]:
		__lowerCAmelCase: int					   =    x.shape[1] if len(x.shape				) > 1 else 1
		__lowerCAmelCase: Dict					   =    1 + np.arange(0      ,				feature_num * offset      ,				__SCREAMING_SNAKE_CASE      ,				dtype=np.intaa				)
		__lowerCAmelCase: Any					   =    x + feature_offset
		return x






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=True				)			->			Union[str, Any]:
		requires_backends(__SCREAMING_SNAKE_CASE      ,				["cython"]				)

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

		__lowerCAmelCase: int					   =    np.asarray(item["edge_index"]      ,				dtype=np.intaa				)

		__lowerCAmelCase: Union[str, Any]					   =    convert_to_single_emb(__SCREAMING_SNAKE_CASE				) + 1
		__lowerCAmelCase: List[str]					   =    item["num_nodes"]

		if len(edge_attr.shape				) == 1:
				__lowerCAmelCase: Tuple					   =    edge_attr[:, None]
		__lowerCAmelCase: Tuple					   =    np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,				dtype=np.intaa				)
		__lowerCAmelCase: Union[str, Any]					   =    convert_to_single_emb(__SCREAMING_SNAKE_CASE				) + 1

		# node adj matrix [num_nodes, num_nodes] bool
		__lowerCAmelCase: Any					   =    np.zeros([num_nodes, num_nodes]      ,				dtype=__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: int					   =    True

		__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    algos_graphormer.floyd_warshall(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[Any]					   =    np.amax(__SCREAMING_SNAKE_CASE				)

		__lowerCAmelCase: Optional[int]					   =    algos_graphormer.gen_edge_input(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[Any]					   =    np.zeros([num_nodes + 1, num_nodes + 1]      ,				dtype=np.single				)  # with graph token

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

		return item





class 					snake_case			:

				def __init__(			self							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]=2_0  ,						UpperCamelCase__							:       List[str]=False)->     Any:



						'''simple docstring'''


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

						__lowerCAmelCase: Dict					   =    spatial_pos_max
						__lowerCAmelCase: int					   =    on_the_fly_processing




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



						'''simple docstring'''


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

						if not isinstance(features[0]  ,						UpperCamelCase__):
								__lowerCAmelCase: Any					   =    [vars(UpperCamelCase__) for f in features]
						__lowerCAmelCase: Tuple					   =    {}

						__lowerCAmelCase: Dict					   =    max(len(i["input_nodes"]) for i in features)
						__lowerCAmelCase: List[Any]					   =    len(features[0]["input_nodes"][0])
						__lowerCAmelCase: Any					   =    len(features[0]["attn_edge_type"][0][0])
						__lowerCAmelCase: List[str]					   =    max(len(i["input_edges"][0][0]) for i in features)
						__lowerCAmelCase: List[str]					   =    len(features[0]["input_edges"][0][0][0])
						__lowerCAmelCase: Optional[Any]					   =    len(UpperCamelCase__)

						__lowerCAmelCase: Dict					   =    torch.zeros(UpperCamelCase__  ,						max_node_num + 1  ,						max_node_num + 1  ,						dtype=torch.float)
						__lowerCAmelCase: Dict					   =    torch.zeros(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=torch.long)
						__lowerCAmelCase: List[str]					   =    torch.zeros(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=torch.long)
						__lowerCAmelCase: Optional[int]					   =    torch.zeros(UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=torch.long)
						__lowerCAmelCase: List[str]					   =    torch.zeros(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=torch.long)
						__lowerCAmelCase: List[Any]					   =    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: Any					   =    torch.tensor(f[k])

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

								__lowerCAmelCase: Optional[Any]					   =    f["attn_bias"]
								__lowerCAmelCase: Any					   =    f[
								    "attn_edge_type"
								]
								__lowerCAmelCase: Optional[Any]					   =    f["spatial_pos"]
								__lowerCAmelCase: List[str]					   =    f["in_degree"]
								__lowerCAmelCase: str					   =    f["input_nodes"]
								__lowerCAmelCase: Union[str, Any]					   =    f["input_edges"]

						__lowerCAmelCase: Tuple					   =    batch["in_degree"]

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

						return batch





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    p - q * (a // b)

		assert a % d == 0 and b % d == 0
		assert d == a * x + b * y

		return (d, x, y)


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="diophantine", verbose=True)
			testmod(name="diophantine_all_soln", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)


