'''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        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			int = 512)    ->		List[Any]:



						"""simple docstring"""

						snake_case :       Union[str, Any]				=    x.shape[1] if len(x.shape) > 1 else 1
						snake_case :       Any				=    1 + np.arange(0,      feature_num * offset,      _lowerCAmelCase,      dtype=np.intaa)
						snake_case :       Optional[Any]				=    x + feature_offset
						return x


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			Optional[int]=True)    ->		Optional[Any]:



						"""simple docstring"""

						requires_backends(_lowerCAmelCase,      ["cython"])

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

						snake_case :       Union[str, Any]				=    np.asarray(item["edge_index"],      dtype=np.intaa)

						snake_case :       str				=    convert_to_single_emb(_lowerCAmelCase) + 1
						snake_case :       List[str]				=    item["num_nodes"]

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

						# node adj matrix [num_nodes, num_nodes] bool
						snake_case :       Tuple				=    np.zeros([num_nodes, num_nodes],      dtype=_lowerCAmelCase)
						snake_case :       Any				=    True

						snake_case :       Dict				=    algos_graphormer.floyd_warshall(_lowerCAmelCase)
						snake_case :       List[Any]				=    np.amax(_lowerCAmelCase)

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

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

						return item




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






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

									snake_case :       List[str]				=    spatial_pos_max
									snake_case :       Optional[int]				=    on_the_fly_processing






			def __call__(							self				,			snake_case_					):
									if self.on_the_fly_processing:
															snake_case :       Union[str, Any]				=    [preprocess_item(_a					) for i in features]

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

									snake_case :       Optional[int]				=    max(len(i["input_nodes"]					) for i in features					)
									snake_case :       Optional[Any]				=    len(features[0]["input_nodes"][0]					)
									snake_case :       Any				=    len(features[0]["attn_edge_type"][0][0]					)
									snake_case :       Optional[int]				=    max(len(i["input_edges"][0][0]					) for i in features					)
									snake_case :       List[str]				=    len(features[0]["input_edges"][0][0][0]					)
									snake_case :       List[Any]				=    len(_a					)

									snake_case :       Union[str, Any]				=    torch.zeros(_a				,			max_node_num + 1				,			max_node_num + 1				,			dtype=torch.float					)
									snake_case :       int				=    torch.zeros(_a				,			_a				,			_a				,			_a				,			dtype=torch.long					)
									snake_case :       Dict				=    torch.zeros(_a				,			_a				,			_a				,			dtype=torch.long					)
									snake_case :       str				=    torch.zeros(_a				,			_a				,			dtype=torch.long					)
									snake_case :       Optional[Any]				=    torch.zeros(_a				,			_a				,			_a				,			dtype=torch.long					)
									snake_case :       List[Any]				=    torch.zeros(
									    _a				,			_a				,			_a				,			_a				,			_a				,			dtype=torch.long					)

									for ix, f in enumerate(_a					):
															for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																					snake_case :       Optional[Any]				=    torch.tensor(f[k]					)

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

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

									snake_case :       Tuple				=    batch["in_degree"]

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

									return batch










from typing import Callable





def     __lowerCamelCase							(   __magic_name__						:	Callable    ):

							def wrapper(__magic_name__						:	Optional[Any]    ):
														a__:							List[Any]									=function_with_docstring.__doc__
														return target_function

							return wrapper





