



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							(   __magic_name__						:	Optional[Any]  ,			__magic_name__						:	int = 512    ):
							a__:							Union[str, Any]									=x.shape[1] if len(x.shape    ) > 1 else 1
							a__:							Any									=1 + np.arange(0  ,			feature_num * offset  ,			__magic_name__  ,			dtype=np.intaa    )
							a__:							Optional[Any]									=x + feature_offset
							return x





def     __lowerCamelCase							(   __magic_name__						:	Optional[int]  ,			__magic_name__						:	Optional[int]=True    ):
							requires_backends(__magic_name__  ,			["cython"]    )

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

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

							a__:							str									=convert_to_single_emb(__magic_name__    ) + 1
							a__:							List[str]									=item["num_nodes"]

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

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

							a__				,      a__:							Dict									=algos_graphormer.floyd_warshall(__magic_name__    )
							a__:							List[Any]									=np.amax(__magic_name__    )

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

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

							return item







class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	Dict=2_0   ,							_a    :	Tuple=False      ):
													if not is_cython_available():
																				raise ImportError("Graphormer preprocessing needs Cython (pyximport)"      )

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







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

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

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

													a__:							Union[str, Any]									=torch.zeros(_a   ,							max_node_num + 1   ,							max_node_num + 1   ,							dtype=torch.float      )
													a__:							int									=torch.zeros(_a   ,							_a   ,							_a   ,							_a   ,							dtype=torch.long      )
													a__:							Dict									=torch.zeros(_a   ,							_a   ,							_a   ,							dtype=torch.long      )
													a__:							str									=torch.zeros(_a   ,							_a   ,							dtype=torch.long      )
													a__:							Optional[Any]									=torch.zeros(_a   ,							_a   ,							_a   ,							dtype=torch.long      )
													a__:							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"]:
																											a__:							Optional[Any]									=torch.tensor(f[k]      )

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

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

													a__:							Tuple									=batch["in_degree"]

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

													return batch










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





