"""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_						( lowerCamelCase ,      lowerCamelCase = 5_1_2	):
				UpperCAmelCase__					=							x.shape[1] if len(x.shape	) > 1 else 1
				UpperCAmelCase__					=							1 + np.arange(0 ,      feature_num * offset ,      lowerCamelCase ,      dtype=np.intaa	)
				UpperCAmelCase__					=							x + feature_offset
				return x



def   a_						( lowerCamelCase ,      lowerCamelCase=True	):
				requires_backends(lowerCamelCase ,      ['cython']	)

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

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

				UpperCAmelCase__					=							convert_to_single_emb(lowerCamelCase	) + 1
				UpperCAmelCase__					=							item['num_nodes']

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

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

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

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

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

				return item







class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Tuple   ,lowerCamelCase__       :     Optional[int]=20   ,lowerCamelCase__       :     int=False      ):
									if not is_cython_available():
													raise ImportError('Graphormer preprocessing needs Cython (pyximport)'      )

									UpperCAmelCase__					=							spatial_pos_max
									UpperCAmelCase__					=							on_the_fly_processing







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

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

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

									UpperCAmelCase__					=							torch.zeros(lowerCamelCase__   ,max_node_num + 1   ,max_node_num + 1   ,dtype=torch.float      )
									UpperCAmelCase__					=							torch.zeros(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,dtype=torch.long      )
									UpperCAmelCase__					=							torch.zeros(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,dtype=torch.long      )
									UpperCAmelCase__					=							torch.zeros(lowerCamelCase__   ,lowerCamelCase__   ,dtype=torch.long      )
									UpperCAmelCase__					=							torch.zeros(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,dtype=torch.long      )
									UpperCAmelCase__					=							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"]:
																	UpperCAmelCase__					=							torch.tensor(f[k]      )

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

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

									UpperCAmelCase__					=							batch['in_degree']

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

									return batch




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



