


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__ (							A__,			A__ = 5_1_2							):
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				x.shape[1] if len(x.shape							) > 1 else 1
			SCREAMING_SNAKE_CASE_		:    Dict 		=				1 + np.arange(0,			feature_num * offset,			__lowerCamelCase,			dtype=np.intaa							)
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				x + feature_offset
			return x






def 	a__ (							A__,			A__=True							):
			requires_backends(__lowerCamelCase,			['cython']							)

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

			SCREAMING_SNAKE_CASE_		:    int 		=				np.asarray(item['edge_index'],			dtype=np.intaa							)

			SCREAMING_SNAKE_CASE_		:    Tuple 		=				convert_to_single_emb(__lowerCamelCase							) + 1
			SCREAMING_SNAKE_CASE_		:    int 		=				item['num_nodes']

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

			# node adj matrix [num_nodes, num_nodes] bool
			SCREAMING_SNAKE_CASE_		:    str 		=				np.zeros([num_nodes, num_nodes],			dtype=__lowerCamelCase							)
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				True

			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Tuple 		=				algos_graphormer.floyd_warshall(__lowerCamelCase							)
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.amax(__lowerCamelCase							)

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

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

			return item







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__=2_0 ,	lowerCAmelCase__=False					):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:    Dict 		=				spatial_pos_max
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				on_the_fly_processing






							def __call__(			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										if self.on_the_fly_processing:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[preprocess_item(lowerCAmelCase__					) for i in features]

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

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				max(len(i['input_nodes']					) for i in features					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				len(features[0]['input_nodes'][0]					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				len(features[0]['attn_edge_type'][0][0]					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				max(len(i['input_edges'][0][0]					) for i in features					)
										SCREAMING_SNAKE_CASE_		:    int 		=				len(features[0]['input_edges'][0][0][0]					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				len(lowerCAmelCase__					)

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

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

													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				f['attn_bias']
													SCREAMING_SNAKE_CASE_		:    str 		=				f[
													    'attn_edge_type'
													]
													SCREAMING_SNAKE_CASE_		:    Dict 		=				f['spatial_pos']
													SCREAMING_SNAKE_CASE_		:    str 		=				f['in_degree']
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				f['input_nodes']
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				f['input_edges']

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				batch['in_degree']

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

										return batch









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





