





'''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       _lowerCAmelCase  ( lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     int = 5_1_2 ):
			__lowercase   =							x.shape[1] if len(x.shape ) > 1 else 1
			__lowercase   =							1 + np.arange(0						,				feature_num * offset						,				lowerCamelCase_						,				dtype=np.intaa )
			__lowercase   =							x + feature_offset
			return x




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     Union[str, Any]=True ):
			requires_backends(lowerCamelCase_						,				['''cython'''] )

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

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

			__lowercase   =							convert_to_single_emb(lowerCamelCase_ ) + 1
			__lowercase   =							item['''num_nodes''']

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

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

			__lowercase  ,						__lowercase   =							algos_graphormer.floyd_warshall(lowerCamelCase_ )
			__lowercase   =							np.amax(lowerCamelCase_ )

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

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

			return item






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase=20   ,_lowerCamelCase=False       )							->							Optional[int]:







				'''simple docstring'''





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

				__lowercase   =							spatial_pos_max
				__lowercase   =							on_the_fly_processing


	def __call__(self   ,_lowerCamelCase       )							->							Dict[str, Any]:







				'''simple docstring'''





				if self.on_the_fly_processing:
							__lowercase   =							[preprocess_item(_lowerCamelCase       ) for i in features]

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

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

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

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

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

				__lowercase   =							batch['''in_degree''']

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

				return batch












'''simple docstring'''



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Union[str, Any]      =				CustomConfig


	def __init__(self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.hidden_size   ,config.hidden_size       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[Any]:







				'''simple docstring'''





				pass






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Dict      =				NoSuperInitConfig


	def __init__(self   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.attribute   ,config.attribute       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				pass





