code: 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			(  _A ,_A = 512					):
  
  
  
  							"""simple docstring"""
  
  
  							_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			(  _A ,_A=True					):
  
  
  
  							"""simple docstring"""
  
  
  							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     ,   UpperCAmelCase=20     ,   UpperCAmelCase=False  ):
  
  
  
  
  
  
  
  														'''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     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if self.on_the_fly_processing:
  																					_lowercase				=	[preprocess_item(UpperCAmelCase  ) for i in features]
  
  														if not isinstance(features[0]     ,   UpperCAmelCase  ):
  																					_lowercase				=	[vars(UpperCAmelCase  ) 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(UpperCAmelCase  )
  
  														_lowercase				=	torch.zeros(UpperCAmelCase     ,   max_node_num + 1     ,   max_node_num + 1     ,   dtype=torch.float  )
  														_lowercase				=	torch.zeros(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   dtype=torch.long  )
  														_lowercase				=	torch.zeros(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   dtype=torch.long  )
  														_lowercase				=	torch.zeros(UpperCAmelCase     ,   UpperCAmelCase     ,   dtype=torch.long  )
  														_lowercase				=	torch.zeros(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   dtype=torch.long  )
  														_lowercase				=	torch.zeros(
  														    UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   dtype=torch.long  )
  
  														for ix, f in enumerate(UpperCAmelCase  ):
  																					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(UpperCAmelCase  ) == 1:  # one task
  																					if isinstance(sample[0]     ,   UpperCAmelCase  ):  # 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
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  