


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     UpperCAmelCase_ (							__snake_case					,						__snake_case = 512						)					->      Tuple:







 """simple docstring"""


 _lowercase											=x.shape[1] if len(x.shape						) > 1 else 1
 _lowercase											=1 + np.arange(0					,						feature_num * offset					,						__snake_case					,						dtype=np.intaa						)
 _lowercase											=x + feature_offset
 return x
def     UpperCAmelCase_ (							__snake_case					,						__snake_case=True						)					->      Tuple:







 """simple docstring"""


 requires_backends(__snake_case					,						['''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(__snake_case						) + 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(__snake_case						) + 1

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

 _lowercase				,   _lowercase											=algos_graphormer.floyd_warshall(__snake_case						)
 _lowercase											=np.amax(__snake_case						)

 _lowercase											=algos_graphormer.gen_edge_input(__snake_case					,						__snake_case					,						__snake_case						)
 _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(__snake_case					,						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 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase=2_0		,					UpperCAmelCase=False	)					->       int:
      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	)					->       Dict[str, Any]:
      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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__            =							{'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__            =							[
      '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VisualBertForMultipleChoice''',
      '''VisualBertForPreTraining''',
      '''VisualBertForQuestionAnswering''',
      '''VisualBertForRegionToPhraseAlignment''',
      '''VisualBertForVisualReasoning''',
      '''VisualBertLayer''',
      '''VisualBertModel''',
      '''VisualBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_visual_bert import (
        VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VisualBertForMultipleChoice,
        VisualBertForPreTraining,
        VisualBertForQuestionAnswering,
        VisualBertForRegionToPhraseAlignment,
        VisualBertForVisualReasoning,
        VisualBertLayer,
        VisualBertModel,
        VisualBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase__            =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

