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							)		->	Any:
       A_							:       Union[str, Any]	      =				x.shape[1] if len(x.shape							) > 1 else 1
       A_							:       Dict	      =				1 + np.arange(0  ,   feature_num * offset  ,   a  ,   dtype=np.intaa							)
       A_							:       str	      =				x + feature_offset
       return x


def       a__      (					a  ,   a=True							)		->	Any:
       requires_backends(a  ,   ['''cython''']							)

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

       A_							:       List[Any]	      =				np.asarray(item['''edge_index''']  ,   dtype=np.intaa							)

       A_							:       Union[str, Any]	      =				convert_to_single_emb(a							) + 1
       A_							:       Optional[int]	      =				item['''num_nodes''']

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

       # node adj matrix [num_nodes, num_nodes] bool
       A_							:       Optional[int]	      =				np.zeros([num_nodes, num_nodes]  ,   dtype=a							)
       A_							:       List[Any]	      =				True

       A_      ,							A_							:       str	      =				algos_graphormer.floyd_warshall(a							)
       A_							:       List[Any]	      =				np.amax(a							)

       A_							:       Optional[int]	      =				algos_graphormer.gen_edge_input(a  ,   a  ,   a							)
       A_							:       Any	      =				np.zeros([num_nodes + 1, num_nodes + 1]  ,   dtype=np.single							)  # with graph token

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

       return item






class     __UpperCAmelCase:






 """simple docstring"""




 def __init__(    self ,       __magic_name__=20 ,       __magic_name__=False      ):

        """simple docstring"""

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

        A_							:       Union[str, Any]	      =				spatial_pos_max
        A_							:       List[str]	      =				on_the_fly_processing
 def __call__(    self ,       __magic_name__      ):

        """simple docstring"""

        if self.on_the_fly_processing:
               A_							:       List[str]	      =				[preprocess_item(__magic_name__      ) for i in features]

        if not isinstance(features[0] ,       __magic_name__      ):
               A_							:       List[str]	      =				[vars(__magic_name__      ) for f in features]
        A_							:       Any	      =				{}

        A_							:       Optional[Any]	      =				max(len(i['''input_nodes''']      ) for i in features      )
        A_							:       Dict	      =				len(features[0]['''input_nodes'''][0]      )
        A_							:       Optional[Any]	      =				len(features[0]['''attn_edge_type'''][0][0]      )
        A_							:       Union[str, Any]	      =				max(len(i['''input_edges'''][0][0]      ) for i in features      )
        A_							:       Optional[Any]	      =				len(features[0]['''input_edges'''][0][0][0]      )
        A_							:       Union[str, Any]	      =				len(__magic_name__      )

        A_							:       int	      =				torch.zeros(__magic_name__ ,       max_node_num + 1 ,       max_node_num + 1 ,       dtype=torch.float      )
        A_							:       Tuple	      =				torch.zeros(__magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       dtype=torch.long      )
        A_							:       Union[str, Any]	      =				torch.zeros(__magic_name__ ,       __magic_name__ ,       __magic_name__ ,       dtype=torch.long      )
        A_							:       Any	      =				torch.zeros(__magic_name__ ,       __magic_name__ ,       dtype=torch.long      )
        A_							:       List[Any]	      =				torch.zeros(__magic_name__ ,       __magic_name__ ,       __magic_name__ ,       dtype=torch.long      )
        A_							:       Tuple	      =				torch.zeros(
            __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       dtype=torch.long      )

        for ix, f in enumerate(__magic_name__      ):
               for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                      A_							:       Optional[int]	      =				torch.tensor(f[k]      )

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

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

        A_							:       Tuple	      =				batch['''in_degree''']

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

        return batch



from transformers import ProcessorMixin






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   """AutoFeatureExtractor"""
 __magic_name__          =   """AutoTokenizer"""


