
"""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 			A_			(					lowercase						, lowercase = 512      )     ->							Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  x.shape[1] if len(x.shape      ) > 1 else 1
     UpperCAmelCase_						:    List[Any]    					=  1 + np.arange(0						, feature_num * offset						, lowercase						, dtype=np.intaa      )
     UpperCAmelCase_						:    Optional[Any]    					=  x + feature_offset
     return x




def 			A_			(					lowercase						, lowercase=True      )     ->							int:


     """simple docstring"""







     requires_backends(lowercase						, ["""cython"""]      )

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

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

     UpperCAmelCase_						:    Dict    					=  convert_to_single_emb(lowercase      ) + 1
     UpperCAmelCase_						:    Any    					=  item["""num_nodes"""]

     if len(edge_attr.shape      ) == 1:
          UpperCAmelCase_						:    Dict    					=  edge_attr[:, None]
     UpperCAmelCase_						:    Optional[int]    					=  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]						, dtype=np.intaa      )
     UpperCAmelCase_						:    str    					=  convert_to_single_emb(lowercase      ) + 1

     # node adj matrix [num_nodes, num_nodes] bool
     UpperCAmelCase_						:    Optional[Any]    					=  np.zeros([num_nodes, num_nodes]						, dtype=lowercase      )
     UpperCAmelCase_						:    Any    					=  True

     UpperCAmelCase_					,UpperCAmelCase_						:    Union[str, Any]    					=  algos_graphormer.floyd_warshall(lowercase      )
     UpperCAmelCase_						:    List[str]    					=  np.amax(lowercase      )

     UpperCAmelCase_						:    Dict    					=  algos_graphormer.gen_edge_input(lowercase						, lowercase						, lowercase      )
     UpperCAmelCase_						:    Any    					=  np.zeros([num_nodes + 1, num_nodes + 1]						, dtype=np.single      )  # with graph token

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

     return item



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : List[str]     ,   a_       : int=20     ,   a_       : Optional[int]=False )->					Tuple:



      """simple docstring"""

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

      UpperCAmelCase_						:    str    					=  spatial_pos_max
      UpperCAmelCase_						:    Any    					=  on_the_fly_processing






 def __call__( self       : Optional[int]     ,   a_       : List[dict] )->					Dict[str, Any]:



      """simple docstring"""

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

      if not isinstance(features[0]     ,   a_ ):
           UpperCAmelCase_						:    Dict    					=  [vars(a_ ) for f in features]
      UpperCAmelCase_						:    str    					=  {}

      UpperCAmelCase_						:    Optional[int]    					=  max(len(i["""input_nodes"""] ) for i in features )
      UpperCAmelCase_						:    Dict    					=  len(features[0]["""input_nodes"""][0] )
      UpperCAmelCase_						:    int    					=  len(features[0]["""attn_edge_type"""][0][0] )
      UpperCAmelCase_						:    Dict    					=  max(len(i["""input_edges"""][0][0] ) for i in features )
      UpperCAmelCase_						:    List[str]    					=  len(features[0]["""input_edges"""][0][0][0] )
      UpperCAmelCase_						:    int    					=  len(a_ )

      UpperCAmelCase_						:    str    					=  torch.zeros(a_     ,   max_node_num + 1     ,   max_node_num + 1     ,   dtype=torch.float )
      UpperCAmelCase_						:    str    					=  torch.zeros(a_     ,   a_     ,   a_     ,   a_     ,   dtype=torch.long )
      UpperCAmelCase_						:    Any    					=  torch.zeros(a_     ,   a_     ,   a_     ,   dtype=torch.long )
      UpperCAmelCase_						:    Optional[Any]    					=  torch.zeros(a_     ,   a_     ,   dtype=torch.long )
      UpperCAmelCase_						:    int    					=  torch.zeros(a_     ,   a_     ,   a_     ,   dtype=torch.long )
      UpperCAmelCase_						:    List[str]    					=  torch.zeros(
          a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   dtype=torch.long )

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

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

           UpperCAmelCase_						:    Optional[int]    					=  f["""attn_bias"""]
           UpperCAmelCase_						:    Optional[int]    					=  f[
               """attn_edge_type"""
           ]
           UpperCAmelCase_						:    Union[str, Any]    					=  f["""spatial_pos"""]
           UpperCAmelCase_						:    Tuple    					=  f["""in_degree"""]
           UpperCAmelCase_						:    Tuple    					=  f["""input_nodes"""]
           UpperCAmelCase_						:    Optional[int]    					=  f["""input_edges"""]

      UpperCAmelCase_						:    List[Any]    					=  batch["""in_degree"""]

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

      return batch



"""simple docstring"""







import warnings

from .generation import GenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 warnings.warn(
     """Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will """
     """be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."""  , lowerCamelCase_  , )

