
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       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     Optional[Any] = 512	):
    UpperCamelCase	:Any     							=       x.shape[1] if len(x.shape	) > 1 else 1
    UpperCamelCase	:Any     							=       1 + np.arange(0					,  feature_num * offset					,  SCREAMING_SNAKE_CASE__					,  dtype=np.intaa	)
    UpperCamelCase	:List[str]     							=       x + feature_offset
    return x


def   _A       (    SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=True	):
    requires_backends(SCREAMING_SNAKE_CASE__					,  ['''cython''']	)

    if keep_features and "edge_attr" in item.keys():  # edge_attr
        UpperCamelCase	:Any     							=       np.asarray(item['''edge_attr''']					,  dtype=np.intaa	)
    else:
        UpperCamelCase	:List[Any]     							=       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	:List[Any]     							=       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	:Union[str, Any]     							=       convert_to_single_emb(SCREAMING_SNAKE_CASE__	) + 1
    UpperCamelCase	:Union[str, Any]     							=       item['''num_nodes''']

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

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

    UpperCamelCase							,       UpperCamelCase	:Any     							=       algos_graphormer.floyd_warshall(SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Optional[int]     							=       np.amax(SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:List[str]     							=       algos_graphormer.gen_edge_input(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:str     							=       np.zeros([num_nodes + 1, num_nodes + 1]					,  dtype=np.single	)  # with graph token

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

    return item







class       UpperCAmelCase_						:



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_=20   ,    SCREAMING_SNAKE_CASE_=False			)    ->				Any:


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

          UpperCamelCase	:Union[str, Any]     							=       spatial_pos_max
          UpperCamelCase	:Optional[int]     							=       on_the_fly_processing




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


          if self.on_the_fly_processing:
              UpperCamelCase	:Tuple     							=       [preprocess_item(lowerCAmelCase__			) for i in features]

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

          UpperCamelCase	:List[str]     							=       max(len(i['''input_nodes''']			) for i in features			)
          UpperCamelCase	:Any     							=       len(features[0]['''input_nodes'''][0]			)
          UpperCamelCase	:Union[str, Any]     							=       len(features[0]['''attn_edge_type'''][0][0]			)
          UpperCamelCase	:int     							=       max(len(i['''input_edges'''][0][0]			) for i in features			)
          UpperCamelCase	:List[Any]     							=       len(features[0]['''input_edges'''][0][0][0]			)
          UpperCamelCase	:Union[str, Any]     							=       len(lowerCAmelCase__			)

          UpperCamelCase	:Optional[int]     							=       torch.zeros(lowerCAmelCase__   ,    max_node_num + 1   ,    max_node_num + 1   ,    dtype=torch.float			)
          UpperCamelCase	:Tuple     							=       torch.zeros(lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    dtype=torch.long			)
          UpperCamelCase	:Tuple     							=       torch.zeros(lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    dtype=torch.long			)
          UpperCamelCase	:str     							=       torch.zeros(lowerCAmelCase__   ,    lowerCAmelCase__   ,    dtype=torch.long			)
          UpperCamelCase	:Optional[int]     							=       torch.zeros(lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    dtype=torch.long			)
          UpperCamelCase	:Optional[int]     							=       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"]:
                  UpperCamelCase	:Optional[int]     							=       torch.tensor(f[k]			)

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

              UpperCamelCase	:int     							=       f['''attn_bias''']
              UpperCamelCase	:Dict     							=       f[
                  '''attn_edge_type'''
              ]
              UpperCamelCase	:str     							=       f['''spatial_pos''']
              UpperCamelCase	:str     							=       f['''in_degree''']
              UpperCamelCase	:Tuple     							=       f['''input_nodes''']
              UpperCamelCase	:Any     							=       f['''input_edges''']

          UpperCamelCase	:Dict     							=       batch['''in_degree''']

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

          return batch













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





