'''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 	_UpperCAmelCase       (				_UpperCamelCase       :      str,  _UpperCamelCase       :      int = 5_12  )      -> Optional[Any]:
    A_      =  x.shape[1] if len(x.shape  ) > 1 else 1
    A_      =  1 + np.arange(0,  feature_num * offset,  _UpperCamelCase,  dtype=np.intaa  )
    A_      =  x + feature_offset
    return x





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int,  _UpperCamelCase       :      Optional[Any]=True  )      -> Tuple:
    requires_backends(_UpperCamelCase,  ['''cython''']  )

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

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

    A_      =  convert_to_single_emb(_UpperCamelCase  ) + 1
    A_      =  item['''num_nodes''']

    if len(edge_attr.shape  ) == 1:
        A_      =  edge_attr[:, None]
    A_      =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]],  dtype=np.intaa  )
    A_      =  convert_to_single_emb(_UpperCamelCase  ) + 1

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

    A_  ,A_      =  algos_graphormer.floyd_warshall(_UpperCamelCase  )
    A_      =  np.amax(_UpperCamelCase  )

    A_      =  algos_graphormer.gen_edge_input(_UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase  )
    A_      =  np.zeros([num_nodes + 1, num_nodes + 1],  dtype=np.single  )  # with graph token

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

    return item




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE=20       ,	_SCREAMING_SNAKE_CASE=False					)						->	List[str]:
           if not is_cython_available():
               raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''					)

           A_      =  spatial_pos_max
           A_      =  on_the_fly_processing

       def __call__( self       ,	_SCREAMING_SNAKE_CASE					)						->	Dict[str, Any]:
           if self.on_the_fly_processing:
               A_      =  [preprocess_item(_SCREAMING_SNAKE_CASE					) for i in features]

           if not isinstance(features[0]       ,	_SCREAMING_SNAKE_CASE					):
               A_      =  [vars(_SCREAMING_SNAKE_CASE					) for f in features]
           A_      =  {}

           A_      =  max(len(i['''input_nodes''']					) for i in features					)
           A_      =  len(features[0]['''input_nodes'''][0]					)
           A_      =  len(features[0]['''attn_edge_type'''][0][0]					)
           A_      =  max(len(i['''input_edges'''][0][0]					) for i in features					)
           A_      =  len(features[0]['''input_edges'''][0][0][0]					)
           A_      =  len(_SCREAMING_SNAKE_CASE					)

           A_      =  torch.zeros(_SCREAMING_SNAKE_CASE       ,	max_node_num + 1       ,	max_node_num + 1       ,	dtype=torch.float					)
           A_      =  torch.zeros(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	dtype=torch.long					)
           A_      =  torch.zeros(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	dtype=torch.long					)
           A_      =  torch.zeros(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	dtype=torch.long					)
           A_      =  torch.zeros(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	dtype=torch.long					)
           A_      =  torch.zeros(
               _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	dtype=torch.long					)

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

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

               A_      =  f['''attn_bias''']
               A_      =  f[
                   '''attn_edge_type'''
               ]
               A_      =  f['''spatial_pos''']
               A_      =  f['''in_degree''']
               A_      =  f['''input_nodes''']
               A_      =  f['''input_edges''']

           A_      =  batch['''in_degree''']

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

           return batch







'''simple docstring'''




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






