





'''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_							(   __snake_case							:    Dict     , __snake_case							:    Optional[Any] = 512     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=x.shape[1] if len(x.shape     ) > 1 else 1
       lowerCamelCase_							=1 + np.arange(0     , feature_num * offset     , __snake_case     , dtype=np.intaa     )
       lowerCamelCase_							=x + feature_offset
       return x





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Union[str, Any]=True     )							->		int:



       """simple docstring"""
       requires_backends(__snake_case     , ['''cython''']     )

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

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

       lowerCamelCase_							=convert_to_single_emb(__snake_case     ) + 1
       lowerCamelCase_							=item['''num_nodes''']

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

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

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

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

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

       return item


class 						__UpperCamelCase :






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



              """simple docstring"""







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

              lowerCamelCase_							=spatial_pos_max
              lowerCamelCase_							=on_the_fly_processing






       def __call__(							self,	lowerCAmelCase						):



              """simple docstring"""







              if self.on_the_fly_processing:
                     lowerCamelCase_							=[preprocess_item(UpperCamelCase__						) for i in features]

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

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

              lowerCamelCase_							=torch.zeros(UpperCamelCase__,	max_node_num + 1,	max_node_num + 1,	dtype=torch.float						)
              lowerCamelCase_							=torch.zeros(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	dtype=torch.long						)
              lowerCamelCase_							=torch.zeros(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	dtype=torch.long						)
              lowerCamelCase_							=torch.zeros(UpperCamelCase__,	UpperCamelCase__,	dtype=torch.long						)
              lowerCamelCase_							=torch.zeros(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	dtype=torch.long						)
              lowerCamelCase_							=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"]:
                            lowerCamelCase_							=torch.tensor(f[k]						)

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

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

              lowerCamelCase_							=batch['''in_degree''']

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

              return batch










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :	Union[str, Any]							   = {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Optional[int]							   = ["""OpenAIGPTTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	int							   = [
         """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OpenAIGPTDoubleHeadsModel""",
         """OpenAIGPTForSequenceClassification""",
         """OpenAIGPTLMHeadModel""",
         """OpenAIGPTModel""",
         """OpenAIGPTPreTrainedModel""",
         """load_tf_weights_in_openai_gpt""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Dict							   = [
         """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFOpenAIGPTDoubleHeadsModel""",
         """TFOpenAIGPTForSequenceClassification""",
         """TFOpenAIGPTLMHeadModel""",
         """TFOpenAIGPTMainLayer""",
         """TFOpenAIGPTModel""",
         """TFOpenAIGPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_openai_fast import OpenAIGPTTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_openai import (
              OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OpenAIGPTDoubleHeadsModel,
              OpenAIGPTForSequenceClassification,
              OpenAIGPTLMHeadModel,
              OpenAIGPTModel,
              OpenAIGPTPreTrainedModel,
              load_tf_weights_in_openai_gpt,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_openai import (
              TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFOpenAIGPTDoubleHeadsModel,
              TFOpenAIGPTForSequenceClassification,
              TFOpenAIGPTLMHeadModel,
              TFOpenAIGPTMainLayer,
              TFOpenAIGPTModel,
              TFOpenAIGPTPreTrainedModel,
          )

else:
     import sys

     a_   :	List[Any]							   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




