


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							(     SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   int = 5_12							):
     __UpperCamelCase       				=x.shape[1] if len(x.shape							) > 1 else 1
     __UpperCamelCase       				=1 + np.arange(0     ,	feature_num * offset     ,	SCREAMING_SNAKE_CASE__     ,	dtype=np.intaa							)
     __UpperCamelCase       				=x + feature_offset
     return x

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   List[str]=True							):
     requires_backends(SCREAMING_SNAKE_CASE__     ,	['cython']							)

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

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

     __UpperCamelCase       				=convert_to_single_emb(SCREAMING_SNAKE_CASE__							) + 1
     __UpperCamelCase       				=item['num_nodes']

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

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

     __UpperCamelCase				, __UpperCamelCase       				=algos_graphormer.floyd_warshall(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=np.amax(SCREAMING_SNAKE_CASE__							)

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

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

     return item






class        UpperCAmelCase__			:



    """simple docstring"""






    def __init__(     self   ,  A_=20   ,  A_=False       )    ->			Tuple:
         if not is_cython_available():
              raise ImportError('Graphormer preprocessing needs Cython (pyximport)'       )

         __UpperCamelCase       				=spatial_pos_max
         __UpperCamelCase       				=on_the_fly_processing






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

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

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

         __UpperCamelCase       				=torch.zeros(A_   ,  max_node_num + 1   ,  max_node_num + 1   ,  dtype=torch.float       )
         __UpperCamelCase       				=torch.zeros(A_   ,  A_   ,  A_   ,  A_   ,  dtype=torch.long       )
         __UpperCamelCase       				=torch.zeros(A_   ,  A_   ,  A_   ,  dtype=torch.long       )
         __UpperCamelCase       				=torch.zeros(A_   ,  A_   ,  dtype=torch.long       )
         __UpperCamelCase       				=torch.zeros(A_   ,  A_   ,  A_   ,  dtype=torch.long       )
         __UpperCamelCase       				=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       				=torch.tensor(f[k]       )

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

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

         __UpperCamelCase       				=batch['in_degree']

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

         return batch









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

   _A									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





