



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       'convbert'



 def __init__(self ,   A=30_522 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=2 ,   A=0.02 ,   A=1E-12 ,   A=1 ,   A=0 ,   A=2 ,   A=768 ,   A=2 ,   A=9 ,   A=1 ,   A=None ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(
          pad_token_id=A ,   bos_token_id=A ,   eos_token_id=A ,   **A ,   )

      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  intermediate_size
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  max_position_embeddings
      _a      =  type_vocab_size
      _a      =  initializer_range
      _a      =  layer_norm_eps
      _a      =  embedding_size
      _a      =  head_ratio
      _a      =  conv_kernel_size
      _a      =  num_groups
      _a      =  classifier_dropout






class __A			(  A						):



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a      =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]       )








'''simple docstring'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



