






from queue import PriorityQueue
from typing import Any

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )	->    float | int:
  for nxt, d in graph[v]:
    if nxt in visited_forward:
      continue
    UpperCamelCase_: str   				=    cst_fwd.get(UpperCAmelCase__     ,   np.inf  )
    UpperCamelCase_: List[Any]   				=    cst_fwd[v] + d
    if new_cost_f < old_cost_f:
      queue.put((new_cost_f, nxt)  )
      UpperCamelCase_: Optional[Any]   				=    new_cost_f
      UpperCamelCase_: List[str]   				=    v
    if nxt in visited_backward:
      if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
        UpperCamelCase_: int   				=    cst_fwd[v] + d + cst_bwd[nxt]
  return shortest_distance

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Union[str, Any]   				=    -1

  UpperCamelCase_: Tuple   				=    set()
  UpperCamelCase_: Tuple   				=    set()
  UpperCamelCase_: int   				=    {source: 0}
  UpperCamelCase_: Dict   				=    {destination: 0}
  UpperCamelCase_: Optional[Any]   				=    {source: None}
  UpperCamelCase_: Dict   				=    {destination: None}
  UpperCamelCase_: PriorityQueue[Any]   				=    PriorityQueue()
  UpperCamelCase_: PriorityQueue[Any]   				=    PriorityQueue()

  UpperCamelCase_: List[Any]   				=    np.inf

  queue_forward.put((0, source)  )
  queue_backward.put((0, destination)  )

  if source == destination:
    return 0

  while not queue_forward.empty() and not queue_backward.empty():
    UpperCamelCase_				,UpperCamelCase_: str   				=    queue_forward.get()
    visited_forward.add(UpperCAmelCase__  )

    UpperCamelCase_				,UpperCamelCase_: Any   				=    queue_backward.get()
    visited_backward.add(UpperCAmelCase__  )

    UpperCamelCase_: Union[str, Any]   				=    pass_and_relaxation(
        UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )

    UpperCamelCase_: Optional[Any]   				=    pass_and_relaxation(
        UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   )

    if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
      break

  if shortest_distance != np.inf:
    UpperCamelCase_: List[str]   				=    shortest_distance
  return shortest_path_distance


A_		:		Dict		     =			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_		:		Any		     =			{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()







from collections import OrderedDict
from typing import Mapping

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


A_		:		List[Any]		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			{
    '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     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







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