


from queue import PriorityQueue
from typing import Any

import numpy as np

def 	_a     ( SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: List[str]			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: Optional[int]			,       ):







 """simple docstring"""



 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  UpperCamelCase__    :			Optional[Any]      				=      cst_fwd.get(__A			,       np.inf   )
  UpperCamelCase__    :			int      				=      cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)   )
   UpperCamelCase__    :			Any      				=      new_cost_f
   UpperCamelCase__    :			Optional[int]      				=      v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    UpperCamelCase__    :			str      				=      cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance

def 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: Dict			,       SCREAMING_SNAKE_CASE		: Optional[int]			,       SCREAMING_SNAKE_CASE		: List[Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			Any      				=      -1

 UpperCamelCase__    :			List[Any]      				=      set()
 UpperCamelCase__    :			Optional[int]      				=      set()
 UpperCamelCase__    :			Optional[int]      				=      {source: 0}
 UpperCamelCase__    :			Any      				=      {destination: 0}
 UpperCamelCase__    :			Any      				=      {source: None}
 UpperCamelCase__    :			List[Any]      				=      {destination: None}
 UpperCamelCase__    :			Any      				=      PriorityQueue()
 UpperCamelCase__    :			Optional[Any]      				=      PriorityQueue()

 UpperCamelCase__    :			List[str]      				=      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__    :			Any      				=      queue_forward.get()
  visited_forward.add(__A   )

  UpperCamelCase__				, UpperCamelCase__    :			Dict      				=      queue_backward.get()
  visited_backward.add(__A   )

  UpperCamelCase__    :			Any      				=      pass_and_relaxation(
      __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       )

  UpperCamelCase__    :			Dict      				=      pass_and_relaxation(
      __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       __A			,       )

  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


__UpperCamelCase				:				Optional[Any]  				= {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
__UpperCamelCase				:				Union[str, 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()







'''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_		       =  {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'xmod'



 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="absolute" ,   A=True ,   A=None ,   A=False ,   A=2 ,   A=False ,   A=True ,   A=True ,   A=("en_XX",) ,   A=None ,   **A ,   )    ->     List[str]:


      """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      =  hidden_act
      _a      =  intermediate_size
      _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      =  position_embedding_type
      _a      =  use_cache
      _a      =  classifier_dropout
      _a      =  pre_norm
      _a      =  adapter_reduction_factor
      _a      =  adapter_layer_norm
      _a      =  adapter_reuse_layer_norm
      _a      =  ln_before_adapter
      _a      =  list(A       )
      _a      =  default_language






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),
          ]       )



