
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__ :		Optional[int]					=logging.get_logger(__name__)

A__ :		Tuple					={
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''vilt'''
 def __init__( self      :     Any					, lowerCamelCase      :     int=3_05_22					, lowerCamelCase      :     Any=2					, lowerCamelCase      :     List[Any]=2					, lowerCamelCase      :     Any=40					, lowerCamelCase      :     List[Any]=7_68					, lowerCamelCase      :     Dict=12					, lowerCamelCase      :     List[Any]=12					, lowerCamelCase      :     Union[str, Any]=30_72					, lowerCamelCase      :     Union[str, Any]="gelu"					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     str=0.0					, lowerCamelCase      :     int=0.02					, lowerCamelCase      :     Optional[int]=1e-1_2					, lowerCamelCase      :     List[Any]=3_84					, lowerCamelCase      :     Tuple=32					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     int=True					, lowerCamelCase      :     List[str]=-1					, lowerCamelCase      :     List[str]=False					, lowerCamelCase      :     int=-1					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(tie_word_embeddings=lowerCamelCase					, **lowerCamelCase							)

   __A		:  List[Any]       				=		vocab_size
   __A		:  Tuple       				=		type_vocab_size
   __A		:  Optional[int]       				=		modality_type_vocab_size
   __A		:  int       				=		max_position_embeddings

   __A		:  Tuple       				=		hidden_size
   __A		:  Dict       				=		num_hidden_layers
   __A		:  Optional[int]       				=		num_attention_heads
   __A		:  List[str]       				=		intermediate_size
   __A		:  Optional[int]       				=		hidden_act
   __A		:  Dict       				=		hidden_dropout_prob
   __A		:  int       				=		attention_probs_dropout_prob
   __A		:  List[Any]       				=		initializer_range
   __A		:  Tuple       				=		layer_norm_eps

   __A		:  Dict       				=		image_size
   __A		:  Optional[int]       				=		patch_size
   __A		:  List[Any]       				=		num_channels
   __A		:  Union[str, Any]       				=		qkv_bias
   __A		:  Union[str, Any]       				=		max_image_length
   __A		:  str       				=		num_images





'''simple docstring'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



