


__UpperCamelCase       :       Tuple						       =				{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   set()
 # keep track of all the paths to be checked
 UpperCamelCase__					:    Optional[Any]          =   [[start]]

 # return path if start is goal
 if start == goal:
  return [start]

 # keeps looping until all possible paths have been checked
 while queue:
  # pop the first path from the queue
  UpperCamelCase__					:    List[str]          =   queue.pop(0     )
  # get the last node from the path
  UpperCamelCase__					:    List[str]          =   path[-1]
  if node not in explored:
   UpperCamelCase__					:    Dict          =   graph[node]
   # go through all neighbour nodes, construct a new path and
   # push it into the queue
   for neighbour in neighbours:
    UpperCamelCase__					:    Dict          =   list(SCREAMING_SNAKE_CASE     )
    new_path.append(SCREAMING_SNAKE_CASE     )
    queue.append(SCREAMING_SNAKE_CASE     )
    # return path if neighbour is goal
    if neighbour == goal:
     return new_path

            # mark node as explored
   explored.add(SCREAMING_SNAKE_CASE     )

    # in case there's no path between the 2 nodes
 return []

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict    , SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 if not graph or start not in graph or target not in graph:
  return -1
 if start == target:
  return 0
 UpperCamelCase__					:    Dict          =   [start]
 UpperCamelCase__					:    List[Any]          =   set(SCREAMING_SNAKE_CASE     )
 # Keep tab on distances from `start` node.
 UpperCamelCase__					:    Tuple          =   {start: 0, target: -1}
 while queue:
  UpperCamelCase__					:    Optional[Any]          =   queue.pop(0     )
  if node == target:
   UpperCamelCase__					:    Any          =   (
       dist[node] if dist[target] == -1 else min(dist[target]    , dist[node]     )
   )
  for adjacent in graph[node]:
   if adjacent not in visited:
    visited.add(SCREAMING_SNAKE_CASE     )
    queue.append(SCREAMING_SNAKE_CASE     )
    UpperCamelCase__					:    Optional[Any]          =   dist[node] + 1
 return dist[target]


if __name__ == "__main__":
       print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
       print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__UpperCamelCase       :       Optional[int]						       =				{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      "markuplm"


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     Optional[int]=30522							,	lowerCamelCase__							:     Dict=768							,	lowerCamelCase__							:     List[str]=12							,	lowerCamelCase__							:     List[Any]=12							,	lowerCamelCase__							:     Optional[Any]=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     Optional[int]=0.1							,	lowerCamelCase__							:     Tuple=512							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     Union[str, Any]=0.02							,	lowerCamelCase__							:     Optional[Any]=1E-1_2							,	lowerCamelCase__							:     List[str]=0							,	lowerCamelCase__							:     Tuple=0							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     List[Any]=256							,	lowerCamelCase__							:     List[str]=1024							,	lowerCamelCase__							:     Optional[int]=216							,	lowerCamelCase__							:     Any=1001							,	lowerCamelCase__							:     int=32							,	lowerCamelCase__							:     Tuple=50							,	lowerCamelCase__							:     str="absolute"							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     List[Any]=None							,	**lowerCamelCase__							:     List[str]							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      super().__init__(
          pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__							,	)
      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    List[Any]          =   hidden_size
      UpperCamelCase__					:    Any          =   num_hidden_layers
      UpperCamelCase__					:    Tuple          =   num_attention_heads
      UpperCamelCase__					:    Any          =   hidden_act
      UpperCamelCase__					:    Dict          =   intermediate_size
      UpperCamelCase__					:    Optional[Any]          =   hidden_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Tuple          =   max_position_embeddings
      UpperCamelCase__					:    List[str]          =   type_vocab_size
      UpperCamelCase__					:    Any          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Any          =   position_embedding_type
      UpperCamelCase__					:    Optional[Any]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout
      # additional properties
      UpperCamelCase__					:    int          =   max_depth
      UpperCamelCase__					:    Optional[Any]          =   max_xpath_tag_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   max_xpath_subs_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   tag_pad_id
      UpperCamelCase__					:    Optional[int]          =   subs_pad_id
      UpperCamelCase__					:    Optional[Any]          =   xpath_unit_hidden_size


