

'''simple docstring'''



_UpperCamelCase						:  Dict							       =				{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def       snake_case   (	snake_case      : dict					,		snake_case      : Union[str, Any]					,		snake_case      : Dict			) ->  list[str]:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # keep track of all the paths to be checked
    lowerCAmelCase 						=							[[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
        lowerCAmelCase 						=							queue.pop(0			)
        # get the last node from the path
        lowerCAmelCase 						=							path[-1]
        if node not in explored:
            lowerCAmelCase 						=							graph[node]
            # go through all neighbour nodes, construct a new path and
            # push it into the queue
            for neighbour in neighbours:
                lowerCAmelCase 						=							list(snake_case			)
                new_path.append(snake_case			)
                queue.append(snake_case			)
                # return path if neighbour is goal
                if neighbour == goal:
                    return new_path

            # mark node as explored
            explored.add(snake_case			)

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


def       snake_case   (	snake_case      : dict					,		snake_case      : Optional[Any]					,		snake_case      : Any			) ->  int:


    """simple docstring"""


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








'''simple docstring'''



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


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

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

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

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





