





lowercase__						:      Optional[Any]	=    {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def  a__    (    lowercase :					dict, lowercase :					Union[str, Any], lowercase :					int				)		->		list[str]:





     """simple docstring"""

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

            # mark node as explored
               explored.add(lowercase				)

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







def  a__    (    lowercase :					dict, lowercase :					Dict, lowercase :					Union[str, Any]				)		->		int:





     """simple docstring"""

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


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

lowercase__						:      Tuple	=    {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					int						    =   'trocr'
       _snake_case     :					List[str]						    =   ['past_key_values']
       _snake_case     :					Tuple						    =   {
           'num_attention_heads': 'decoder_attention_heads',
           'hidden_size': 'd_model',
           'num_hidden_layers': 'decoder_layers',
       }





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]=50265	,			lowerCAmelCase__       :		List[Any]=1024	,			lowerCAmelCase__       :		List[str]=12	,			lowerCAmelCase__       :		int=16	,			lowerCAmelCase__       :		List[Any]=4096	,			lowerCAmelCase__       :		Optional[int]="gelu"	,			lowerCAmelCase__       :		int=512	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		Optional[int]=0.0	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Tuple=2	,			lowerCAmelCase__       :		Dict=0.02	,			lowerCAmelCase__       :		Tuple=0.0	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		Optional[Any]=False	,			lowerCAmelCase__       :		Any=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Optional[int]=1	,			lowerCAmelCase__       :		List[Any]=0	,			lowerCAmelCase__       :		Any=2	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	d_model
            _UpperCamelCase       =	decoder_layers
            _UpperCamelCase       =	decoder_attention_heads
            _UpperCamelCase       =	decoder_ffn_dim
            _UpperCamelCase       =	activation_function
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	dropout
            _UpperCamelCase       =	attention_dropout
            _UpperCamelCase       =	activation_dropout
            _UpperCamelCase       =	init_std
            _UpperCamelCase       =	decoder_layerdrop
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	scale_embedding
            _UpperCamelCase       =	use_learned_position_embeddings
            _UpperCamelCase       =	layernorm_embedding

            super().__init__(
                pad_token_id=lowerCAmelCase__	,			bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			decoder_start_token_id=lowerCAmelCase__	,			**lowerCAmelCase__	,			)



