






from __future__ import annotations

A_		:		int		     =			{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    graph
         # mapping node to its parent in resulting breadth first tree
         UpperCamelCase_: dict[str, str | None]   				=    {}
         UpperCamelCase_: Optional[Any]   				=    source_vertex





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    {self.source_vertex}
         UpperCamelCase_: List[Any]   				=    None
         UpperCamelCase_: Tuple   				=    [self.source_vertex]  # first in first out queue

         while queue:
           UpperCamelCase_: List[str]   				=    queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
             if adjacent_vertex not in visited:
               visited.add(_lowerCamelCase							)
               UpperCamelCase_: Tuple   				=    vertex
               queue.append(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         if target_vertex == self.source_vertex:
           return self.source_vertex

         UpperCamelCase_: Union[str, Any]   				=    self.parent.get(_lowerCamelCase							)
         if target_vertex_parent is None:
           UpperCamelCase_: List[Any]   				=    (
               f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
           )
           raise ValueError(_lowerCamelCase							)

         return self.shortest_path(_lowerCamelCase							) + f'''->{target_vertex}'''


if __name__ == "__main__":
   A_		:		Optional[int]		     =			Graph(graph, 'G')
   g.breath_first_search()
   print(g.shortest_path('D'))
   print(g.shortest_path('G'))
   print(g.shortest_path('Foo'))







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


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

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