





'''simple docstring'''


from __future__ import annotations

a__			:					List[str]       						=						{
    '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					:				Optional[int]       ,     a__					:				dict[str, list[str]]       ,     a__					:				str						):
              UpperCAmelCase  =						graph
              # mapping node to its parent in resulting breadth first tree
              UpperCAmelCase  =						{}
              UpperCAmelCase  =						source_vertex

       def 			__snake_case      ( self					:				Optional[int]						):
              UpperCAmelCase  =						{self.source_vertex}
              UpperCAmelCase  =						None
              UpperCAmelCase  =						[self.source_vertex]  # first in first out queue

              while queue:
                     UpperCAmelCase  =						queue.pop(0						)
                     for adjacent_vertex in self.graph[vertex]:
                            if adjacent_vertex not in visited:
                                   visited.add(a__						)
                                   UpperCAmelCase  =						vertex
                                   queue.append(a__						)



       def 			__snake_case      ( self					:				Any       ,     a__					:				str						):
              if target_vertex == self.source_vertex:
                     return self.source_vertex

              UpperCAmelCase  =						self.parent.get(a__						)
              if target_vertex_parent is None:
                     UpperCAmelCase  =						(
                         f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                     )
                     raise ValueError(a__						)

              return self.shortest_path(a__						) + f"->{target_vertex}"


if __name__ == "__main__":
    a__			:					Tuple       						=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))






"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

