





"""simple docstring"""



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


a_ =			logging.get_logger(__name__)

a_ =			{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """biogpt"""

 def __init__(    self					,						__lowerCamelCase=4_2384					,						__lowerCamelCase=1024					,						__lowerCamelCase=24					,						__lowerCamelCase=16					,						__lowerCamelCase=4096					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=1024					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.0					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     vocab_size
     __A    :						List[str]						 =     max_position_embeddings
     __A    :						Optional[int]						 =     hidden_size
     __A    :						List[str]						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						Any						 =     intermediate_size
     __A    :						Dict						 =     hidden_act
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						List[Any]						 =     attention_probs_dropout_prob
     __A    :						List[str]						 =     initializer_range
     __A    :						int						 =     layer_norm_eps
     __A    :						Optional[int]						 =     scale_embedding
     __A    :						Union[str, Any]						 =     use_cache
     __A    :						Union[str, Any]						 =     layerdrop
     __A    :						Any						 =     activation_dropout
     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




