






from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[float]      ,					__lowercase :      Any     )    ->   Any:





    """simple docstring"""

    print(f"Vertex\tShortest Distance from vertex {src}"     )
    for i, d in enumerate(__lowercase     ):
        print(f"{i}\t\t{d}"     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[dict[str, int]]      ,					__lowercase :      list[float]      ,					__lowercase :      int     )    ->   Any:





    """simple docstring"""

    for j in range(__lowercase     ):
        __A     ,						__A     ,						__A       =						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
        if distance[u] != float("""inf"""     ) and distance[u] + w < distance[v]:
            return True
    return False



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[dict[str, int]]      ,					__lowercase :      int      ,					__lowercase :      int      ,					__lowercase :      int     )    ->   list[float]:





    """simple docstring"""

    __A       =						[float("""inf"""     )] * vertex_count
    __A       =						0.0

    for _ in range(vertex_count - 1     ):
        for j in range(__lowercase     ):
            __A     ,						__A     ,						__A       =						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

            if distance[u] != float("""inf"""     ) and distance[u] + w < distance[v]:
                __A       =						distance[u] + w

    __A       =						check_negative_cycle(__lowercase      ,					__lowercase      ,					__lowercase     )
    if negative_cycle_exists:
        raise Exception("""Negative cycle found"""     )

    return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a   :							Optional[Any]						= int(input("Enter number of vertices: ").strip())
  __a   :							Optional[Any]						= int(input("Enter number of edges: ").strip())

  __a   :							list[dict[str, int]]						= [{} for _ in range(E)]

  for i in range(E):
    print("Edge ", i + 1)
    __a	,__a	,__a   :							Optional[Any]						= (
        int(x)
        for x in input("Enter source, destination, weight: ").strip().split(" ")
    )
    __a   :							List[Any]						= {"src": src, "dst": dest, "weight": weight}

  __a   :							int						= int(input("\nEnter shortest path source:").strip())
  __a   :							Dict						= bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)












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


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

# TODO: upload to AWS
__a   :							Union[str, Any]						= {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"retribert"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int=30_522			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Union[str, Any]=8			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      List[Any]=3_072			,						UpperCamelCase_							:      Tuple="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[str]=512			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      List[str]=1e-12			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=128			,						UpperCamelCase_							:      Union[str, Any]=0			,						**UpperCamelCase_							:      List[str]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						hidden_act
         __A       =						intermediate_size
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						share_encoders
         __A       =						projection_dim




