import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase = None							,				lowercase = None							,				lowercase = None							,				):
   if config_name_or_path is None:
      lowerCAmelCase_:							List[Any]       =	"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

   if generator_tokenizer_name_or_path is None:
      lowerCAmelCase_:							Optional[int]       =	generator_name_or_path

   if question_encoder_tokenizer_name_or_path is None:
      lowerCAmelCase_:							Tuple       =	question_encoder_name_or_path

   lowerCAmelCase_:							List[Any]       =	RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

   # Save model.
   lowerCAmelCase_:							Optional[int]       =	RagConfig.from_pretrained(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	AutoConfig.from_pretrained(lowercase					)
   lowerCAmelCase_:							Dict       =	AutoConfig.from_pretrained(lowercase					)

   lowerCAmelCase_:							Optional[int]       =	gen_config
   lowerCAmelCase_:							List[str]       =	question_encoder_config

   lowerCAmelCase_:							List[str]       =	model_class.from_pretrained_question_encoder_generator(
       lowercase							,				lowercase							,				config=lowercase					)
   rag_model.save_pretrained(lowercase					)

   # Sanity check.
   model_class.from_pretrained(lowercase					)

   # Save tokenizers.
   lowerCAmelCase_:							Tuple       =	AutoTokenizer.from_pretrained(lowercase					)
   gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"					)
   lowerCAmelCase_:							Any       =	AutoTokenizer.from_pretrained(lowercase					)
   question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"					)


if __name__ == "__main__":
 a  :      Tuple            =    argparse.ArgumentParser()
 parser.add_argument(
     """--model_type""",
     choices=["""rag_sequence""", """rag_token"""],
     required=True,
     type=str,
     help="""RAG model type: rag_sequence, rag_token""",
 )
 parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
 parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
 parser.add_argument(
     """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
 )

 parser.add_argument(
     """--generator_tokenizer_name_or_path""",
     type=str,
     help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
 )
 parser.add_argument(
     """--question_encoder_tokenizer_name_or_path""",
     type=str,
     help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
 )
 parser.add_argument(
     """--config_name_or_path""",
     type=str,
     help=(
         """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
         """ ``model_type``"""
     ),
 )

 a  :      Optional[Any]            =    parser.parse_args()

 a  :      Any            =    Path(args.dest)
 dest_dir.mkdir(exist_ok=True)

 consolidate(
     args.model_type,
     args.generator_name_or_path,
     args.question_encoder_name_or_path,
     dest_dir,
     args.config_name_or_path,
     args.generator_tokenizer_name_or_path,
     args.question_encoder_tokenizer_name_or_path,
 )
from __future__ import annotations
def 		snake_case__	(		lowercase							,				lowercase					):
   print(F'''Vertex\tShortest Distance from vertex {src}'''					)
   for i, d in enumerate(lowercase					):
      print(F'''{i}\t\t{d}'''					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for j in range(lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(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 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	[float("inf"					)] * vertex_count
   lowerCAmelCase_:							Dict       =	0.0

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

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

   lowerCAmelCase_:							str       =	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  :      List[str]            =    int(input("""Enter number of vertices: """).strip())
 a  :      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[int]            =    (
      int(x)
      for x in input("""Enter source, destination, weight: """).strip().split(""" """)
  )
  a  :      Optional[int]            =    {"""src""": src, """dst""": dest, """weight""": weight}

 a  :      List[Any]            =    int(input("""\nEnter shortest path source:""").strip())
 a  :      Tuple            =    bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)