import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a  :      Optional[Any]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {"""vocab_file""": """spiece.model"""}

a  :      List[str]            =    {
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

a  :      Dict            =    {"""bert_for_seq_generation""": 5_1_2}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Union[str, Any]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				Any          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE:				List[int]          =    []
    SCREAMING_SNAKE_CASE:				List[str]          =    ['input_ids', 'attention_mask']




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__="<s>"       ,						lowerCamelCase__="</s>"       ,						lowerCamelCase__="<unk>"       ,						lowerCamelCase__="<pad>"       ,						lowerCamelCase__="<::::>"       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Tuple       =	{} if sp_model_kwargs is None else sp_model_kwargs

       # Add extra_ids to the special token list
       super().__init__(
           bos_token=lowerCamelCase__       ,						eos_token=lowerCamelCase__       ,						unk_token=lowerCamelCase__       ,						pad_token=lowerCamelCase__       ,						sep_token=lowerCamelCase__       ,						sp_model_kwargs=self.sp_model_kwargs       ,						**lowerCamelCase__       ,						)

       lowerCAmelCase_:							str       =	vocab_file

       lowerCAmelCase_:							Union[str, Any]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(lowerCamelCase__ )




    @property
    def      _a    (    self ):
       return self.sp_model.get_piece_size()




    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	{self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab




    def __getstate__(    self ):
       lowerCAmelCase_:							int       =	self.__dict__.copy()
       lowerCAmelCase_:							List[str]       =	None
       return state




    def __setstate__(    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	d

       # for backward compatibility
       if not hasattr(self       ,						"sp_model_kwargs" ):
          lowerCAmelCase_:							Optional[Any]       =	{}

       lowerCAmelCase_:							Optional[int]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )




    def      _a    (    self       ,						lowerCamelCase__ ):
       return self.sp_model.encode(lowerCamelCase__       ,						out_type=lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       return self.sp_model.piece_to_id(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[int]       =	self.sp_model.IdToPiece(lowerCamelCase__ )
       return token




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Dict       =	[]
       lowerCAmelCase_:							List[Any]       =	""
       for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
             out_string += self.sp_model.decode(lowerCamelCase__ ) + token
             lowerCAmelCase_:							Tuple       =	[]
          else:
             current_sub_tokens.append(lowerCamelCase__ )
       out_string += self.sp_model.decode(lowerCamelCase__ )
       return out_string.strip()




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return
       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.vocab_file ):
          copyfile(self.vocab_file       ,						lowerCamelCase__ )
       elif not os.path.isfile(self.vocab_file ):
          with open(lowerCamelCase__       ,						"wb" ) as fi:
             lowerCAmelCase_:							Dict       =	self.sp_model.serialized_model_proto()
             fi.write(lowerCamelCase__ )

       return (out_vocab_file,)
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)