



from __future__ import annotations


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Optional[int]:







  '''simple docstring'''


  print(F'''Vertex\tShortest Distance from vertex {src}'''							)
  for i, d in enumerate(_lowerCAmelCase							):
    print(F'''{i}\t\t{d}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  for j in range(_lowerCAmelCase							):
    lowercase_					,    lowercase_					,    lowercase_								= (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						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


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

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

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

  lowercase_								= check_negative_cycle(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase							)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""							)

  return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase						:		List[str]			= int(input("Enter number of vertices: ").strip())
       UpperCAmelCase						:		List[Any]			= int(input("Enter number of edges: ").strip())

       UpperCAmelCase						:		List[str]			= [{} for _ in range(E)]

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

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






"""simple docstring"""
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


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


UpperCAmelCase						:		List[Any]			= "▁"

UpperCAmelCase						:		Union[str, Any]			= {"vocab_file": "spiece.model"}

UpperCAmelCase						:		List[Any]			= {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCAmelCase						:		Optional[Any]			= {
    "google/reformer-crime-and-punishment": 52_4288,
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   ["input_ids", "attention_mask"]

    def __init__(				self			:				int ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Dict="</s>" ,  lowerCAmelCase_			:				Dict="<unk>" ,  lowerCAmelCase_			:				Dict=[] ,  lowerCAmelCase_			:				Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:				List[str] ,  ):







      """simple docstring"""







      lowercase_								= {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  additional_special_tokens=lowerCAmelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase_ ,  )

      lowercase_								= vocab_file
      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)

    @property
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      return self.sp_model.get_piece_size()

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= {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			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.__dict__.copy()
      lowercase_								= None
      return state

    def __setstate__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= d

      # for backward compatibility
      if not hasattr(self ,  """sp_model_kwargs"""):
        lowercase_								= {}

      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      return self.sp_model.piece_to_id(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      if index < self.sp_model.get_piece_size():
        lowercase_								= self.sp_model.IdToPiece(lowerCAmelCase_)
      return token

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= """"""
      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
          lowercase_								= []
        else:
          current_sub_tokens.append(lowerCAmelCase_)
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()




    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[str] = None):







      """simple docstring"""







      if not os.path.isdir(lowerCAmelCase_):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
        return
      lowercase_								= 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:
          lowercase_								= self.sp_model.serialized_model_proto()
          fi.write(lowerCAmelCase_)

      return (out_vocab_file,)

