from __future__ import annotations
class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Any			,    lowercase__					:  int):







        '''simple docstring'''



        lowerCAmelCase__     	=					vertices
        lowerCAmelCase__     	=					[[0] * vertices for _ in range(lowercase__)]




 def 		__snake_case						(			self					:  Union[str, Any]			,    lowercase__					:  list[int]):







        '''simple docstring'''



        print('Vertex \t Distance from Source')
        for vertex in range(self.vertices):
               print(lowercase__			,    '\t\t'			,    distances_from_source[vertex])




 def 		__snake_case						(			self					:  Optional[Any]			,    lowercase__					:  list[int]			,    lowercase__					:  list[bool]):







        '''simple docstring'''



        lowerCAmelCase__     	=					1e7
        lowerCAmelCase__     	=					0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices):
               if distances_from_source[vertex] < minimum and visited[vertex] is False:
                      lowerCAmelCase__     	=					distances_from_source[vertex]
                      lowerCAmelCase__     	=					vertex
        return min_index




 def 		__snake_case						(			self					:  List[str]			,    lowercase__					:  int):







        '''simple docstring'''



        lowerCAmelCase__     	=					[int(1e7)] * self.vertices  # distances from the source
        lowerCAmelCase__     	=					0
        lowerCAmelCase__     	=					[False] * self.vertices

        for _ in range(self.vertices):
               lowerCAmelCase__     	=					self.minimum_distance(lowercase__			,    lowercase__)
               lowerCAmelCase__     	=					True

               # Update dist value of the adjacent vertices
               # of the picked vertex only if the current
               # distance is greater than new distance and
               # the vertex in not in the shortest path tree
               for v in range(self.vertices):
                      if (
                          self.graph[u][v] > 0
                          and visited[v] is False
                          and distances[v] > distances[u] + self.graph[u][v]
                      ):
                             lowerCAmelCase__     	=					distances[u] + self.graph[u][v]

        self.print_solution(lowercase__)


if __name__ == "__main__":
    lowerCAmelCase__							= Graph(9)
    lowerCAmelCase__							= [
        [0, 4, 0, 0, 0, 0, 0, 8, 0],
        [4, 0, 8, 0, 0, 0, 0, 11, 0],
        [0, 8, 0, 7, 0, 4, 0, 0, 2],
        [0, 0, 7, 0, 9, 14, 0, 0, 0],
        [0, 0, 0, 9, 0, 10, 0, 0, 0],
        [0, 0, 4, 14, 10, 0, 2, 0, 0],
        [0, 0, 0, 0, 0, 2, 0, 1, 6],
        [8, 11, 0, 0, 0, 0, 1, 0, 7],
        [0, 0, 2, 0, 0, 0, 6, 7, 0],
    ]
    graph.dijkstra(0)




"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







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





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)