


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_A		:				Tuple									=       logging.getLogger(__name__)



def     __lowerCAmelCase						(							)       ->							Optional[int]:
   __lowerCamelCase:				Any     =	argparse.ArgumentParser(
       description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).""" )
   parser.add_argument("""--file_path"""						,      type=snake_case						,      default="""data/dump.txt"""						,      help="""The path to the data.""" )
   parser.add_argument("""--tokenizer_type"""						,      type=snake_case						,      default="""bert"""						,      choices=["""bert""", """roberta""", """gpt2"""] )
   parser.add_argument("""--tokenizer_name"""						,      type=snake_case						,      default="""bert-base-uncased"""						,      help="""The tokenizer to use.""" )
   parser.add_argument("""--dump_file"""						,      type=snake_case						,      default="""data/dump"""						,      help="""The dump file prefix.""" )
   __lowerCamelCase:				Tuple     =	parser.parse_args()

   logger.info(f'Loading Tokenizer ({args.tokenizer_name})' )
   if args.tokenizer_type == "bert":
      __lowerCamelCase:				List[str]     =	BertTokenizer.from_pretrained(args.tokenizer_name )
      __lowerCamelCase:				int     =	tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
      __lowerCamelCase:				Tuple     =	tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      __lowerCamelCase:				int     =	RobertaTokenizer.from_pretrained(args.tokenizer_name )
      __lowerCamelCase:				int     =	tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
      __lowerCamelCase:				Optional[int]     =	tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
   elif args.tokenizer_type == "gpt2":
      __lowerCamelCase:				List[str]     =	GPTaTokenizer.from_pretrained(args.tokenizer_name )
      __lowerCamelCase:				int     =	tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
      __lowerCamelCase:				int     =	tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

   logger.info(f'Loading text from {args.file_path}' )
   with open(args.file_path						,      """r"""						,      encoding="""utf8""" ) as fp:
      __lowerCamelCase:				Optional[int]     =	fp.readlines()

   logger.info("""Start encoding""" )
   logger.info(f'{len(snake_case )} examples to process.' )

   __lowerCamelCase:				Dict     =	[]
   __lowerCamelCase:				List[str]     =	0
   __lowerCamelCase:				Optional[Any]     =	10000
   __lowerCamelCase:				Optional[Any]     =	time.time()
   for text in data:
      __lowerCamelCase:				Any     =	f'{bos} {text.strip()} {sep}'
      __lowerCamelCase:				Any     =	tokenizer.encode(snake_case						,      add_special_tokens=snake_case )
      rslt.append(snake_case )

      iter += 1
      if iter % interval == 0:
         __lowerCamelCase:				List[str]     =	time.time()
         logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
         __lowerCamelCase:				int     =	time.time()
   logger.info("""Finished binarization""" )
   logger.info(f'{len(snake_case )} examples processed.' )

   __lowerCamelCase:				Optional[Any]     =	f'{args.dump_file}.{args.tokenizer_name}.pickle'
   __lowerCamelCase:				List[str]     =	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      __lowerCamelCase:				str     =	[np.uintaa(snake_case ) for d in rslt]
   else:
      __lowerCamelCase:				List[Any]     =	[np.intaa(snake_case ) for d in rslt]
   random.shuffle(rslt_ )
   logger.info(f'Dump to {dp_file}' )
   with open(snake_case						,      """wb""" ) as handle:
      pickle.dump(rslt_						,      snake_case						,      protocol=pickle.HIGHEST_PROTOCOL )


if __name__ == "__main__":
   main()






from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


