




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
)
lowerCamelCase							:						Dict					       =  logging.getLogger(__name__)

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       argparse.ArgumentParser(
          description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'	)
      parser.add_argument('--file_path'      ,				type=lowercase      ,				default='data/dump.txt'      ,				help='The path to the data.'	)
      parser.add_argument('--tokenizer_type'      ,				type=lowercase      ,				default='bert'      ,				choices=['bert', 'roberta', 'gpt2']	)
      parser.add_argument('--tokenizer_name'      ,				type=lowercase      ,				default='bert-base-uncased'      ,				help='The tokenizer to use.'	)
      parser.add_argument('--dump_file'      ,				type=lowercase      ,				default='data/dump'      ,				help='The dump file prefix.'	)
      lowerCamelCase_							=       parser.parse_args()

      logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""	)
      if args.tokenizer_type == "bert":
            lowerCamelCase_							=       BertTokenizer.from_pretrained(args.tokenizer_name	)
            lowerCamelCase_							=       tokenizer.special_tokens_map['cls_token']  # `[CLS]`
            lowerCamelCase_							=       tokenizer.special_tokens_map['sep_token']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            lowerCamelCase_							=       RobertaTokenizer.from_pretrained(args.tokenizer_name	)
            lowerCamelCase_							=       tokenizer.special_tokens_map['cls_token']  # `<s>`
            lowerCamelCase_							=       tokenizer.special_tokens_map['sep_token']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            lowerCamelCase_							=       GPTaTokenizer.from_pretrained(args.tokenizer_name	)
            lowerCamelCase_							=       tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
            lowerCamelCase_							=       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_							=       fp.readlines()

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

      lowerCamelCase_							=       []
      lowerCamelCase_							=       0
      lowerCamelCase_							=       1_00_00
      lowerCamelCase_							=       time.time()
      for text in data:
            lowerCamelCase_							=       f"""{bos} {text.strip()} {sep}"""
            lowerCamelCase_							=       tokenizer.encode(lowercase      ,				add_special_tokens=lowercase	)
            rslt.append(lowercase	)

            iter += 1
            if iter % interval == 0:
                  lowerCamelCase_							=       time.time()
                  logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""	)
                  lowerCamelCase_							=       time.time()
      logger.info('Finished binarization'	)
      logger.info(f"""{len(lowercase	)} examples processed."""	)

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


if __name__ == "__main__":
 main()











'''simple docstring'''



import sys

__UpperCamelCase   :							List[str]         =   (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = N     )	->      int:

   """simple docstring"""
   __a					  =     -sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE__     ) - 12     ):
      __a					  =     1
      for j in range(13     ):
         product *= int(n[i + j]     )
      if product > largest_product:
         __a					  =     product
   return largest_product


if __name__ == "__main__":
 print(f"""{solution() = }""")