

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      		=     logging.getLogger(__name__)



def 				snake_case_()							->     Tuple:



		"""simple docstring"""






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

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

		logger.info('''Start encoding'''					)
		logger.info(F"""{len(lowerCAmelCase__					)} examples to process."""					)

		_snake_case						=							[]
		_snake_case						=							0
		_snake_case						=							10_000
		_snake_case						=							time.time()
		for text in data:
				_snake_case						=							F"""{bos} {text.strip()} {sep}"""
				_snake_case						=							tokenizer.encode(lowerCAmelCase__			,	add_special_tokens=lowerCAmelCase__					)
				rslt.append(lowerCAmelCase__					)

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

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


if __name__ == "__main__":
	main()




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

