



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
)
_lowercase: Dict      			=		logging.getLogger(__name__)






def     a(      )		->       Optional[int]:
					"""simple docstring"""





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

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

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

					a					     =   []
					a					     =   0
					a					     =   1_0000
					a					     =   time.time()
					for text in data:
										a					     =   f'''{bos} {text.strip()} {sep}'''
										a					     =   tokenizer.encode(A ,     add_special_tokens=A       )
										rslt.append(A       )

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

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


if __name__ == "__main__":
				main()





def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
