

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_       	=	logging.getLogger(__name__)


def   __SCREAMING_SNAKE_CASE					():
						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=SCREAMING_SNAKE_CASE__  ,		default='''data/dump.txt'''  ,		help='''The path to the data.'''       )
						parser.add_argument('''--tokenizer_type'''  ,		type=SCREAMING_SNAKE_CASE__  ,		default='''bert'''  ,		choices=['''bert''', '''roberta''', '''gpt2''']       )
						parser.add_argument('''--tokenizer_name'''  ,		type=SCREAMING_SNAKE_CASE__  ,		default='''bert-base-uncased'''  ,		help='''The tokenizer to use.'''       )
						parser.add_argument('''--dump_file'''  ,		type=SCREAMING_SNAKE_CASE__  ,		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(SCREAMING_SNAKE_CASE__       )} examples to process.'''       )

						snake_case_   =	[]
						snake_case_   =	0
						snake_case_   =	10000
						snake_case_   =	time.time()
						for text in data:
												snake_case_   =	F'''{bos} {text.strip()} {sep}'''
												snake_case_   =	tokenizer.encode(SCREAMING_SNAKE_CASE__  ,		add_special_tokens=SCREAMING_SNAKE_CASE__       )
												rslt.append(SCREAMING_SNAKE_CASE__       )

												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(SCREAMING_SNAKE_CASE__       )} 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(SCREAMING_SNAKE_CASE__       ) for d in rslt]
						else:
												snake_case_   =	[np.intaa(SCREAMING_SNAKE_CASE__       ) for d in rslt]
						random.shuffle(rslt_       )
						logger.info(F'''Dump to {dp_file}'''       )
						with open(SCREAMING_SNAKE_CASE__  ,		'''wb'''       ) as handle:
												pickle.dump(rslt_  ,		SCREAMING_SNAKE_CASE__  ,		protocol=pickle.HIGHEST_PROTOCOL       )


if __name__ == "__main__":
					main()





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


