

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 					lowerCAmelCase__							(	)			->      Union[str, Any]:




				'''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=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."      )
				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(SCREAMING_SNAKE_CASE_      )} examples to process.'      )

				A__        =			[]
				A__        =			0
				A__        =			1_0_0_0_0
				A__        =			time.time()
				for text in data:
								A__        =			F'{bos} {text.strip()} {sep}'
								A__        =			tokenizer.encode(SCREAMING_SNAKE_CASE_	,       add_special_tokens=SCREAMING_SNAKE_CASE_      )
								rslt.append(SCREAMING_SNAKE_CASE_      )

								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(SCREAMING_SNAKE_CASE_      )} examples processed.'      )

				A__        =			F'{args.dump_file}.{args.tokenizer_name}.pickle'
				A__        =			tokenizer.vocab_size
				if vocab_size < (1 << 1_6):
								A__        =			[np.uintaa(SCREAMING_SNAKE_CASE_      ) for d in rslt]
				else:
								A__        =			[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__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

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

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

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

