





"""simple docstring"""




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a=1024		)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				Optional[Any]        =[], []

  lowerCamelCase__:				Optional[int]        =list(zip(_a     ,   _a		)		)
  lowerCamelCase__:				List[Any]        =sorted_examples[0]

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

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

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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		Any:







  """simple docstring"""





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





def 					lowerCAmelCase_			(	)					->		int:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =argparse.ArgumentParser()
  parser.add_argument("--tok_name"     ,   type=_a     ,   help="like facebook/bart-large-cnn,t5-base, etc."		)
  parser.add_argument("--max_seq_len"     ,   type=_a     ,   default=128		)
  parser.add_argument("--data_dir"     ,   type=_a		)
  parser.add_argument("--save_path"     ,   type=_a		)
  lowerCamelCase__:				Union[str, Any]        =parser.parse_args()
  lowerCamelCase__:				List[str]        =AutoTokenizer.from_pretrained(args.tok_name		)
  return pack_data_dir(_a     ,   Path(args.data_dir		)     ,   args.max_seq_len     ,   args.save_path		)


if __name__ == "__main__":
     packer_cli()








import argparse
import logging
import pickle
from collections import Counter


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__)

if __name__ == "__main__":
     __A     =    argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0522, type=int)
     __A     =    parser.parse_args()

     logger.info(f'Loading data from {args.data_file}')
     with open(args.data_file, "rb") as fp:
          __A     =    pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     __A     =    Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __A     =    [0] * args.vocab_size
     for k, v in counter.items():
          __A     =    v

     logger.info(f'Dump to {args.token_counts_dump}')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

