






"""simple docstring"""


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def     snake_case						(A_   :str			,    A_   :int			,    A_   :List[Any]			,    A_   :int=1_0_2_4   ):







  '''simple docstring'''

  a,  a  :    Optional[Any]       =     [], []

  a  :    Optional[Any]       =     list(zip(A_			,    A_   )   )
  a,  a  :    Any       =     sorted_examples[0]

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

  for src, tgt in tqdm(sorted_examples[1:]   ):
    a  :    List[str]       =     new_src + ' ' + src
    a  :    Tuple       =     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_   )
      a,  a  :    Dict       =     src, tgt
    else:  # can fit, keep adding
      a,  a  :    Optional[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     snake_case						(A_   :str			,    A_   :Path			,    A_   :int			,    A_   :int   ):







  '''simple docstring'''

  a  :    Tuple       =     Path(A_   )
  save_path.mkdir(exist_ok=A_   )
  for split in ["train"]:
    a,  a  :    Optional[Any]       =     data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    a  :    Optional[Any]       =     [x.rstrip() for x in Path(A_   ).open().readlines()]
    a  :    int       =     [x.rstrip() for x in Path(A_   ).open().readlines()]
    a,  a  :    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"]:
    a,  a  :    Dict       =     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     snake_case						():







  '''simple docstring'''

  a  :    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=1_2_8   )
  parser.add_argument('--data_dir'			,    type=A_   )
  parser.add_argument('--save_path'			,    type=A_   )
  a  :    int       =     parser.parse_args()
  a  :    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()












"""simple docstring"""


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments




