import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			A__      (	snake_case_ :		str       ,				snake_case_ :		List[str]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		int=1_024      ):
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [], []

 SCREAMING_SNAKE_CASE__:   Dict=      list(zip(snake_case_       ,				snake_case_      )      )
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      sorted_examples[0]

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

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

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







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







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


if __name__ == "__main__":
      packer_cli()


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

