


import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
      # setup instructions, if using on-demand hardware

      # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
      # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
      # Throw an error if user passes both BYO and on-demand cluster args
      # Otherwise, use default values
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()
      parser.add_argument('--user', type=str, default='ubuntu')
      parser.add_argument('--host', type=str, default='localhost')
      parser.add_argument('--key_path', type=str, default=None)
      parser.add_argument('--instance', type=str, default='V100:1')
      parser.add_argument('--provider', type=str, default='cheapest')
      parser.add_argument('--use_spot', type=bool, default=False)
      parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
      snake_case__       ,   snake_case__				:	List[Any]					       =					parser.parse_known_args()
      if args.host != "localhost":
            if args.instance != "V100:1" or args.provider != "cheapest":
                  raise ValueError('Cannot specify both BYO and on-demand cluster args')
            snake_case__				:	Optional[int]					       =					rh.cluster(
                name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
            )
      else:
            snake_case__				:	List[Any]					       =					rh.cluster(
                name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      snake_case__				:	Union[str, Any]					       =					args.example.rsplit('/', 1)[0]

      # Set up remote environment
      cluster.install_packages(['pip:./'])  # Installs transformers from local source
      # Note transformers is copied into the home directory on the remote machine, so we can install from there
      cluster.run([F'pip install -r transformers/examples/{example_dir}/requirements.txt'])
      cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

      # Run example. You can bypass the CLI wrapper and paste your own code here.
      cluster.run([F'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

      # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
      # from my_script... import train
      # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
      # launch_train_gpu = rh.function(fn=train,
      #                                system=gpu,
      #                                reqs=reqs,
      #                                name='train_bert_glue')
      #
      # We can pass in arguments just like we would to a function:
      # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
      #                  stream_logs=True)









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,
)





