


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
  SCREAMING_SNAKE_CASE							    =							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')
  SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE							    =							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')
    SCREAMING_SNAKE_CASE							    =							rh.cluster(
        name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
    )
  else:
    SCREAMING_SNAKE_CASE							    =							rh.cluster(
        name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  SCREAMING_SNAKE_CASE							    =							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 ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout



