





"""simple docstring"""





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
  __A											=						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""")
  __A											=						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""")
    __A											=						rh.cluster(
        name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
    )
  else:
    __A											=						rh.cluster(
        name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  __A											=						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)






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

