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







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size'	,  [None, 400 * 2**20, 600 * 2**20]		)
@pytest.mark.parametrize('input_in_memory_max_size'	,  ['default', 0, 100 * 2**20, 900 * 2**20]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config	,  'IN_MEMORY_MAX_SIZE'	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		__lowerCAmelCase      :						Tuple						      =				dataset_size < in_memory_max_size
	else:
		__lowerCAmelCase      :						str						      =				False
	__lowerCAmelCase      :						Optional[int]						      =				is_small_dataset(_UpperCamelCase		)
	assert result == expected