





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)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)