





'''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_					:    List[str]      	=			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_					:    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')
										lowerCAmelCase_					:    List[Any]      	=			rh.cluster(
										    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
										)
					else:
										lowerCAmelCase_					:    Dict      	=			rh.cluster(
										    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
										)
					lowerCAmelCase_					:    Optional[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 typing import TYPE_CHECKING

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


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

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