



import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase	       =	'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
    pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
    pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   Union[str, Any]=None     )     ->					Dict:
   require_version(deps[pkg]	,					snake_case__     )








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



