



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


snake_case_    					=	"""scaler.pt"""
snake_case_    					=	"""pytorch_model"""
snake_case_    					=	"""random_states"""
snake_case_    					=	"""optimizer"""
snake_case_    					=	"""scheduler"""
snake_case_    					=	"""pytorch_model.bin"""
snake_case_    					=	"""pytorch_model.bin.index.json"""
snake_case_    					=	"""model.safetensors"""
snake_case_    					=	"""model.safetensors.index.json"""
snake_case_    					=	"""1.10.2"""
snake_case_    					=	"""py38"""
snake_case_    					=	"""4.17.0"""
snake_case_    					=	["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
snake_case_    					=	["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
snake_case_    					=	["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
snake_case_    					=	["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
snake_case_    					=	["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
snake_case_    					=	"""2.0.1"""
snake_case_    					=	["""pdsh""", """standard""", """openmpi""", """mvapich"""]
snake_case_    					=	["""default""", """reduce-overhead""", """max-autotune"""]

snake_case_    					=	{""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
snake_case_    					=	[
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

snake_case_    					=	["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
snake_case_    					=	["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]





