






'''simple docstring'''


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def 						__magic_name__(			*_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			torch.cuda.device_count()
						print(f"Device count: {num_processes}"       )
						with patch_environment(
						    world_size=_A    ,   master_addr="""127.0.01"""    ,   master_port="""29500"""    ,   mixed_precision=args[1].mixed_precision       ):
												UpperCamelCase__       =			PrepareForLaunch(_A    ,   distributed_type="""MULTI_GPU"""       )
												torch.multiprocessing.start_processes(_A    ,   args=_A    ,   nprocs=_A    ,   start_method="""spawn"""       )


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					lowerCamelCase_					:			int     						=				rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					lowerCamelCase_					:			List[Any]     						=				[
					    '''pip:./''',
					    '''transformers''',
					    '''datasets''',
					    '''evaluate''',
					    '''tqdm''',
					    '''scipy''',
					    '''scikit-learn''',
					    '''tensorboard''',
					    '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
					]
					lowerCamelCase_					:			List[str]     						=				rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

					# Define train args/config, run train function
					lowerCamelCase_					:			Union[str, Any]     						=				argparse.Namespace(cpu=False, mixed_precision='''fp16''')
					lowerCamelCase_					:			Optional[int]     						=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
