




'''simple docstring'''







import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 						_lowercase						(      *lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			torch.cuda.device_count()
		print(f'''Device count: {num_processes}'''    )
		with patch_environment(
		    world_size=UpperCamelCase__		,				master_addr="127.0.01"		,				master_port="29500"		,				mixed_precision=args[1].mixed_precision    ):
				_snake_case      			=			PrepareForLaunch(UpperCamelCase__		,				distributed_type="MULTI_GPU"    )
				torch.multiprocessing.start_processes(UpperCamelCase__		,				args=UpperCamelCase__		,				nprocs=UpperCamelCase__		,				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				:		str				      =    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				:		Dict				      =    [
						    'pip:./',
						    'transformers',
						    'datasets',
						    'evaluate',
						    'tqdm',
						    'scipy',
						    'scikit-learn',
						    'tensorboard',
						    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
						]
						_lowerCamelCase				:		List[Any]				      =    rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

						# Define train args/config, run train function
						_lowerCamelCase				:		Optional[Any]				      =    argparse.Namespace(cpu=False, mixed_precision='fp16')
						_lowerCamelCase				:		Tuple				      =    {'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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





