






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def __A   (				*__lowerCAmelCase     )->  Any:





						"""simple docstring"""



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

		# Define train args/config, run train function
		_a				     =  argparse.Namespace(cpu=False, mixed_precision='''fp16''')
		_a				     =  {'''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'])








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"regnet"
	UpperCamelCase__								=				["x", "y"]

	def __init__(							self	,   UpperCAmelCase=3	,   UpperCAmelCase=32	,   UpperCAmelCase=[128, 192, 512, 1088]	,   UpperCAmelCase=[2, 6, 12, 2]	,   UpperCAmelCase=64	,   UpperCAmelCase="y"	,   UpperCAmelCase="relu"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase   )
							if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types   )}"""   )
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      embedding_size
							_UpperCAmelCase     =      hidden_sizes
							_UpperCAmelCase     =      depths
							_UpperCAmelCase     =      groups_width
							_UpperCAmelCase     =      layer_type
							_UpperCAmelCase     =      hidden_act
							# always downsample in the first stage
							_UpperCAmelCase     =      True
