






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





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

	# Define train args/config, run train function
	lowercase  							= argparse.Namespace(cpu=False, mixed_precision="fp16")
	lowercase  							= {'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 typing import TYPE_CHECKING

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	lowercase  							= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





