
'''simple docstring'''



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





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








'''simple docstring'''



from typing import TYPE_CHECKING

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


__A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          =    ["DetrFeatureExtractor"]
				__A          =    ["DetrImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          =    [
				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DetrForObjectDetection",
				    "DetrForSegmentation",
				    "DetrModel",
				    "DetrPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

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






