code: 
  '''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'])
  
  
  
  
  
  
  
style_context: 
  '''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__)
  
  
  
  
  
  
  