

"""simple docstring"""





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def        _lowerCAmelCase      (						*UpperCAmelCase     :							List[str]				):


					'''simple docstring'''






					UpperCamelCase__		:    str         =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				):
										UpperCamelCase__		:    Any         =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
				_SCREAMING_SNAKE_CASE       :  Tuple        = 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
				_SCREAMING_SNAKE_CASE       :  List[str]        = [
				    """pip:./""",
				    """transformers""",
				    """datasets""",
				    """evaluate""",
				    """tqdm""",
				    """scipy""",
				    """scikit-learn""",
				    """tensorboard""",
				    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
				]
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

				# Define train args/config, run train function
				_SCREAMING_SNAKE_CASE       :  int        = argparse.Namespace(cpu=False, mixed_precision="""fp16""")
				_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
				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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Optional[int]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


