

import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def        __lowercase   (  *_A      )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Any	  = 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      ):
            SCREAMING_SNAKE_CASE			:   List[str]	  = 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
  UpperCAmelCase__			:  Optional[Any]					     =     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
  UpperCAmelCase__			:  str					     =     [
      """pip:./""",
      """transformers""",
      """datasets""",
      """evaluate""",
      """tqdm""",
      """scipy""",
      """scikit-learn""",
      """tensorboard""",
      """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
  ]
  UpperCAmelCase__			:  int					     =     rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

  # Define train args/config, run train function
  UpperCAmelCase__			:  Union[str, Any]					     =     argparse.Namespace(cpu=False, mixed_precision="""fp16""")
  UpperCAmelCase__			:  Any					     =     {"""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


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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





