


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def   UpperCamelCase__   (    *UpperCAmelCase_			)						->							Union[str, Any]:




       '''simple docstring'''






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

   # Define train args/config, run train function
   UpperCamelCase__				       =			argparse.Namespace(cpu=False, mixed_precision='fp16')
   UpperCamelCase__				       =			{'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


UpperCamelCase__				       =			{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Wav2Vec2ConformerForAudioFrameClassification',
       'Wav2Vec2ConformerForCTC',
       'Wav2Vec2ConformerForPreTraining',
       'Wav2Vec2ConformerForSequenceClassification',
       'Wav2Vec2ConformerForXVector',
       'Wav2Vec2ConformerModel',
       'Wav2Vec2ConformerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)