





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def      a    (   *_lowercase :  str							)  ->str:
		UpperCamelCase__								=						torch.cuda.device_count()
		print(F"""Device count: {num_processes}"""							)
		with patch_environment(
		    world_size=_SCREAMING_SNAKE_CASE			,    master_addr="""127.0.01"""			,    master_port="""29500"""			,    mixed_precision=args[1].mixed_precision							):
				UpperCamelCase__								=						PrepareForLaunch(_SCREAMING_SNAKE_CASE			,    distributed_type="""MULTI_GPU"""							)
				torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,    args=_SCREAMING_SNAKE_CASE			,    nprocs=_SCREAMING_SNAKE_CASE			,    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
				__lowerCAmelCase 	=      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
				__lowerCAmelCase 	=      [
				    "pip:./",
				    "transformers",
				    "datasets",
				    "evaluate",
				    "tqdm",
				    "scipy",
				    "scikit-learn",
				    "tensorboard",
				    "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
				]
				__lowerCAmelCase 	=      rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

				# Define train args/config, run train function
				__lowerCAmelCase 	=      argparse.Namespace(cpu=False, mixed_precision="fp16")
				__lowerCAmelCase 	=      {"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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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


