




'''simple docstring'''







import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





def 						snake_case_							(*_a     :							List[Any]					):
    UpperCAmelCase      	=		torch.cuda.device_count()
    print(F"Device count: {num_processes}"					)
    with patch_environment(
        world_size=_a   ,       master_addr='''127.0.01'''   ,       master_port='''29500'''   ,       mixed_precision=args[1].mixed_precision					):
        UpperCAmelCase      	=		PrepareForLaunch(_a   ,       distributed_type='''MULTI_GPU'''					)
        torch.multiprocessing.start_processes(_a   ,       args=_a   ,       nprocs=_a   ,       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'])










'''simple docstring'''







import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch


class       _a  (     unittest.TestCase							,							__a			):
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		load_tool('''speech-to-text'''    )
           self.tool.setup()
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )







       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(audio=torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )




