"""simple docstring"""
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







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

 # Define train args/config, run train function
 lowercase__   			=      argparse.Namespace(cpu=False, mixed_precision='fp16')
 lowercase__   			=      {'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 dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput




@dataclass
class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    None
							SCREAMING_SNAKE_CASE		  =    None






class  _snake_case	(    unittest.TestCase							):






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Dict		     =       ModelOutputTest(a=3_0     )
												self.assertEqual(x.a    ,			3_0     )
												self.assertIsNone(x.b     )
												self.assertIsNone(x.c     )
												with self.assertRaises(snake_case__     ):
																	__a:    int		     =       x.d






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Tuple		     =       ModelOutputTest(a=3_0    ,			b=1_0     )
												self.assertEqual(x[0]    ,			3_0     )
												self.assertEqual(x[1]    ,			1_0     )
												self.assertEqual(x[:2]    ,			(3_0, 1_0)     )
												self.assertEqual(x[:]    ,			(3_0, 1_0)     )

												__a:    Tuple		     =       ModelOutputTest(a=3_0    ,			c=1_0     )
												self.assertEqual(x[0]    ,			3_0     )
												self.assertEqual(x[1]    ,			1_0     )
												self.assertEqual(x[:2]    ,			(3_0, 1_0)     )
												self.assertEqual(x[:]    ,			(3_0, 1_0)     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    List[str]		     =       ModelOutputTest(a=3_0    ,			b=1_0     )
												self.assertEqual(x["""a"""]    ,			3_0     )
												self.assertEqual(x["""b"""]    ,			1_0     )
												with self.assertRaises(snake_case__     ):
																	__a:    Union[str, Any]		     =       x["""c"""]

												__a:    str		     =       ModelOutputTest(a=3_0    ,			c=1_0     )
												self.assertEqual(x["""a"""]    ,			3_0     )
												self.assertEqual(x["""c"""]    ,			1_0     )
												with self.assertRaises(snake_case__     ):
																	__a:    List[str]		     =       x["""b"""]






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    str		     =       ModelOutputTest(a=3_0     )
												self.assertEqual(list(x.keys()     )    ,			["""a"""]     )
												self.assertEqual(list(x.values()     )    ,			[3_0]     )
												self.assertEqual(list(x.items()     )    ,			[("""a""", 3_0)]     )
												self.assertEqual(list(snake_case__     )    ,			["""a"""]     )

												__a:    Optional[Any]		     =       ModelOutputTest(a=3_0    ,			b=1_0     )
												self.assertEqual(list(x.keys()     )    ,			["""a""", """b"""]     )
												self.assertEqual(list(x.values()     )    ,			[3_0, 1_0]     )
												self.assertEqual(list(x.items()     )    ,			[("""a""", 3_0), ("""b""", 1_0)]     )
												self.assertEqual(list(snake_case__     )    ,			["""a""", """b"""]     )

												__a:    Union[str, Any]		     =       ModelOutputTest(a=3_0    ,			c=1_0     )
												self.assertEqual(list(x.keys()     )    ,			["""a""", """c"""]     )
												self.assertEqual(list(x.values()     )    ,			[3_0, 1_0]     )
												self.assertEqual(list(x.items()     )    ,			[("""a""", 3_0), ("""c""", 1_0)]     )
												self.assertEqual(list(snake_case__     )    ,			["""a""", """c"""]     )

												with self.assertRaises(snake_case__     ):
																	__a:    List[Any]		     =       x.update({"""d""": 2_0}     )
												with self.assertRaises(snake_case__     ):
																	del x["a"]
												with self.assertRaises(snake_case__     ):
																	__a:    Union[str, Any]		     =       x.pop("""a"""     )
												with self.assertRaises(snake_case__     ):
																	__a:    List[Any]		     =       x.setdefault("""d"""    ,			3_2     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												__a:    List[str]		     =       ModelOutputTest(a=3_0     )
												__a:    str		     =       1_0
												self.assertEqual(x.a    ,			1_0     )
												self.assertEqual(x["""a"""]    ,			1_0     )






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[str]		     =       ModelOutputTest(a=3_0     )
												__a:    Any		     =       1_0
												self.assertEqual(x.a    ,			1_0     )
												self.assertEqual(x["""a"""]    ,			1_0     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    List[Any]		     =       ModelOutputTest({"""a""": 3_0, """b""": 1_0}     )
												self.assertEqual(list(x.keys()     )    ,			["""a""", """b"""]     )
												self.assertEqual(x.a    ,			3_0     )
												self.assertEqual(x.b    ,			1_0     )






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       ModelOutputTest([("""a""", 3_0), ("""b""", 1_0)]     )
												self.assertEqual(list(x.keys()     )    ,			["""a""", """b"""]     )
												self.assertEqual(x.a    ,			3_0     )
												self.assertEqual(x.b    ,			1_0     )

												with self.assertRaises(snake_case__     ):
																	__a:    str		     =       ModelOutputTest([("""a""", 3_0), (1_0, 1_0)]     )

												__a:    str		     =       ModelOutputTest(a=(3_0, 3_0)     )
												self.assertEqual(list(x.keys()     )    ,			["""a"""]     )
												self.assertEqual(x.a    ,			(3_0, 3_0)     )
