




'''simple docstring'''


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def 					SCREAMING_SNAKE_CASE(   *__lowercase	)			-> int:
     A:		List[Any]          =		torch.cuda.device_count()
     print(F"""Device count: {num_processes}"""	)
     with patch_environment(
         world_size=__lowercase		,				master_addr='''127.0.01'''		,				master_port='''29500'''		,				mixed_precision=args[1].mixed_precision	):
          A:		Union[str, Any]          =		PrepareForLaunch(__lowercase		,				distributed_type='''MULTI_GPU'''	)
          torch.multiprocessing.start_processes(__lowercase		,				args=__lowercase		,				nprocs=__lowercase		,				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'])














'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )