


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def      _lowerCAmelCase  (							*lowerCAmelCase_							:Tuple							)->Any:
   '''simple docstring'''






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

   # Define train args/config, run train function
   SCREAMING_SNAKE_CASE		:Any											=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
   SCREAMING_SNAKE_CASE		:List[Any]											=       {'''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'])








import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:Dict											=       {'''UserAgent''': UserAgent().random}
SCREAMING_SNAKE_CASE		:Dict											=       '''https://www.mywaifulist.moe/random'''



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:str							)->None:
   '''simple docstring'''






   snake_case_         =   requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							)
   with open(lowerCAmelCase_						,  "wb"							) as file:
      file.write(image.content							)



def      _lowerCAmelCase  (							)->tuple[str, str, str]:
   '''simple docstring'''






   snake_case_         =   BeautifulSoup(requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							).text						,  "html.parser"							)
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:title"}							).attrs["content"]
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:image"}							).attrs["content"]
   snake_case_         =   soup.find("p"						,  id="description"							).get_text()
   snake_case_      ,      snake_case_         =   os.path.splitext(os.path.basename(lowerCAmelCase_							)							)
   snake_case_         =   title.strip().replace(" "						,  "_"							)
   snake_case_         =   F'''{image_title}{image_extension}'''
   save_image(lowerCAmelCase_						,  lowerCAmelCase_							)
   return (title, description, image_title)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE		:Optional[Any]											=       random_anime_character()
   print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')




