'''simple docstring'''




from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

snake_case  =			'''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):

      try:
            # Has user provided both inputs?
            if not drug_name or not zip_code:
                  return None

            _lowerCAmelCase		   =   BASE_URL.format(_lowerCamelCase  ,  _lowerCamelCase    )
            _lowerCAmelCase		   =   get(_lowerCamelCase    )

            # Is the response ok?
            response.raise_for_status()

            # Scrape the data using bs4
            _lowerCAmelCase		   =   BeautifulSoup(response.text  ,  'html.parser'    )

            # This list will store the name and price.
            _lowerCAmelCase		   =   []

            # Fetch all the grids that contains the items.
            _lowerCAmelCase		   =   soup.find_all('div'  ,  {'class': 'grid-x pharmCard'}    )
            if grid_list and len(_lowerCamelCase    ) > 0:
                  for grid in grid_list:
                        # Get the pharmacy price.
                        _lowerCAmelCase		   =   grid.find('p'  ,  {'class': 'list-title'}    ).text

                        # Get price of the drug.
                        _lowerCAmelCase		   =   grid.find('span'  ,  {'p', 'price price-large'}    ).text

                        pharmacy_price_list.append(
                            {
                                'pharmacy_name': pharmacy_name,
                                'price': price,
                            }    )

            return pharmacy_price_list

      except (HTTPError, exceptions.RequestException, ValueError):
            return None


if __name__ == "__main__":
 # Enter a drug name and a zip code
 snake_case  =			input('''Enter drug name: ''').strip()
 snake_case  =			input('''Enter zip code: ''').strip()

 snake_case  =			fetch_pharmacy_and_price_list(
     drug_name, zip_code
 )

 if pharmacy_price_list:
  print(f'''\nSearch results for {drug_name} at location {zip_code}:''')
  for pharmacy_price in pharmacy_price_list:
   snake_case  =			pharmacy_price['''pharmacy_name''']
   snake_case  =			pharmacy_price['''price''']

   print(f'''Pharmacy: {name} Price: {price}''')
 else:
  print(f'''No results found for {drug_name}''')






'''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_   ( *_lowerCamelCase			:							Union[str, Any]    ):
      _lowerCAmelCase		   =   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    ):
            _lowerCAmelCase		   =   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
 snake_case  =			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
 snake_case  =			[
     '''pip:./''',
     '''transformers''',
     '''datasets''',
     '''evaluate''',
     '''tqdm''',
     '''scipy''',
     '''scikit-learn''',
     '''tensorboard''',
     '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
 ]
 snake_case  =			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

 # Define train args/config, run train function
 snake_case  =			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
 snake_case  =			{'''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'])





