code: 
  
  
  
  
  
  
  from urllib.error import HTTPError
  
  from bsa import BeautifulSoup
  from requests import exceptions, get
  
  lowerCamelCase				  =  '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      try:
          # Has user provided both inputs?
          if not drug_name or not zip_code:
              return None
  
          _lowercase     =	BASE_URL.format(_lowercase,      _lowercase      )
          _lowercase     =	get(_lowercase      )
  
          # Is the response ok?
          response.raise_for_status()
  
          # Scrape the data using bs4
          _lowercase     =	BeautifulSoup(response.text,      'html.parser'      )
  
          # This list will store the name and price.
          _lowercase     =	[]
  
          # Fetch all the grids that contains the items.
          _lowercase     =	soup.find_all('div',      {'class': 'grid-x pharmCard'}      )
          if grid_list and len(_lowercase      ) > 0:
              for grid in grid_list:
                  # Get the pharmacy price.
                  _lowercase     =	grid.find('p',      {'class': 'list-title'}      ).text
  
                  # Get price of the drug.
                  _lowercase     =	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
     lowerCamelCase				  =  input('''Enter drug name: ''').strip()
     lowerCamelCase				  =  input('''Enter zip code: ''').strip()
  
     lowerCamelCase				  =  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:
           lowerCamelCase				  =  pharmacy_price['''pharmacy_name''']
           lowerCamelCase				  =  pharmacy_price['''price''']
  
           print(f"Pharmacy: {name} Price: {price}")
     else:
        print(f"No results found for {drug_name}")
  
style_context: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  ['''UtteranceLevel''']
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )
  
      _lowercase     =	checkpoint['Downstream']
  
      _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
      _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
      _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
          _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )
  
      if hf_congfig.use_weighted_layer_sum:
          _lowercase     =	checkpoint['Featurizer']['weights']
  
      _lowercase     =	downstream_dict['projector.weight']
      _lowercase     =	downstream_dict['projector.bias']
      _lowercase     =	downstream_dict['model.post_net.linear.weight']
      _lowercase     =	downstream_dict['model.post_net.linear.bias']
  
      hf_feature_extractor.save_pretrained(_lowercase      )
      hf_model.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     lowerCamelCase										=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  