


from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A				:   List[str]		= "https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




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

            lowerCamelCase__		:    List[str]   =  BASE_URL.format(_A   , _A						)
            lowerCamelCase__		:    int   =  get(_A						)

            # Is the response ok?
            response.raise_for_status()

            # Scrape the data using bs4
            lowerCamelCase__		:    Dict   =  BeautifulSoup(response.text   , "html.parser"						)

            # This list will store the name and price.
            lowerCamelCase__		:    int   =  []

            # Fetch all the grids that contains the items.
            lowerCamelCase__		:    List[str]   =  soup.find_all("div"   , {"class": "grid-x pharmCard"}						)
            if grid_list and len(_A						) > 0:
                  for grid in grid_list:
                        # Get the pharmacy price.
                        lowerCamelCase__		:    Optional[int]   =  grid.find("p"   , {"class": "list-title"}						).text

                        # Get price of the drug.
                        lowerCamelCase__		:    Union[str, Any]   =  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
    A				:   Any		= input("Enter drug name: ").strip()
    A				:   Union[str, Any]		= input("Enter zip code: ").strip()

    A				:   list | None		= 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:
            A				:   Dict		= pharmacy_price["pharmacy_name"]
            A				:   Optional[int]		= pharmacy_price["price"]

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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "AutoFeatureExtractor"
       A__ 						=      "Speech2Text2Tokenizer"





       def __init__(       self							:      List[str] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             super().__init__(__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  self.feature_extractor
             lowerCamelCase__		:    Optional[int]   =  False





       def __call__(       self							:      List[str] ,				*__lowerCamelCase							:      Union[str, Any] ,				**__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             if self._in_target_context_manager:
                   return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase	)

             if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
                   lowerCamelCase__		:    List[Any]   =  kwargs.pop("raw_speech"	)
             else:
                   lowerCamelCase__		:    str   =  kwargs.pop("audio" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  kwargs.pop("sampling_rate" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  kwargs.pop("text" ,				__lowerCamelCase	)
             if len(__lowerCamelCase	) > 0:
                   lowerCamelCase__		:    Dict   =  args[0]
                   lowerCamelCase__		:    int   =  args[1:]

             if audio is None and text is None:
                   raise ValueError("You need to specify either an `audio` or `text` input to process."	)

             if audio is not None:
                   lowerCamelCase__		:    str   =  self.feature_extractor(__lowerCamelCase ,				*__lowerCamelCase ,				sampling_rate=__lowerCamelCase ,				**__lowerCamelCase	)
             if text is not None:
                   lowerCamelCase__		:    Optional[int]   =  self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase	)

             if text is None:
                   return inputs
             elif audio is None:
                   return encodings
             else:
                   lowerCamelCase__		:    Optional[int]   =  encodings["input_ids"]
                   return inputs





       def 						lowerCAmelCase					(       self							:      Any ,				*__lowerCamelCase							:      Any ,				**__lowerCamelCase							:      Any	):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict ,				*__lowerCamelCase							:      int ,				**__lowerCamelCase							:      List[str]	):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       @contextmanager
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             warnings.warn(
                 "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                 "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                 "your audio inputs, or in a separate call."	)
             lowerCamelCase__		:    int   =  True
             lowerCamelCase__		:    List[Any]   =  self.tokenizer
             yield
             lowerCamelCase__		:    Optional[int]   =  self.feature_extractor
             lowerCamelCase__		:    List[str]   =  False


