




from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

UpperCAmelCase__						:       Optional[int]							=    'https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		list | None:





  """simple docstring"""





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

    _lowercase: str						     =    BASE_URL.format(UpperCamelCase					,     UpperCamelCase							)
    _lowercase: int						     =    get(UpperCamelCase							)

    # Is the response ok?
    response.raise_for_status()

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

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

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

        # Get price of the drug.
        _lowercase: Optional[int]						     =    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
   UpperCAmelCase__						:       Tuple							=    input('Enter drug name: ').strip()
   UpperCAmelCase__						:       Optional[Any]							=    input('Enter zip code: ').strip()

   UpperCAmelCase__						:       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:
         UpperCAmelCase__						:       Optional[Any]							=    pharmacy_price['pharmacy_name']
         UpperCAmelCase__						:       Optional[Any]							=    pharmacy_price['price']

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






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
