
"""simple docstring"""
from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			list | None:


     '''simple docstring'''

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

          __a:						int		    =					BASE_URL.format(snake_case__	,				snake_case__ )
          __a:						str		    =					get(snake_case__ )

          # Is the response ok?
          response.raise_for_status()

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

          # This list will store the name and price.
          __a:						Tuple		    =					[]

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

                    # Get price of the drug.
                    __a:						Dict		    =					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
   lowercase		:						int =					input('Enter drug name: ').strip()
   lowercase		:						int =					input('Enter zip code: ').strip()

   lowercase		:						Optional[Any] =					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:
         lowercase		:						Dict =					pharmacy_price['pharmacy_name']
         lowercase		:						Tuple =					pharmacy_price['price']

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


"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase		:						Tuple =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						['''pixel_values''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			bool = True						, snake_case__						:			Optional[Dict[str, int]] = None						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			bool = True						, snake_case__						:			Union[int, float] = 1 / 2_5_5						, snake_case__						:			bool = True						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												__a:						Dict		    =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:						Optional[Any]		    =					get_size_dict(snake_case__    )
												__a:						Union[str, Any]		    =					do_resize
												__a:						str		    =					do_rescale
												__a:						Tuple		    =					do_normalize
												__a:						Dict		    =					size
												__a:						str		    =					resample
												__a:						Any		    =					rescale_factor
												__a:						Optional[int]		    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:						int		    =					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			np.ndarray						, snake_case__						:			Dict[str, int]						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						int		    =					get_size_dict(snake_case__    )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
												return resize(
												    snake_case__						, size=(size["""height"""], size["""width"""])						, resample=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			np.ndarray						, snake_case__						:			float						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												return rescale(snake_case__						, scale=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			np.ndarray						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												return normalize(snake_case__						, mean=snake_case__						, std=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ImageInput						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Dict[str, int] = None						, snake_case__						:			PILImageResampling = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[float] = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[str, TensorType]] = None						, snake_case__						:			Union[str, ChannelDimension] = ChannelDimension.FIRST						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						Any		    =					do_resize if do_resize is not None else self.do_resize
												__a:						Any		    =					do_rescale if do_rescale is not None else self.do_rescale
												__a:						int		    =					do_normalize if do_normalize is not None else self.do_normalize
												__a:						Any		    =					resample if resample is not None else self.resample
												__a:						Any		    =					rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:						Dict		    =					image_mean if image_mean is not None else self.image_mean
												__a:						List[str]		    =					image_std if image_std is not None else self.image_std

												__a:						int		    =					size if size is not None else self.size
												__a:						Any		    =					get_size_dict(snake_case__    )

												__a:						List[str]		    =					make_list_of_images(snake_case__    )

												if not valid_images(snake_case__    ):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

												if do_resize and size is None:
																	raise ValueError("""Size must be specified if do_resize is True."""    )

												if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

												# All transformations expect numpy arrays.
												__a:						str		    =					[to_numpy_array(snake_case__    ) for image in images]

												if do_resize:
																	__a:						Dict		    =					[self.resize(image=snake_case__						, size=snake_case__						, resample=snake_case__    ) for image in images]

												if do_rescale:
																	__a:						str		    =					[self.rescale(image=snake_case__						, scale=snake_case__    ) for image in images]

												if do_normalize:
																	__a:						Dict		    =					[self.normalize(image=snake_case__						, mean=snake_case__						, std=snake_case__    ) for image in images]

												__a:						int		    =					[to_channel_dimension_format(snake_case__						, snake_case__    ) for image in images]

												__a:						str		    =					{"""pixel_values""": images}
												return BatchFeature(data=snake_case__						, tensor_type=snake_case__    )
