



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    ['''image_processor''', '''tokenizer''']
				__lowerCamelCase			=    '''AutoImageProcessor'''
				__lowerCamelCase			=    '''AutoTokenizer'''




				def __init__(							self						,    _snake_case=None						,    _snake_case=None						,    **_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   None
									if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""						,    _snake_case						,    )
														_lowerCAmelCase      =   kwargs.pop("""feature_extractor"""		)

									_lowerCAmelCase      =   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
														raise ValueError("""You need to specify an `image_processor`."""		)
									if tokenizer is None:
														raise ValueError("""You need to specify a `tokenizer`."""		)

									super().__init__(_snake_case						,    _snake_case		)
									_lowerCAmelCase      =   self.image_processor
									_lowerCAmelCase      =   False




				def __call__(							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





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

									_lowerCAmelCase      =   kwargs.pop("""images"""						,    _snake_case		)
									_lowerCAmelCase      =   kwargs.pop("""text"""						,    _snake_case		)
									if len(_snake_case		) > 0:
														_lowerCAmelCase      =   args[0]
														_lowerCAmelCase      =   args[1:]

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

									if images is not None:
														_lowerCAmelCase      =   self.image_processor(_snake_case						,    *_snake_case						,    **_snake_case		)
									if text is not None:
														_lowerCAmelCase      =   self.tokenizer(_snake_case						,    **_snake_case		)

									if text is None:
														return inputs
									elif images is None:
														return encodings
									else:
														_lowerCAmelCase      =   encodings["""input_ids"""]
														return inputs




				def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





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




				def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





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




				@contextmanager
				def 							snake_case       (							self		):



									"""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 images inputs, or in a separate call."""		)
									_lowerCAmelCase      =   True
									_lowerCAmelCase      =   self.tokenizer
									yield
									_lowerCAmelCase      =   self.image_processor
									_lowerCAmelCase      =   False




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,    _snake_case						,    )
									return self.image_processor_class




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,    _snake_case						,    )
									return self.image_processor








from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

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

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

										# 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(snake_case      ) > 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
	A__     				=	input("""Enter drug name: """).strip()
	A__     				=	input("""Enter zip code: """).strip()

	A__     				=	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__     				=	pharmacy_price["""pharmacy_name"""]
			A__     				=	pharmacy_price["""price"""]

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



