
'''simple docstring'''

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   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     str      )					->       list | None:







			'''simple docstring'''



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

						lowerCAmelCase__								=							BASE_URL.format(UpperCamelCase_						,	UpperCamelCase_      )
						lowerCAmelCase__								=							get(UpperCamelCase_      )

						# 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(UpperCamelCase_      ) > 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
		UpperCAmelCase__   : Tuple					=		input("Enter drug name: ").strip()
		UpperCAmelCase__   : List[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[int]					=		pharmacy_price["pharmacy_name"]
						UpperCAmelCase__   : Dict					=		pharmacy_price["price"]

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







'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
			model.save_pretrained(UpperCamelCase_      )


if __name__ == "__main__":
		UpperCAmelCase__   : List[str]					=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





