



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


A__     				=	getLogger(__name__)


A__     				=	'cuda' if torch.cuda.is_available() else 'cpu'


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case = 8     ,						snake_case = DEFAULT_DEVICE     ,						snake_case=False     ,						snake_case="summarization"     ,						snake_case=None     ,						**snake_case     ,						):





     """simple docstring"""

     _lowerCAmelCase      =   Path(lowerCamelCase_      ).open("""w"""     ,						encoding="""utf-8"""      )
     _lowerCAmelCase      =   str(lowerCamelCase_      )
     _lowerCAmelCase      =   AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_      ).to(lowerCamelCase_      )
     if fpaa:
          _lowerCAmelCase      =   model.half()

     _lowerCAmelCase      =   AutoTokenizer.from_pretrained(lowerCamelCase_      )
     logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'      )  # if this is wrong, check config.model_type.

     _lowerCAmelCase      =   time.time()
     # update config with task specific params
     use_task_specific_params(lowerCamelCase_     ,						lowerCamelCase_      )
     if prefix is None:
          _lowerCAmelCase      =   prefix or getattr(model.config     ,						"""prefix"""     ,						""""""      ) or """"""
     for examples_chunk in tqdm(list(chunks(lowerCamelCase_     ,						lowerCamelCase_      )      )      ):
          _lowerCAmelCase      =   [prefix + text for text in examples_chunk]
          _lowerCAmelCase      =   tokenizer(lowerCamelCase_     ,						return_tensors="""pt"""     ,						truncation=lowerCamelCase_     ,						padding="""longest"""      ).to(lowerCamelCase_      )
          _lowerCAmelCase      =   model.generate(
              input_ids=batch.input_ids     ,						attention_mask=batch.attention_mask     ,						**lowerCamelCase_     ,						)
          _lowerCAmelCase      =   tokenizer.batch_decode(lowerCamelCase_     ,						skip_special_tokens=lowerCamelCase_     ,						clean_up_tokenization_spaces=lowerCamelCase_      )
          for hypothesis in dec:
               fout.write(hypothesis + """\n"""      )
               fout.flush()
     fout.close()
     _lowerCAmelCase      =   int(time.time() - start_time      )  # seconds
     _lowerCAmelCase      =   len(lowerCamelCase_      )
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,						4      )}


def 		_UpperCAmelCase    (				):





     """simple docstring"""

     return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""      )


def 		_UpperCAmelCase    (				snake_case=True      ):





     """simple docstring"""

     _lowerCAmelCase      =   argparse.ArgumentParser()
     parser.add_argument("""model_name"""     ,						type=lowerCamelCase_     ,						help="""like facebook/bart-large-cnn,t5-base, etc."""      )
     parser.add_argument("""input_path"""     ,						type=lowerCamelCase_     ,						help="""like cnn_dm/test.source"""      )
     parser.add_argument("""save_path"""     ,						type=lowerCamelCase_     ,						help="""where to save summaries"""      )
     parser.add_argument("""--reference_path"""     ,						type=lowerCamelCase_     ,						required=lowerCamelCase_     ,						help="""like cnn_dm/test.target"""      )
     parser.add_argument("""--score_path"""     ,						type=lowerCamelCase_     ,						required=lowerCamelCase_     ,						default="""metrics.json"""     ,						help="""where to save metrics"""      )
     parser.add_argument("""--device"""     ,						type=lowerCamelCase_     ,						required=lowerCamelCase_     ,						default=lowerCamelCase_     ,						help="""cuda, cuda:1, cpu etc."""      )
     parser.add_argument(
         """--prefix"""     ,						type=lowerCamelCase_     ,						required=lowerCamelCase_     ,						default=lowerCamelCase_     ,						help="""will be added to the begininng of src examples"""      )
     parser.add_argument("""--task"""     ,						type=lowerCamelCase_     ,						default="""summarization"""     ,						help="""used for task_specific_params + metrics"""      )
     parser.add_argument("""--bs"""     ,						type=lowerCamelCase_     ,						default=8     ,						required=lowerCamelCase_     ,						help="""batch size"""      )
     parser.add_argument(
         """--n_obs"""     ,						type=lowerCamelCase_     ,						default=-1     ,						required=lowerCamelCase_     ,						help="""How many observations. Defaults to all."""      )
     parser.add_argument("""--fp16"""     ,						action="""store_true"""      )
     parser.add_argument("""--dump-args"""     ,						action="""store_true"""     ,						help="""print the custom hparams with the results"""      )
     parser.add_argument(
         """--info"""     ,						nargs="""?"""     ,						type=lowerCamelCase_     ,						const=datetime_now()     ,						help=(
             """use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."""
             """ lang=en-ru. If no value is passed, the current datetime string will be used."""
         )     ,						)
     # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
     _lowerCAmelCase     ,     _lowerCAmelCase      =   parser.parse_known_args()
     _lowerCAmelCase      =   parse_numeric_n_bool_cl_kwargs(lowerCamelCase_      )
     if parsed_args and verbose:
          print(F'parsed the following generate kwargs: {parsed_args}'      )
     with open(args.input_path      ) as f:
          _lowerCAmelCase      =   [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
     if args.n_obs > 0:
          _lowerCAmelCase      =   examples[: args.n_obs]
     Path(args.save_path      ).parent.mkdir(exist_ok=lowerCamelCase_      )
     if args.reference_path is None and Path(args.score_path      ).exists():
          warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.'      )
     _lowerCAmelCase      =   generate_summaries_or_translations(
         lowerCamelCase_     ,						args.save_path     ,						args.model_name     ,						batch_size=args.bs     ,						device=args.device     ,						fpaa=args.fpaa     ,						task=args.task     ,						prefix=args.prefix     ,						**lowerCamelCase_     ,						)

     if args.reference_path is None:
          return {}

     # Compute scores
     _lowerCAmelCase      =   calculate_bleu if """translation""" in args.task else calculate_rouge
     _lowerCAmelCase      =   [x.rstrip() for x in open(args.save_path      ).readlines()]
     _lowerCAmelCase      =   [x.rstrip() for x in open(args.reference_path      ).readlines()][: len(lowerCamelCase_      )]
     _lowerCAmelCase      =   score_fn(lowerCamelCase_     ,						lowerCamelCase_      )
     scores.update(lowerCamelCase_      )

     if args.dump_args:
          scores.update(lowerCamelCase_      )
     if args.info:
          _lowerCAmelCase      =   args.info

     if verbose:
          print(lowerCamelCase_      )

     if args.score_path is not None:
          json.dump(lowerCamelCase_     ,						open(args.score_path     ,						"""w"""      )      )

     return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)








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}")



