


"""simple docstring"""




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 				lowercase_		(		_A      :   Optional[int]=None   , _A      :   Tuple=None						):

      """simple docstring"""




      return field(default_factory=lambda: default   , metadata=_A						)







@dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      field(
           metadata={"help": "The csv file to plot."}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Disable logarithmic scale when plotting"}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={
               "help": "Whether the csv file has training results or inference results. Defaults to inference results."
           }							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,  )
       A__ 						=      list_field(
           default=lowercase__							,  metadata={"help": "List of model names that are used instead of the ones in the csv file."})



def 				lowercase_		(		_A      :   List[str]						):

      """simple docstring"""




      try:
            int(_A						)
            return True
      except ValueError:
            return False

def 				lowercase_		(		_A      :   Dict						):

      """simple docstring"""




      try:
            float(_A						)
            return True
      except ValueError:
            return False







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Any ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  args
             lowerCamelCase__		:    Optional[int]   =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

             with open(self.args.csv_file ,				newline=""	) as csv_file:
                   lowerCamelCase__		:    Tuple   =  csv.DictReader(__lowerCamelCase	)
                   for row in reader:
                         lowerCamelCase__		:    Dict   =  row["model"]
                         self.result_dict[model_name]["bsz"].append(int(row["batch_size"]	)	)
                         self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]	)	)
                         if can_convert_to_int(row["result"]	):
                               # value is not None
                               lowerCamelCase__		:    List[str]   =  int(row["result"]	)
                         elif can_convert_to_float(row["result"]	):
                               # value is not None
                               lowerCamelCase__		:    str   =  float(row["result"]	)





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  plt.subplots()
             lowerCamelCase__		:    Union[str, Any]   =  "Time usage" if self.args.is_time else "Memory usage"
             lowerCamelCase__		:    Union[str, Any]   =  title_str + " for training" if self.args.is_train else title_str + " for inference"

             if not self.args.no_log_scale:
                   # set logarithm scales
                   ax.set_xscale("log"	)
                   ax.set_yscale("log"	)

             for axis in [ax.xaxis, ax.yaxis]:
                   axis.set_major_formatter(ScalarFormatter()	)

             for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
                   lowerCamelCase__		:    Any   =  sorted(set(self.result_dict[model_name]["bsz"]	)	)
                   lowerCamelCase__		:    str   =  sorted(set(self.result_dict[model_name]["seq_len"]	)	)
                   lowerCamelCase__		:    List[Any]   =  self.result_dict[model_name]["result"]

                   (lowerCamelCase__)		:    Any   =  (
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   lowerCamelCase__		:    int   =  (
                       model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                   )

                   for inner_loop_value in inner_loop_array:
                         if self.args.plot_along_batch:
                               lowerCamelCase__		:    Union[str, Any]   =  np.asarray(
                                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=__lowerCamelCase ,				)
                         else:
                               lowerCamelCase__		:    Optional[Any]   =  np.asarray(
                                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

                         (lowerCamelCase__)		:    str   =  (
                             ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                         )

                         lowerCamelCase__		:    Optional[Any]   =  np.asarray(__lowerCamelCase ,				__lowerCamelCase	)[: len(__lowerCamelCase	)]
                         plt.scatter(
                             __lowerCamelCase ,				__lowerCamelCase ,				label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"	)
                         plt.plot(__lowerCamelCase ,				__lowerCamelCase ,				"--"	)

                   title_str += f" {label_model_name} vs."

             lowerCamelCase__		:    List[Any]   =  title_str[:-4]
             lowerCamelCase__		:    Union[str, Any]   =  "Time in s" if self.args.is_time else "Memory in MB"

             # plot
             plt.title(__lowerCamelCase	)
             plt.xlabel(__lowerCamelCase	)
             plt.ylabel(__lowerCamelCase	)
             plt.legend()

             if self.args.figure_png_file is not None:
                   plt.savefig(self.args.figure_png_file	)
             else:
                   plt.show()



def 				lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:    List[str]   =  HfArgumentParser(_A						)
      lowerCamelCase__		:    Union[str, Any]   =  parser.parse_args_into_dataclasses()[0]
      lowerCamelCase__		:    Tuple   =  Plot(args=_A						)
      plot.plot()


if __name__ == "__main__":
    main()






from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A				:   List[str]		= "https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




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

            lowerCamelCase__		:    List[str]   =  BASE_URL.format(_A   , _A						)
            lowerCamelCase__		:    int   =  get(_A						)

            # Is the response ok?
            response.raise_for_status()

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

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

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

                        # Get price of the drug.
                        lowerCamelCase__		:    Union[str, Any]   =  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				:   Any		= input("Enter drug name: ").strip()
    A				:   Union[str, Any]		= input("Enter zip code: ").strip()

    A				:   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:
            A				:   Dict		= pharmacy_price["pharmacy_name"]
            A				:   Optional[int]		= pharmacy_price["price"]

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


