






'''simple docstring'''
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class     _UpperCAmelCase  (						unittest.TestCase     ):
    # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
    __lowerCamelCase:				Tuple						    =      (ScoreSdeVeScheduler,)
    __lowerCamelCase:				Optional[int]						    =      ()




    @property
    def 			lowerCAmelCase__			(     self					:    List[Any]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       4
       lowercase_		:      Tuple          =       3
       lowercase_		:      str          =       8
       lowercase_		:      Tuple          =       8

       lowercase_		:      Optional[Any]          =       torch.rand((batch_size, num_channels, height, width)     )

       return sample




    @property
    def 			lowerCAmelCase__			(     self					:    Optional[int]     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       4
       lowercase_		:      str          =       3
       lowercase_		:      Tuple          =       8
       lowercase_		:      Optional[Any]          =       8

       lowercase_		:      Tuple          =       batch_size * num_channels * height * width
       lowercase_		:      Dict          =       torch.arange(a     )
       lowercase_		:      str          =       sample.reshape(a					,       a					,       a					,       a     )
       lowercase_		:      Union[str, Any]          =       sample / num_elems
       lowercase_		:      Any          =       sample.permute(3					,       0					,       1					,       2     )

       return sample




    def 			lowerCAmelCase__			(     self					:    List[Any]     ):






       '''simple docstring'''






       def model(a					:    int					,       a					:    Optional[Any]					,       *a					:    int     ):
          return sample * t / (t + 1)

       return model




    def 			lowerCAmelCase__			(     self					:    int					,       **a					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      Any          =       {
           "num_train_timesteps": 2_0_0_0,
           "snr": 0.15,
           "sigma_min": 0.01,
           "sigma_max": 1_3_4_8,
           "sampling_eps": 1e-5,
       }

       config.update(**a     )
       return config




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    Tuple=0					,       **a					:    int     ):






       '''simple docstring'''






       lowercase_		:      int          =       dict(self.forward_default_kwargs     )

       for scheduler_class in self.scheduler_classes:
          lowercase_		:      Optional[Any]          =       self.dummy_sample
          lowercase_		:      List[str]          =       0.1 * sample

          lowercase_		:      List[Any]          =       self.get_scheduler_config(**a     )
          lowercase_		:      Union[str, Any]          =       scheduler_class(**a     )

          with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(a     )
             lowercase_		:      Any          =       scheduler_class.from_pretrained(a     )

          lowercase_		:      Optional[Any]          =       scheduler.step_pred(
              a					,       a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample
          lowercase_		:      Tuple          =       new_scheduler.step_pred(
              a					,       a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample

          assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"

          lowercase_		:      int          =       scheduler.step_correct(a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample
          lowercase_		:      str          =       new_scheduler.step_correct(
              a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample

          assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler correction are not identical"




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    Dict=0					,       **a					:    Optional[int]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       dict(self.forward_default_kwargs     )
       kwargs.update(a     )

       for scheduler_class in self.scheduler_classes:
          lowercase_		:      Any          =       self.dummy_sample
          lowercase_		:      Optional[int]          =       0.1 * sample

          lowercase_		:      List[str]          =       self.get_scheduler_config()
          lowercase_		:      Dict          =       scheduler_class(**a     )

          with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(a     )
             lowercase_		:      Tuple          =       scheduler_class.from_pretrained(a     )

          lowercase_		:      Union[str, Any]          =       scheduler.step_pred(
              a					,       a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample
          lowercase_		:      Dict          =       new_scheduler.step_pred(
              a					,       a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample

          assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler outputs are not identical"

          lowercase_		:      str          =       scheduler.step_correct(a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample
          lowercase_		:      int          =       new_scheduler.step_correct(
              a					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample

          assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler correction are not identical"




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       for timesteps in [1_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]					,       [1, 1_0_0, 1_0_0_0]     ):
          self.check_over_configs(sigma_min=a					,       sigma_max=a     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       for t in [0.1, 0.5, 0.75]:
          self.check_over_forward(time_step=a     )




    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       dict(self.forward_default_kwargs     )

       lowercase_		:      int          =       self.scheduler_classes[0]
       lowercase_		:      List[str]          =       self.get_scheduler_config()
       lowercase_		:      Tuple          =       scheduler_class(**a     )

       lowercase_		:      Any          =       3

       lowercase_		:      Optional[Any]          =       self.dummy_model()
       lowercase_		:      Dict          =       self.dummy_sample_deter

       scheduler.set_sigmas(a     )
       scheduler.set_timesteps(a     )
       lowercase_		:      Optional[int]          =       torch.manual_seed(0     )

       for i, t in enumerate(scheduler.timesteps     ):
          lowercase_		:      List[str]          =       scheduler.sigmas[i]

          for _ in range(scheduler.config.correct_steps     ):
             with torch.no_grad():
                lowercase_		:      Any          =       model(a					,       a     )
             lowercase_		:      List[Any]          =       scheduler.step_correct(a					,       a					,       generator=a					,       **a     ).prev_sample

          with torch.no_grad():
             lowercase_		:      List[Any]          =       model(a					,       a     )

          lowercase_		:      str          =       scheduler.step_pred(a					,       a					,       a					,       generator=a					,       **a     )
          lowercase_      ,						lowercase_		:      str          =       output.prev_sample, output.prev_sample_mean

       lowercase_		:      Tuple          =       torch.sum(torch.abs(a     )     )
       lowercase_		:      List[Any]          =       torch.mean(torch.abs(a     )     )

       assert np.isclose(result_sum.item()					,       143_7275_8528.0     )
       assert np.isclose(result_mean.item()					,       1871_4530.0     )





    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       dict(self.forward_default_kwargs     )

       lowercase_		:      int          =       kwargs.pop("num_inference_steps"					,       a     )

       for scheduler_class in self.scheduler_classes:
          lowercase_		:      Dict          =       self.get_scheduler_config()
          lowercase_		:      Union[str, Any]          =       scheduler_class(**a     )

          lowercase_		:      Any          =       self.dummy_sample
          lowercase_		:      Optional[int]          =       0.1 * sample

          if num_inference_steps is not None and hasattr(a					,       "set_timesteps"     ):
             scheduler.set_timesteps(a     )
          elif num_inference_steps is not None and not hasattr(a					,       "set_timesteps"     ):
             lowercase_		:      List[str]          =       num_inference_steps

          lowercase_		:      int          =       scheduler.step_pred(a					,       0					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample
          lowercase_		:      Optional[int]          =       scheduler.step_pred(a					,       1					,       a					,       generator=torch.manual_seed(0     )					,       **a     ).prev_sample

          self.assertEqual(output_a.shape					,       sample.shape     )
          self.assertEqual(output_a.shape					,       output_a.shape     )












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

from bsa import BeautifulSoup
from requests import exceptions, get

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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




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

      lowercase_		:      List[str]          =       BASE_URL.format(_UpperCamelCase  ,						_UpperCamelCase							)
      lowercase_		:      Union[str, Any]          =       get(_UpperCamelCase							)

      # Is the response ok?
      response.raise_for_status()

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

      # This list will store the name and price.
      lowercase_		:      List[str]          =       []

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

            # Get price of the drug.
            lowercase_		:      List[str]          =       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__	=     input('Enter drug name: ').strip()
 UpperCamelCase__	=     input('Enter zip code: ').strip()

 UpperCamelCase__	=     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__	=     pharmacy_price['pharmacy_name']
   UpperCamelCase__	=     pharmacy_price['price']

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




