





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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

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

						UpperCAmelCase       		=    BASE_URL.format(_A      ,      _A			)
						UpperCAmelCase       		=    get(_A			)

						# Is the response ok?
						response.raise_for_status()

						# Scrape the data using bs4
						UpperCAmelCase       		=    BeautifulSoup(response.text      ,      '''html.parser'''			)

						# This list will store the name and price.
						UpperCAmelCase       		=    []

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

												# Get price of the drug.
												UpperCAmelCase       		=    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    :   int          =       input("Enter drug name: ").strip()
	__A    :   Optional[int]          =       input("Enter zip code: ").strip()

	__A    :   List[str]          =       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    :   Any          =       pharmacy_price["pharmacy_name"]
			__A    :   Dict          =       pharmacy_price["price"]

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










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			UpperCAmelCase       		=    np.stack((horizontal_flow, vertical_flow)      ,      2			)

			# Create a grid of all pixel coordinates and subtract the flow to get the
			# target pixels coordinates
			UpperCAmelCase       		=    np.stack(
			    np.meshgrid(np.arange(0      ,      image.shape[1]			)      ,      np.arange(0      ,      image.shape[0]			)			)      ,      2			)
			UpperCAmelCase       		=    np.round(grid - flow			).astype(np.intaa			)

			# Find the locations outside of the original image
			UpperCAmelCase       		=    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]			))
			UpperCAmelCase       		=    0

			UpperCAmelCase       		=    image[grid[:, :, 1], grid[:, :, 0]]

			# Set pixels at invalid locations to 0
			UpperCAmelCase       		=    0

			return warped





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

			# Initialize flow
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			UpperCAmelCase       		=    np.array([[-1, 1], [-1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[-1, -1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[1, 1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array(
			    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]			)

			# Iteratively refine the flow
			for _ in range(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

						# This updates the flow as proposed in the paper (Step 12)
						UpperCAmelCase       		=    (
						    derivative_x * avg_horizontal_velocity
						    + derivative_y * avg_vertical_velocity
						    + derivative_t
						)
						UpperCAmelCase       		=    update / (alpha**2 + derivative_x**2 + derivative_y**2)

						UpperCAmelCase       		=    avg_horizontal_velocity - derivative_x * update
						UpperCAmelCase       		=    avg_vertical_velocity - derivative_y * update

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()



