


'''simple docstring'''







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = True					,	__SCREAMING_SNAKE_CASE = math.inf					,	__SCREAMING_SNAKE_CASE = -math.inf					,	__SCREAMING_SNAKE_CASE = math.inf					,	__SCREAMING_SNAKE_CASE = -math.inf					,	__SCREAMING_SNAKE_CASE = False					,	__SCREAMING_SNAKE_CASE = 100					,	__SCREAMING_SNAKE_CASE = 0.01					,	__SCREAMING_SNAKE_CASE = 1					,	)					->       Optional[Any]:
     """simple docstring"""




     __a   			= False
     __a   			= search_prob
     __a   			= start_temperate
     __a   			= []
     __a   			= 0
     __a   			= None

     while not search_end:
          __a   			= current_state.score()
          if best_state is None or current_score > best_state.score():
               __a   			= current_state
          scores.append(__A		)
          iterations += 1
          __a   			= None
          __a   			= current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __a   			= random.randint(0					,	len(__A		) - 1		)  # picking a random neighbor
               __a   			= neighbors.pop(__A		)
               __a   			= picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __a   			= change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __a   			= picked_neighbor
               else:
                    __a   			= (math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __a   			= picked_neighbor
          __a   			= current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __a   			= True
          else:
               __a   			= next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(__A		)					,	__A		)
          plt.xlabel("""Iterations"""		)
          plt.ylabel("""Function values"""		)
          plt.show()
     return best_state


if __name__ == "__main__":




 def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Any:
      """simple docstring"""




      return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_   =      simulated_annealing(
     prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_   =      simulated_annealing(
     prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
     f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )




 def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       List[str]:
      """simple docstring"""




      return (3 * x**2) - (6 * y)

 SCREAMING_SNAKE_CASE_   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_   =      simulated_annealing(prob, find_max=False, visualization=True)
 print(
     'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )

 SCREAMING_SNAKE_CASE_   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_   =      simulated_annealing(prob, find_max=True, visualization=True)



 print(
     'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
     f"""{local_min.score()}"""
 )











'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """simple docstring"""



     _a           =    F'''https://www.amazon.in/laptop/s?k={product}'''
     _a           =    {
         '''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
         '''Accept-Language''': '''en-US, en;q=0.5''',
     }
     _a           =    BeautifulSoup(requests.get(__A    ,      headers=__A).text)
     # Initialize a Pandas dataframe with the column titles
     _a           =    DataFrame(
         columns=[
             '''Product Title''',
             '''Product Link''',
             '''Current Price of the product''',
             '''Product Rating''',
             '''MRP of the product''',
             '''Discount''',
         ])
     # Loop through each entry and store them in the dataframe
     for item, _ in zip_longest(
         soup.find_all(
             '''div'''    ,      attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''}    ,      )    ,      soup.find_all('''div'''    ,      attrs={'''class''': '''a-row a-size-base a-color-base'''})    ,      ):
          try:
               _a           =    item.ha.text
               _a           =    '''https://www.amazon.in/''' + item.ha.a['''href''']
               _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-offscreen'''}).text
               try:
                    _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-icon-alt'''}).text
               except AttributeError:
                    _a           =    '''Not available'''
               try:
                    _a           =    (
                        '''₹'''
                        + item.find(
                            '''span'''    ,      attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
                    )
               except AttributeError:
                    _a           =    ''''''
               try:
                    _a           =    float(
                        (
                            (
                                float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                                - float(product_price.strip('''₹''').replace(''','''    ,      ''''''))
                            )
                            / float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                        )
                        * 100)
               except ValueError:
                    _a           =    float('''nan''')
          except AttributeError:
               pass
          _a           =    [
              product_title,
              product_link,
              product_price,
              product_rating,
              product_mrp,
              discount,
          ]
     _a           =    ''' '''
     _a           =    ''' '''
     data_frame.index += 1
     return data_frame


if __name__ == "__main__":
  lowercase_									=    "headphones"
  get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



