




'''simple docstring'''






# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class 					lowercase_			:





     def __init__(    self    :				Optional[int]    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Optional[Any] ):
           snake_case__					:      Optional[Any]  			=						key
           snake_case__					:      Any  			=						freq





     def __str__(    self    :				Optional[int] ):
           return F"Node(key={self.key}, freq={self.freq})"






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      if i > j or i < 0 or j > len(__SCREAMING_SNAKE_CASE		) - 1:
            return

      snake_case__					:      int  			=						root[i][j]
      if parent == -1:  # root does not have a parent
            print(f"{key[node]} is the root of the binary search tree."		)
      elif is_left:
            print(f"{key[node]} is the left child of key {parent}."		)
      else:
            print(f"{key[node]} is the right child of key {parent}."		)

      print_binary_search_tree(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			node - 1							,			key[node]							,			__SCREAMING_SNAKE_CASE		)
      print_binary_search_tree(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			node + 1							,			__SCREAMING_SNAKE_CASE							,			key[node]							,			__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      nodes.sort(key=lambda __SCREAMING_SNAKE_CASE		: node.key		)

      snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Tuple  			=						[nodes[i].key for i in range(__SCREAMING_SNAKE_CASE		)]
      snake_case__					:      Any  			=						[nodes[i].freq for i in range(__SCREAMING_SNAKE_CASE		)]

      # This 2D array stores the overall tree cost (which's as minimized as possible);
      # for a single key, cost is equal to frequency of the key.
      snake_case__					:      Any  			=						[[freqs[i] if i == j else 0 for j in range(__SCREAMING_SNAKE_CASE		)] for i in range(__SCREAMING_SNAKE_CASE		)]
      # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
      # array
      snake_case__					:      Tuple  			=						[[freqs[i] if i == j else 0 for j in range(__SCREAMING_SNAKE_CASE		)] for i in range(__SCREAMING_SNAKE_CASE		)]
      # stores tree roots that will be used later for constructing binary search tree
      snake_case__					:      str  			=						[[i if i == j else 0 for j in range(__SCREAMING_SNAKE_CASE		)] for i in range(__SCREAMING_SNAKE_CASE		)]

      for interval_length in range(2							,			n + 1		):
            for i in range(n - interval_length + 1		):
                  snake_case__					:      str  			=						i + interval_length - 1

                  snake_case__					:      Tuple  			=						sys.maxsize  # set the value to "infinity"
                  snake_case__					:      Tuple  			=						total[i][j - 1] + freqs[j]

                  # Apply Knuth's optimization
                  # Loop without optimization: for r in range(i, j + 1):
                  for r in range(root[i][j - 1]							,			root[i + 1][j] + 1		):  # r is a temporal root
                        snake_case__					:      List[str]  			=						dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                        snake_case__					:      Any  			=						dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                        snake_case__					:      List[Any]  			=						left + total[i][j] + right

                        if dp[i][j] > cost:
                              snake_case__					:      Tuple  			=						cost
                              snake_case__					:      List[str]  			=						r

      print('Binary search tree nodes:'		)
      for node in nodes:
            print(__SCREAMING_SNAKE_CASE		)

      print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."		)
      print_binary_search_tree(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			0							,			n - 1							,			-1							,			__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		)						->      List[Any]:
      # A sample binary search tree
      snake_case__					:      Dict  			=						[Node(__SCREAMING_SNAKE_CASE							,			randint(1							,			50		)		) for i in range(10							,			0							,			-1		)]
      find_optimal_binary_search_tree(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()






'''simple docstring'''






import bsa
import requests






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      dict[str, str]:
      return {
          "name": soup.ha.a.text,
          "genre": soup.find('span'							,			class_='genre'		).text.strip(),
          "rating": soup.strong.text,
          "page_link": f"https://www.imdb.com{soup.a.get('href'		)}",
      }






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 5		)						->      tuple:
      snake_case__					:      Tuple  			=						int(float(__SCREAMING_SNAKE_CASE		)		)
      if num_movies < 1:
            return ()
      snake_case__					:      Any  			=						(
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      snake_case__					:      Union[str, Any]  			=						bsa.BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE		).content							,			'html.parser'		)
      return tuple(
          get_movie_data_from_soup(__SCREAMING_SNAKE_CASE		)
          for movie in source.find_all('div'							,			class_='lister-item mode-advanced'		)		)


if __name__ == "__main__":
      import json

      A_ 			=		int(input("How many movies would you like to see? "))
      print(
          ", ".join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )
