
"""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 A_			:




  '''simple docstring'''







  def __init__(	self       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					key
     UpperCAmelCase_      :      Any      =					freq







  def __str__(	self						):

     """simple docstring"""
     return F"""Node(key={self.key}, freq={self.freq})"""







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   if i > j or i < 0 or j > len(snake_case__						) - 1:
      return

   UpperCAmelCase_      :      str      =					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(snake_case__,	snake_case__,	snake_case__,	node - 1,	key[node],	snake_case__						)
   print_binary_search_tree(snake_case__,	snake_case__,	node + 1,	snake_case__,	key[node],	snake_case__						)







def 			__a  (	__lowerCamelCase						):
   nodes.sort(key=lambda __lowerCamelCase						: node.key						)

   UpperCAmelCase_      :      Optional[Any]      =					len(snake_case__						)

   UpperCAmelCase_      :      int      =					[nodes[i].key for i in range(snake_case__						)]
   UpperCAmelCase_      :      Union[str, Any]      =					[nodes[i].freq for i in range(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.
   UpperCAmelCase_      :      Tuple      =					[[freqs[i] if i == j else 0 for j in range(snake_case__						)] for i in range(snake_case__						)]
   # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
   # array
   UpperCAmelCase_      :      List[str]      =					[[freqs[i] if i == j else 0 for j in range(snake_case__						)] for i in range(snake_case__						)]
   # stores tree roots that will be used later for constructing binary search tree
   UpperCAmelCase_      :      Optional[Any]      =					[[i if i == j else 0 for j in range(snake_case__						)] for i in range(snake_case__						)]

   for interval_length in range(2,	n + 1						):
      for i in range(n - interval_length + 1						):
         UpperCAmelCase_      :      Union[str, Any]      =					i + interval_length - 1

         UpperCAmelCase_      :      Dict      =					sys.maxsize  # set the value to "infinity"
         UpperCAmelCase_      :      int      =					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
            UpperCAmelCase_      :      Optional[Any]      =					dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
            UpperCAmelCase_      :      List[str]      =					dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
            UpperCAmelCase_      :      Optional[Any]      =					left + total[i][j] + right

            if dp[i][j] > cost:
               UpperCAmelCase_      :      Tuple      =					cost
               UpperCAmelCase_      :      Optional[int]      =					r

   print("Binary search tree nodes:"						)
   for node in nodes:
      print(snake_case__						)

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







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


if __name__ == "__main__":
    main()


"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class A_			(unittest.TestCase   ):




  '''simple docstring'''







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					tempfile.mkdtemp()

     UpperCAmelCase_      :      List[Any]      =					CLIPImageProcessor()
     UpperCAmelCase_      :      Any      =					BertTokenizer.from_pretrained(
         "hf-internal-testing/tiny-random-BertModel"       ,     model_input_names=["input_ids", "attention_mask"]						)

     UpperCAmelCase_      :      Tuple      =					GitProcessor(lowercase_       ,     lowercase_						)

     processor.save_pretrained(self.tmpdirname						)







  def  UpperCamelCase__      (	self       ,     **lowercase_						):

     """simple docstring"""
     return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).tokenizer







  def  UpperCamelCase__      (	self       ,     **lowercase_						):

     """simple docstring"""
     return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).image_processor







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     shutil.rmtree(self.tmpdirname						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta						)]

     UpperCAmelCase_      :      Optional[Any]      =					[Image.fromarray(np.moveaxis(lowercase_       ,     0       ,     -1						)						) for x in image_inputs]

     return image_inputs







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      List[Any]      =					GitProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     UpperCAmelCase_      :      Optional[int]      =					self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"						)
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor(do_normalize=lowercase_       ,     padding_value=1.0						)

     UpperCAmelCase_      :      str      =					GitProcessor.from_pretrained(
         self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=lowercase_       ,     padding_value=1.0						)

     self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()						)
     self.assertIsInstance(processor.tokenizer       ,     lowercase_						)

     self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()						)
     self.assertIsInstance(processor.image_processor       ,     lowercase_						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Dict      =					self.get_image_processor()
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      Union[str, Any]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      List[Any]      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Optional[Any]      =					image_processor(lowercase_       ,     return_tensors="np"						)
     UpperCAmelCase_      :      int      =					processor(images=lowercase_       ,     return_tensors="np"						)

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1E-2						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      Tuple      =					self.get_tokenizer()

     UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      Optional[Any]      =					"lower newer"

     UpperCAmelCase_      :      Any      =					processor(text=lowercase_						)

     UpperCAmelCase_      :      int      =					tokenizer(lowercase_       ,     return_token_type_ids=lowercase_						)

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					self.get_image_processor()
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      Optional[int]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      List[str]      =					"lower newer"
     UpperCAmelCase_      :      Dict      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Any      =					processor(text=lowercase_       ,     images=lowercase_						)

     self.assertListEqual(list(inputs.keys()						)       ,     ["input_ids", "attention_mask", "pixel_values"]						)

     # test if it raises when no input is passed
     with pytest.raises(lowercase_						):
        processor()







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      str      =					self.get_tokenizer()

     UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      Tuple      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase_      :      Any      =					processor.batch_decode(lowercase_						)
     UpperCAmelCase_      :      Optional[Any]      =					tokenizer.batch_decode(lowercase_						)

     self.assertListEqual(lowercase_       ,     lowercase_						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      Optional[Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      str      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      int      =					"lower newer"
     UpperCAmelCase_      :      Tuple      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Optional[Any]      =					processor(text=lowercase_       ,     images=lowercase_						)

     # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
     self.assertListEqual(list(inputs.keys()						)       ,     ["input_ids", "attention_mask", "pixel_values"]						)


