






"""simple docstring"""



import sys
from random import randint

class 	a					:







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	key
      lowercase							   =	freq







    def __str__(      self	):
      return F'Node(key={self.key}, freq={self.freq})'


def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  List[Any]		, __snake_case      :  Union[str, Any]		, __snake_case      :  Any		, __snake_case      :  Optional[Any]		, __snake_case      :  List[str]		, __snake_case      :  Tuple   ):





  '''simple docstring'''





  if i > j or i < 0 or j > len(__snake_case   ) - 1:
    return

  lowercase							   =	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 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  List[str]   ):





  '''simple docstring'''





  nodes.sort(key=lambda __snake_case   : node.key   )

  lowercase							   =	len(__snake_case   )

  lowercase							   =	[nodes[i].key for i in range(__snake_case   )]
  lowercase							   =	[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.
  lowercase							   =	[[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
  lowercase							   =	[[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
  lowercase							   =	[[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   ):
      lowercase							   =	i + interval_length - 1

      lowercase							   =	sys.maxsize  # set the value to "infinity"
      lowercase							   =	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
        lowercase							   =	dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
        lowercase							   =	dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
        lowercase							   =	left + total[i][j] + right

        if dp[i][j] > cost:
          lowercase							   =	cost
          lowercase							   =	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 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





  lowercase							   =	[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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


