


class      lowerCamelCase	:

       def __init__(			self	,						a__					):






        '''simple docstring'''






        snake_case   : int	  =      data
        snake_case   : BinaryTreeNode | None	  =      None
        snake_case   : BinaryTreeNode | None	  =      None







def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase       )		->BinaryTreeNode | None:


 '''simple docstring'''
 if node is None:
  snake_case   : Dict	  =      BinaryTreeNode(__lowerCamelCase       )
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  snake_case   : str	  =      insert(node.left_child ,     __lowerCamelCase       )
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  snake_case   : str	  =      insert(node.right_child ,     __lowerCamelCase       )
 return node






def    _UpperCAmelCase	(   __lowerCamelCase       )		->list[int]:  # if node is None,return


 '''simple docstring'''
 if node:
  snake_case   : Optional[Any]	  =      inorder(node.left_child       )
  snake_case   : Dict	  =      [*inorder_array, node.data]
  snake_case   : List[str]	  =      inorder_array + inorder(node.right_child       )
 else:
  snake_case   : str	  =      []
 return inorder_array






def    _UpperCAmelCase	(   )		->BinaryTreeNode | None:


 '''simple docstring'''
 snake_case   : Any	  =      insert(__lowerCamelCase ,     15       )
 insert(__lowerCamelCase ,     10       )
 insert(__lowerCamelCase ,     25       )
 insert(__lowerCamelCase ,     6       )
 insert(__lowerCamelCase ,     14       )
 insert(__lowerCamelCase ,     20       )
 insert(__lowerCamelCase ,     60       )
 return root






def    _UpperCAmelCase	(   )		->None:


 '''simple docstring'''
 # main function
 snake_case   : Optional[int]	  =      make_tree()
 print('''Printing values of binary search tree in Inorder Traversal.'''       )
 inorder(__lowerCamelCase       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()










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 MobileNetVaImageProcessor

class      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : str	  =      size if size is not None else {'''shortest_edge''': 20}
        snake_case   : List[str]	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        snake_case   : Optional[Any]	  =      parent
        snake_case   : Dict	  =      batch_size
        snake_case   : List[str]	  =      num_channels
        snake_case   : Union[str, Any]	  =      image_size
        snake_case   : Optional[Any]	  =      min_resolution
        snake_case   : Union[str, Any]	  =      max_resolution
        snake_case   : Optional[int]	  =      do_resize
        snake_case   : Optional[int]	  =      size
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      MobileNetVaImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      MobileNetVaImageProcessingTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''center_crop'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 20}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 18, '''width''': 18}					)

        snake_case   : Dict	  =      self.image_processing_class.from_dict(self.image_processor_dict	,						size=42	,						crop_size=84					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 42}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 84, '''width''': 84}					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						Image.Image					)

        # Test not batched input
        snake_case   : List[str]	  =      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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						numpify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						np.ndarray					)

        # Test not batched input
        snake_case   : Dict	  =      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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        snake_case   : List[Any]	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						torchify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						torch.Tensor					)

        # Test not batched input
        snake_case   : List[str]	  =      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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : Any	  =      image_processing(a__	,						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.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)






