
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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ChineseCLIPImageProcessor






class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[str]=7			,			lowerCAmelCase_     :							str=3			,			lowerCAmelCase_     :							str=1_8			,			lowerCAmelCase_     :							Optional[Any]=3_0			,			lowerCAmelCase_     :							Tuple=4_0_0			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							Any=None			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							Optional[int]=None			,			lowerCAmelCase_     :							Optional[Any]=True			,			lowerCAmelCase_     :							List[Any]=[0.48145466, 0.4578275, 0.40821073]			,			lowerCAmelCase_     :							int=[0.26862954, 0.26130258, 0.27577711]			,			lowerCAmelCase_     :							Optional[Any]=True			,			):

      """simple docstring"""
      _A:  Union[str, Any]					=       size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      _A:  int					=       crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
      _A:  str					=       parent
      _A:  Union[str, Any]					=       batch_size
      _A:  Any					=       num_channels
      _A:  str					=       image_size
      _A:  str					=       min_resolution
      _A:  Union[str, Any]					=       max_resolution
      _A:  int					=       do_resize
      _A:  Union[str, Any]					=       size
      _A:  Tuple					=       do_center_crop
      _A:  Optional[Any]					=       crop_size
      _A:  str					=       do_normalize
      _A:  Tuple					=       image_mean
      _A:  Union[str, Any]					=       image_std
      _A:  Any					=       do_convert_rgb


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=False			,			lowerCAmelCase_     :							List[Any]=False			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         _A:  Union[str, Any]					=       []
         for i in range(self.batch_size     ):
            image_inputs.append(
                np.random.randint(
                    2_5_5			,			size=(self.num_channels, self.max_resolution, self.max_resolution)			,			dtype=np.uinta     )     )
      else:
         _A:  Tuple					=       []
         for i in range(self.batch_size     ):
            _A			,  _A:  Dict					=       np.random.choice(np.arange(self.min_resolution			,			self.max_resolution     )			,			2     )
            image_inputs.append(np.random.randint(2_5_5			,			size=(self.num_channels, width, height)			,			dtype=np.uinta     )     )

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _A:  Any					=       [Image.fromarray(np.moveaxis(lowerCAmelCase_			,			0			,			-1     )     ) for x in image_inputs]

      if torchify:
         _A:  Optional[Any]					=       [torch.from_numpy(lowerCAmelCase_     ) for x in image_inputs]

      return image_inputs






@require_torch
@require_vision
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Union[str, Any] 				=    ChineseCLIPImageProcessor if is_vision_available() else None


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  List[str]					=       ChineseCLIPImageProcessingTester(self			,			do_center_crop=lowerCAmelCase_     )


   @property
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_resize'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''size'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_center_crop'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''center_crop'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_normalize'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_mean'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_std'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_convert_rgb'''     )     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.image_processing_class.from_dict(self.image_processor_dict     )
      self.assertEqual(image_processor.size			,			{'''height''': 2_2_4, '''width''': 2_2_4}     )
      self.assertEqual(image_processor.crop_size			,			{'''height''': 1_8, '''width''': 1_8}     )

      _A:  Any					=       self.image_processing_class.from_dict(self.image_processor_dict			,			size=4_2			,			crop_size=8_4     )
      self.assertEqual(image_processor.size			,			{'''shortest_edge''': 4_2}     )
      self.assertEqual(image_processor.crop_size			,			{'''height''': 8_4, '''width''': 8_4}     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  int					=       self.image_processing_class(**self.image_processor_dict     )
      # create random PIL images
      _A:  Optional[int]					=       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_     )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_			,			Image.Image     )

      # Test not batched input
      _A:  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
      _A:  Optional[int]					=       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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  Union[str, Any]					=       self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      _A:  Any					=       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_			,			numpify=lowerCAmelCase_     )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_			,			np.ndarray     )

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


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  Optional[Any]					=       self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      _A:  List[Any]					=       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_			,			torchify=lowerCAmelCase_     )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_			,			torch.Tensor     )

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






@require_torch
@require_vision
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[Any] 				=    ChineseCLIPImageProcessor if is_vision_available() else None


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  List[str]					=       ChineseCLIPImageProcessingTester(self			,			num_channels=4			,			do_center_crop=lowerCAmelCase_     )
      _A:  Union[str, Any]					=       3


   @property
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  str					=       self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_resize'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''size'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_center_crop'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''center_crop'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_normalize'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_mean'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''image_std'''     )     )
      self.assertTrue(hasattr(lowerCAmelCase_			,			'''do_convert_rgb'''     )     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      # Initialize image_processing
      _A:  int					=       self.image_processing_class(**self.image_processor_dict     )
      # create random PIL images
      _A:  Dict					=       self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_     )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_			,			Image.Image     )

      # Test not batched input
      _A:  List[Any]					=       image_processing(image_inputs[0]			,			return_tensors='''pt'''     ).pixel_values
      self.assertEqual(
          encoded_images.shape			,			(
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)

      # Test batched
      _A:  Dict					=       image_processing(lowerCAmelCase_			,			return_tensors='''pt'''     ).pixel_values
      self.assertEqual(
          encoded_images.shape			,			(
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,			)





from __future__ import annotations






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  List[str]					=       data
      _A:  Node | None					=       None
      _A:  Node | None					=       None






def   lowerCamelCase__							(					a      )      ->   None:  # In Order traversal of the tree
   if tree:
      display(tree.left      )
      print(tree.data      )
      display(tree.right      )






def   lowerCamelCase__							(					a      )      ->   int:
   return 1 + max(depth_of_tree(tree.left      )		,		depth_of_tree(tree.right      )      ) if tree else 0






def   lowerCamelCase__							(					a      )      ->   bool:
   if not tree:
      return True
   if tree.left and tree.right:
      return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
   else:
      return not tree.left and not tree.right






def   lowerCamelCase__							(					)      ->   None:  # Main function for testing.
   _A:  Optional[int]					=       Node(1      )
   _A:  int					=       Node(2      )
   _A:  str					=       Node(3      )
   _A:  Union[str, Any]					=       Node(4      )
   _A:  Dict					=       Node(5      )
   _A:  int					=       Node(6      )
   _A:  Optional[Any]					=       Node(7      )
   _A:  List[str]					=       Node(8      )
   _A:  int					=       Node(9      )

   print(is_full_binary_tree(a      )      )
   print(depth_of_tree(a      )      )
   print('''Tree is: '''      )
   display(a      )


if __name__ == "__main__":
   main()



