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 MobileViTImageProcessor







class __snake_case				(						unittest.TestCase		):




   def __init__(    self			:				int       ,	_lowercase			:				str       ,	_lowercase			:				Optional[Any]=7       ,	_lowercase			:				Tuple=3       ,	_lowercase			:				Union[str, Any]=18       ,	_lowercase			:				Optional[int]=30       ,	_lowercase			:				Tuple=4_00       ,	_lowercase			:				Optional[Any]=True       ,	_lowercase			:				Optional[Any]=None       ,	_lowercase			:				List[Any]=True       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				str=True       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      size if size is not None else {"shortest_edge": 20}
     SCREAMING_SNAKE_CASE__										=      crop_size if crop_size is not None else {"height": 18, "width": 18}
     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      image_size
     SCREAMING_SNAKE_CASE__										=      min_resolution
     SCREAMING_SNAKE_CASE__										=      max_resolution
     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      size
     SCREAMING_SNAKE_CASE__										=      do_center_crop
     SCREAMING_SNAKE_CASE__										=      crop_size
     SCREAMING_SNAKE_CASE__										=      do_flip_channel_order




   def 	__a						(    self			:				Optional[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_flip_channel_order": self.do_flip_channel_order,
     }







@require_torch
@require_vision
class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							MobileViTImageProcessor if is_vision_available() else None




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      MobileViTImageProcessingTester(self      )




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      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"""      )      )
     self.assertTrue(hasattr(_a       ,	"""do_flip_channel_order"""      )      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      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}      )

     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				str      ):



     """simple docstring"""






     pass




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_a      )
     for image in image_inputs:
       self.assertIsInstance(_a       ,	Image.Image      )

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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"""],
         )       ,	)


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	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.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
