


"""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 DeiTImageProcessor



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def __init__(self							,			a_							,			a_=7							,			a_=3							,			a_=18							,			a_=30							,			a_=4_00							,			a_=True							,			a_=None							,			a_=True							,			a_=None							,			a_=True							,			a_=[0.5, 0.5, 0.5]							,			a_=[0.5, 0.5, 0.5]							,			):





			'''simple docstring'''

			__snake_case      :		Tuple         =							size if size is not None else {'''height''': 20, '''width''': 20}
			__snake_case      :		Optional[Any]         =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

			__snake_case      :		int         =							parent
			__snake_case      :		Union[str, Any]         =							batch_size
			__snake_case      :		Optional[int]         =							num_channels
			__snake_case      :		Union[str, Any]         =							image_size
			__snake_case      :		Dict         =							min_resolution
			__snake_case      :		int         =							max_resolution
			__snake_case      :		str         =							do_resize
			__snake_case      :		Optional[int]         =							size
			__snake_case      :		int         =							do_center_crop
			__snake_case      :		Union[str, Any]         =							crop_size
			__snake_case      :		List[str]         =							do_normalize
			__snake_case      :		str         =							image_mean
			__snake_case      :		Optional[int]         =							image_std



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''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,
			}



@require_torch
@require_vision
class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =DeiTImageProcessor if is_vision_available() else None
		lowerCamelCase__           =True



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							DeiTImageProcessingTester(self   )



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[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'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_normalize'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_mean'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_std'''   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

			__snake_case      :		List[str]         =							self.image_processing_class.from_dict(self.image_processor_dict							,			size=42							,			crop_size=84   )
			self.assertEqual(image_processor.size							,			{'''height''': 42, '''width''': 42}   )
			self.assertEqual(image_processor.crop_size							,			{'''height''': 84, '''width''': 84}   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__snake_case      :		int         =							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      :		Dict         =							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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			__snake_case      :		List[str]         =							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      :		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
			__snake_case      :		Dict         =							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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__snake_case      :		List[str]         =							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      :		Tuple         =							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      :		Tuple         =							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   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

