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 EfficientNetImageProcessor




class 					_snake_case						(						unittest.TestCase				):
					def __init__(  self:				Tuple	,  __lowerCamelCase:				List[str]	,  __lowerCamelCase:				Optional[Any]=13	,  __lowerCamelCase:				Optional[int]=3	,  __lowerCamelCase:				Any=18	,  __lowerCamelCase:				Optional[Any]=30	,  __lowerCamelCase:				Optional[Any]=4_00	,  __lowerCamelCase:				str=True	,  __lowerCamelCase:				Tuple=None	,  __lowerCamelCase:				Tuple=True	,  __lowerCamelCase:				Tuple=[0.5, 0.5, 0.5]	,  __lowerCamelCase:				Optional[int]=[0.5, 0.5, 0.5]	,  )    ->				Tuple:
												__UpperCAmelCase						:			Optional[Any]         =   size if size is not None else {"height": 18, "width": 18}
												__UpperCAmelCase						:			List[Any]         =   parent
												__UpperCAmelCase						:			Dict         =   batch_size
												__UpperCAmelCase						:			int         =   num_channels
												__UpperCAmelCase						:			Any         =   image_size
												__UpperCAmelCase						:			Dict         =   min_resolution
												__UpperCAmelCase						:			Optional[Any]         =   max_resolution
												__UpperCAmelCase						:			Any         =   do_resize
												__UpperCAmelCase						:			Dict         =   size
												__UpperCAmelCase						:			Optional[int]         =   do_normalize
												__UpperCAmelCase						:			Optional[int]         =   image_mean
												__UpperCAmelCase						:			Tuple         =   image_std





					def    _lowerCamelCase (  self:				Dict     )    ->				Dict:
												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 					_snake_case						(						_lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		Optional[int]  =     EfficientNetImageProcessor if is_vision_available() else None
					def    _lowerCamelCase (  self:				Dict     )    ->				List[Any]:
												__UpperCAmelCase						:			Optional[Any]         =   EfficientNetImageProcessorTester(self     )
					@property
					def    _lowerCamelCase (  self:				str     )    ->				List[str]:
												return self.image_processor_tester.prepare_image_processor_dict()
					def    _lowerCamelCase (  self:				int     )    ->				Optional[Any]:
												__UpperCAmelCase						:			Optional[int]         =   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    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Any:
												__UpperCAmelCase						:			List[Any]         =   self.image_processing_class.from_dict(self.image_processor_dict     )
												self.assertEqual(image_processor.size	,  {"height": 18, "width": 18}     )

												__UpperCAmelCase						:			Optional[int]         =   self.image_processing_class.from_dict(self.image_processor_dict	,  size=42     )
												self.assertEqual(image_processor.size	,  {"height": 42, "width": 42}     )
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Dict:
												# Initialize image_processing
												__UpperCAmelCase						:			List[str]         =   self.image_processing_class(**self.image_processor_dict     )
												# create random PIL images
												__UpperCAmelCase						:			int         =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCamelCase     )
												for image in image_inputs:
																			self.assertIsInstance(__lowerCamelCase	,  Image.Image     )

												# Test not batched input
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )
					def    _lowerCamelCase (  self:				Dict     )    ->				Optional[Any]:
												# Initialize image_processing
												__UpperCAmelCase						:			str         =   self.image_processing_class(**self.image_processor_dict     )
												# create random numpy tensors
												__UpperCAmelCase						:			Optional[Any]         =   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
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )





					def    _lowerCamelCase (  self:				Any     )    ->				Union[str, Any]:
												# Initialize image_processing
												__UpperCAmelCase						:			Optional[int]         =   self.image_processing_class(**self.image_processor_dict     )
												# create random PyTorch tensors
												__UpperCAmelCase						:			Optional[Any]         =   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
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )

												# Test batched
												__UpperCAmelCase						:			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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,  )





from __future__ import annotations




class 					_snake_case						:
					def __init__(  self:				List[Any]	,  __lowerCamelCase:				int = 0     )    ->				None:
												__UpperCAmelCase						:			Dict         =   value
												__UpperCAmelCase						:			Node | None         =   None
												__UpperCAmelCase						:			Node | None         =   None




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Node | None:
							if treea is None:
														return treea
							if treea is None:
														return treea

							__UpperCAmelCase						:			Tuple         =   treea.value + treea.value
							__UpperCAmelCase						:			Optional[Any]         =   merge_two_binary_trees(treea.left,    treea.left							)
							__UpperCAmelCase						:			List[str]         =   merge_two_binary_trees(treea.right,    treea.right							)
							return treea




def   _UpperCamelCase   (			snake_case__							)						->   None:
							if root:
														print(root.value							)
														print_preorder(root.left							)
														print_preorder(root.right							)


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

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

	print('''Tree1 is: ''')
	print_preorder(treea)
	print('''Tree2 is: ''')
	print_preorder(treea)
	_snake_case			=						merge_two_binary_trees(treea, treea)
	print('''Merged Tree is: ''')
	print_preorder(merged_tree)




