



'''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 		lowerCamelCase__   (    unittest.TestCase  ):




					'''simple docstring'''



					def __init__(	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Dict=7  ,	UpperCamelCase_					:						int=3  ,	UpperCamelCase_					:						List[Any]=18  ,	UpperCamelCase_					:						str=30  ,	UpperCamelCase_					:						List[Any]=400  ,	UpperCamelCase_					:						List[Any]=True  ,	UpperCamelCase_					:						List[Any]=None  ,	UpperCamelCase_					:						Optional[int]=True  ,	UpperCamelCase_					:						Optional[int]=None  ,	UpperCamelCase_					:						List[Any]=True  ,	UpperCamelCase_					:						List[Any]=[0.5, 0.5, 0.5]  ,	UpperCamelCase_					:						Optional[Any]=[0.5, 0.5, 0.5]  ,	)	->							Tuple:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		size if size is not None else {'height': 20, 'width': 20}
						_lowercase       :							List[str]  	=		crop_size if crop_size is not None else {'height': 18, 'width': 18}

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




					def     __UpperCAmelCase  (	self					:						List[Any]       )	->							Union[str, 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,
						}






@require_torch
@require_vision
class 		lowerCamelCase__   (    A						, unittest.TestCase  ):




					'''simple docstring'''



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




					def     __UpperCAmelCase  (	self					:						Optional[Any]       )	->							int:



						'''simple docstring'''





						_lowercase       :							Union[str, Any]  	=		DeiTImageProcessingTester(self       )




					@property
					def     __UpperCAmelCase  (	self					:						Dict       )	->							int:



						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




					def     __UpperCAmelCase  (	self					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.image_processing_class(**self.image_processor_dict       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'do_resize'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'size'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'do_center_crop'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'center_crop'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'do_normalize'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'image_mean'       )       )
						self.assertTrue(hasattr(UpperCamelCase_  ,	'image_std'       )       )




					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		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}       )

						_lowercase       :							Optional[int]  	=		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     __UpperCAmelCase  (	self					:						Dict       )	->							Optional[Any]:



						'''simple docstring'''





						pass




					def     __UpperCAmelCase  (	self					:						int       )	->							List[str]:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		self.image_processing_class(**self.image_processor_dict       )
						# create random PIL images
						_lowercase       :							Any  	=		prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase_       )
						for image in image_inputs:
							self.assertIsInstance(UpperCamelCase_  ,	Image.Image       )

						# Test not batched input
						_lowercase       :							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
						_lowercase       :							int  	=		image_processing(UpperCamelCase_  ,	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					:						int       )	->							Optional[int]:



						'''simple docstring'''





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

						# Test not batched input
						_lowercase       :							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
						_lowercase       :							Optional[Any]  	=		image_processing(UpperCamelCase_  ,	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					:						Optional[Any]       )	->							Optional[int]:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		self.image_processing_class(**self.image_processor_dict       )
						# create random PyTorch tensors
						_lowercase       :							Optional[int]  	=		prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase_  ,	torchify=UpperCamelCase_       )
						for image in image_inputs:
							self.assertIsInstance(UpperCamelCase_  ,	torch.Tensor       )

						# Test not batched input
						_lowercase       :							Optional[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
						_lowercase       :							Optional[int]  	=		image_processing(UpperCamelCase_  ,	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'],
						    )  ,	)





'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						if node is None:
							return

						if path_sum == self.target:
							self.paths += 1

						if node.left:
							self.depth_first_search(node.left  ,	path_sum + node.left.value       )
						if node.right:
							self.depth_first_search(node.right  ,	path_sum + node.right.value       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
