



'''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 ViTImageProcessor

class __snake_case(      unittest.TestCase					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_=7	,       A_=3	,       A_=18	,       A_=30	,       A_=400	,       A_=True	,       A_=None	,       A_=True	,       A_=[0.5, 0.5, 0.5]	,       A_=[0.5, 0.5, 0.5]	,       )	->	str:
							lowerCAmelCase    =	size if size is not None else {"""height""": 18, """width""": 18}
							lowerCAmelCase    =	parent
							lowerCAmelCase    =	batch_size
							lowerCAmelCase    =	num_channels
							lowerCAmelCase    =	image_size
							lowerCAmelCase    =	min_resolution
							lowerCAmelCase    =	max_resolution
							lowerCAmelCase    =	do_resize
							lowerCAmelCase    =	size
							lowerCAmelCase    =	do_normalize
							lowerCAmelCase    =	image_mean
							lowerCAmelCase    =	image_std




						def 				__snake_case    (					self      )	->	List[str]:
							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(      _lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Tuple           =		ViTImageProcessor if is_vision_available() else None


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	ViTImageProcessingTester(self      )


						@property
						def 				__snake_case    (					self      )	->	int:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_	,       """image_mean"""      )      )
							self.assertTrue(hasattr(A_	,       """image_std"""      )      )
							self.assertTrue(hasattr(A_	,       """do_normalize"""      )      )
							self.assertTrue(hasattr(A_	,       """do_resize"""      )      )
							self.assertTrue(hasattr(A_	,       """size"""      )      )


						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase    =	self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size	,       {"""height""": 18, """width""": 18}      )

							lowerCAmelCase    =	self.image_processing_class.from_dict(self.image_processor_dict	,       size=42      )
							self.assertEqual(image_processor.size	,       {"""height""": 42, """width""": 42}      )


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


						def 				__snake_case    (					self      )	->	Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase    =	self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							lowerCAmelCase    =	prepare_image_inputs(self.image_processor_tester	,       equal_resolution=A_      )
							for image in image_inputs:
								self.assertIsInstance(A_	,       Image.Image      )

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


						def 				__snake_case    (					self      )	->	List[str]:
							# Initialize image_processing
							lowerCAmelCase    =	self.image_processing_class(**self.image_processor_dict      )
							# create random numpy tensors
							lowerCAmelCase    =	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
							lowerCAmelCase    =	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
							lowerCAmelCase    =	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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )	,       )




						def 				__snake_case    (					self      )	->	Dict:
							# Initialize image_processing
							lowerCAmelCase    =	self.image_processing_class(**self.image_processor_dict      )
							# create random PyTorch tensors
							lowerCAmelCase    =	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
							lowerCAmelCase    =	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
							lowerCAmelCase    =	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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )	,       )




'''simple docstring'''

def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int = 4_000_000							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	1
	lowerCAmelCase    =	2
	lowerCAmelCase    =	0
	while j <= n:
		if j % 2 == 0:
			total += j
		lowerCAmelCase,   lowerCAmelCase    =	j, i + j

	return total


if __name__ == "__main__":
				print(F'''{solution() = }''')