





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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor


class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def __init__(	self:	List[str],				lowerCamelCase_:	Tuple,				lowerCamelCase_:	Tuple=7,				lowerCamelCase_:	Tuple=3,				lowerCamelCase_:	Dict=18,				lowerCamelCase_:	str=30,				lowerCamelCase_:	Any=400,				lowerCamelCase_:	Any=True,				lowerCamelCase_:	Dict=None,				lowerCamelCase_:	Union[str, Any]=True,				lowerCamelCase_:	Dict=None,				lowerCamelCase_:	str=True,				lowerCamelCase_:	Tuple=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],				lowerCamelCase_:	Any=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],				lowerCamelCase_:	List[str]=True,				):
			lowercase__				:			str            =		size if size is not None else {'shortest_edge': 20}
			lowercase__				:			Dict            =		crop_size if crop_size is not None else {'height': 18, 'width': 18}
			lowercase__				:			Dict            =		parent
			lowercase__				:			str            =		batch_size
			lowercase__				:			Optional[Any]            =		num_channels
			lowercase__				:			List[str]            =		image_size
			lowercase__				:			Tuple            =		min_resolution
			lowercase__				:			List[str]            =		max_resolution
			lowercase__				:			int            =		do_resize
			lowercase__				:			str            =		size
			lowercase__				:			Dict            =		do_center_crop
			lowercase__				:			List[str]            =		crop_size
			lowercase__				:			Any            =		do_normalize
			lowercase__				:			Dict            =		image_mean
			lowercase__				:			Union[str, Any]            =		image_std
			lowercase__				:			Tuple            =		do_convert_rgb

	def       snake_case__(	self:	Optional[int] ):
			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,
			    "do_convert_rgb": self.do_convert_rgb,
			}





	def       snake_case__(	self:	int,				lowerCamelCase_:	List[Any]=False,				lowerCamelCase_:	List[Any]=False,				lowerCamelCase_:	str=False ):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			if equal_resolution:
					lowercase__				:			str            =		[]
					for i in range(self.batch_size ):
							image_inputs.append(
							    np.random.randint(
							        255,				size=(self.num_channels, self.max_resolution, self.max_resolution),				dtype=np.uinta ) )
			else:
					lowercase__				:			Optional[int]            =		[]
					for i in range(self.batch_size ):
							lowercase__      ,							lowercase__				:			Optional[Any]            =		np.random.choice(np.arange(self.min_resolution,				self.max_resolution ),				2 )
							image_inputs.append(np.random.randint(255,				size=(self.num_channels, width, height),				dtype=np.uinta ) )

			if not numpify and not torchify:
					# PIL expects the channel dimension as last dimension
					lowercase__				:			Optional[Any]            =		[Image.fromarray(np.moveaxis(lowerCamelCase_,				0,				-1 ) ) for x in image_inputs]

			if torchify:
					lowercase__				:			List[Any]            =		[torch.from_numpy(lowerCamelCase_ ) for x in image_inputs]

			return image_inputs


@require_torch
@require_vision
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							CLIPImageProcessor if is_vision_available() else None

	def       snake_case__(	self:	Any ):
			lowercase__				:			Optional[int]            =		CLIPImageProcessingTester(self )

	@property
	def       snake_case__(	self:	Union[str, Any] ):
			return self.image_processor_tester.prepare_image_processor_dict()

	def       snake_case__(	self:	Any ):
			lowercase__				:			List[Any]            =		self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_resize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'size' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_center_crop' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'center_crop' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_normalize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_mean' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_std' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_convert_rgb' ) )

	def       snake_case__(	self:	str ):
			lowercase__				:			Union[str, Any]            =		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} )

			lowercase__				:			Any            =		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       snake_case__(	self:	Union[str, Any] ):
			pass

	def       snake_case__(	self:	List[Any] ):
			# Initialize image_processing
			lowercase__				:			Dict            =		self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			lowercase__				:			Tuple            =		self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				Image.Image )

			# Test not batched input
			lowercase__				:			List[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__				:			Dict            =		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.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    ),				)

	def       snake_case__(	self:	List[Any] ):
			# Initialize image_processing
			lowercase__				:			int            =		self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			lowercase__				:			Dict            =		self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_,				numpify=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				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(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.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    ),				)





	def       snake_case__(	self:	Dict ):
			# Initialize image_processing
			lowercase__				:			Optional[int]            =		self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			lowercase__				:			List[Any]            =		self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_,				torchify=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				torch.Tensor )

			# Test not batched input
			lowercase__				:			Union[str, 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__				:			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.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    ),				)


@require_torch
@require_vision
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							CLIPImageProcessor if is_vision_available() else None

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		CLIPImageProcessingTester(self,				num_channels=4 )
			lowercase__				:			Any            =		3

	@property
	def       snake_case__(	self:	List[str] ):
			return self.image_processor_tester.prepare_image_processor_dict()

	def       snake_case__(	self:	int ):
			lowercase__				:			str            =		self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_resize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'size' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_center_crop' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'center_crop' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_normalize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_mean' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_std' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_convert_rgb' ) )

	def       snake_case__(	self:	Union[str, Any] ):
			pass





	def       snake_case__(	self:	List[str] ):
			# Initialize image_processing
			lowercase__				:			str            =		self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			lowercase__				:			List[str]            =		self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				Image.Image )

			# Test not batched input
			lowercase__				:			str            =		image_processing(image_inputs[0],				return_tensors='pt' ).pixel_values
			self.assertEqual(
			    encoded_images.shape,				(
			        1,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    ),				)

			# Test batched
			lowercase__				:			Optional[int]            =		image_processing(lowerCamelCase_,				return_tensors='pt' ).pixel_values
			self.assertEqual(
			    encoded_images.shape,				(
			        self.image_processor_tester.batch_size,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    ),				)









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		9

		while num > 0:
				if num % 10 != digit:
						return False
				num //= 100
				digit -= 1

		return True


def 			SCREAMING_SNAKE_CASE__     (					)      ->       int:




		'''simple docstring'''
		lowercase__				:			List[str]            =		138_902_663

		while not is_square_form(num * num ):
				if num % 10 == 3:
						num -= 6  # (3 - 6) % 10 = 7
				else:
						num -= 4  # (7 - 4) % 10 = 3

		return num * 10


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


