





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 LevitImageProcessor


class 						lowercase    (     unittest.TestCase     ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {'''shortest_edge''': 18}
												SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_center_crop
												SCREAMING_SNAKE_CASE    			=       crop_size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					int      )					->				Dict:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "do_center_crop": self.do_center_crop,
												    "size": self.size,
												    "crop_size": self.crop_size,
												}



@require_torch
@require_vision
class 						lowercase    (     a__				,       unittest.TestCase     ):
							lowercase__						:       Tuple								=							LevitImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       LevitImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(lowercase__	,							"image_mean"      )      )
												self.assertTrue(hasattr(lowercase__	,							"image_std"      )      )
												self.assertTrue(hasattr(lowercase__	,							"do_normalize"      )      )
												self.assertTrue(hasattr(lowercase__	,							"do_resize"      )      )
												self.assertTrue(hasattr(lowercase__	,							"do_center_crop"      )      )
												self.assertTrue(hasattr(lowercase__	,							"size"      )      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"shortest_edge": 18}      )
												self.assertEqual(image_processor.crop_size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       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   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												pass




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase__      )
												for image in image_inputs:
																	self.assertIsInstance(lowercase__	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(lowercase__	,							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   :					str      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase__	,							numpify=lowercase__      )
												for image in image_inputs:
																	self.assertIsInstance(lowercase__	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(lowercase__	,							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   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase__	,							torchify=lowercase__      )
												for image in image_inputs:
																	self.assertIsInstance(lowercase__	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(lowercase__	,							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 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  ), F"The input value of [n={number}] is not an integer"

					if number == 1:
										return 2
					elif number < 1:
										SCREAMING_SNAKE_CASE    			=       F"The input value of [n={number}] has to be > 0"
										raise ValueError(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       sylvester(number - 1  )
										SCREAMING_SNAKE_CASE    			=       num - 1
										SCREAMING_SNAKE_CASE    			=       num
										return lower * upper + 1


if __name__ == "__main__":
							print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")




