


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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Optional[int]=7	,				_lowerCamelCase				:					Any=3	,				_lowerCamelCase				:					List[str]=18	,				_lowerCamelCase				:					int=30	,				_lowerCamelCase				:					Tuple=4_00	,				_lowerCamelCase				:					Dict=True	,				_lowerCamelCase				:					Tuple=None	,				_lowerCamelCase				:					Optional[int]=True	,				_lowerCamelCase				:					Optional[Any]=None	,				_lowerCamelCase				:					int=True	,				_lowerCamelCase				:					Dict=[0.5, 0.5, 0.5]	,				_lowerCamelCase				:					int=[0.5, 0.5, 0.5]	,				)							->					Union[str, Any]:
										__magic_name__	  = size if size is not None else {"height": 20, "width": 20}
										__magic_name__	  = crop_size if crop_size is not None else {"height": 18, "width": 18}

										__magic_name__	  = parent
										__magic_name__	  = batch_size
										__magic_name__	  = num_channels
										__magic_name__	  = image_size
										__magic_name__	  = min_resolution
										__magic_name__	  = max_resolution
										__magic_name__	  = do_resize
										__magic_name__	  = size
										__magic_name__	  = do_center_crop
										__magic_name__	  = crop_size
										__magic_name__	  = do_normalize
										__magic_name__	  = image_mean
										__magic_name__	  = image_std

						def 					__A					( self				:					List[Any]  )							->					Dict:
										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 		UpperCamelCase_		(					A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Optional[int]         =     DeiTImageProcessor if is_vision_available() else None
						UpperCAmelCase__	:       Union[str, Any]         =     True
						def 					__A					( self				:					List[Any]  )							->					Dict:
										__magic_name__	  = DeiTImageProcessingTester(self  )
						@property
						def 					__A					( self				:					str  )							->					Tuple:
										return self.image_processor_tester.prepare_image_processor_dict()
						def 					__A					( self				:					int  )							->					List[Any]:
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(a_	,				"do_resize"  )  )
										self.assertTrue(hasattr(a_	,				"size"  )  )
										self.assertTrue(hasattr(a_	,				"do_center_crop"  )  )
										self.assertTrue(hasattr(a_	,				"center_crop"  )  )
										self.assertTrue(hasattr(a_	,				"do_normalize"  )  )
										self.assertTrue(hasattr(a_	,				"image_mean"  )  )
										self.assertTrue(hasattr(a_	,				"image_std"  )  )
						def 					__A					( self				:					Tuple  )							->					int:
										__magic_name__	  = 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}  )

										__magic_name__	  = 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 					__A					( self				:					Optional[int]  )							->					Dict:
										pass
						def 					__A					( self				:					int  )							->					List[str]:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										__magic_name__	  = prepare_image_inputs(self.image_processor_tester	,				equal_resolution=a_  )
										for image in image_inputs:
														self.assertIsInstance(a_	,				Image.Image  )

										# Test not batched input
										__magic_name__	  = 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
										__magic_name__	  = 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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	,				)
						def 					__A					( self				:					Dict  )							->					int:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random numpy tensors
										__magic_name__	  = 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
										__magic_name__	  = 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
										__magic_name__	  = 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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	,				)

						def 					__A					( self				:					Optional[Any]  )							->					Optional[int]:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										__magic_name__	  = 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
										__magic_name__	  = 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
										__magic_name__	  = 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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	,				)







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int = 100						):
				'''simple docstring'''




				__magic_name__	  = [[0 for _ in range(lowerCamelCase_						)] for _ in range(m + 1						)]
				for i in range(m + 1						):
								__magic_name__	  = 1

				for n in range(m + 1						):
								for k in range(1	,  lowerCamelCase_						):
												memo[n][k] += memo[n][k - 1]
												if n > k:
																memo[n][k] += memo[n - k - 1][k]

				return memo[m][m - 1] - 1


if __name__ == "__main__":
						print(solution(int(input('Enter a number: ').strip())))



