code: 
  """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 ViltImageProcessor
  
  
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
  							def __init__(      self ,						a_ ,						a_=7 ,						a_=3 ,						a_=18 ,						a_=30 ,						a_=400 ,						a_=True ,						a_=None ,						a_=2 ,						a_=True ,						a_=[0.5, 0.5, 0.5] ,						a_=[0.5, 0.5, 0.5] ,						)							->							Optional[int]:
  								_UpperCAmelCase =						size if size is not None else {"shortest_edge": 30}
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						num_channels
  								_UpperCAmelCase =						image_size
  								_UpperCAmelCase =						min_resolution
  								_UpperCAmelCase =						max_resolution
  								_UpperCAmelCase =						do_resize
  								_UpperCAmelCase =						size
  								_UpperCAmelCase =						size_divisor
  								_UpperCAmelCase =						do_normalize
  								_UpperCAmelCase =						image_mean
  								_UpperCAmelCase =						image_std
  
  
  
  							def       _a   (      self   )							->							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,
  								    "size_divisor": self.size_divisor,
  								}
  
  
  
  							def       _a   (      self ,						a_ ,						a_=False   )							->							int:
  								if not batched:
  									_UpperCAmelCase =						self.size["shortest_edge"]
  									_UpperCAmelCase =						image_inputs[0]
  									if isinstance(a_ ,						Image.Image   ):
  										_UpperCAmelCase ,      _UpperCAmelCase =						image.size
  									else:
  										_UpperCAmelCase ,      _UpperCAmelCase =						image.shape[1], image.shape[2]
  									_UpperCAmelCase =						size / min(a_ ,						a_   )
  									if h < w:
  										_UpperCAmelCase ,      _UpperCAmelCase =						size, scale * w
  									else:
  										_UpperCAmelCase ,      _UpperCAmelCase =						scale * h, size
  
  									_UpperCAmelCase =						int((1333 / 800) * size   )
  									if max(a_ ,						a_   ) > max_size:
  										_UpperCAmelCase =						max_size / max(a_ ,						a_   )
  										_UpperCAmelCase =						newh * scale
  										_UpperCAmelCase =						neww * scale
  
  									_UpperCAmelCase ,      _UpperCAmelCase =						int(newh + 0.5   ), int(neww + 0.5   )
  									_UpperCAmelCase ,      _UpperCAmelCase =						(
  									    newh // self.size_divisor * self.size_divisor,
  									    neww // self.size_divisor * self.size_divisor,
  									)
  
  								else:
  									_UpperCAmelCase =						[]
  									for image in image_inputs:
  										_UpperCAmelCase ,      _UpperCAmelCase =						self.get_expected_values([image]   )
  										expected_values.append((expected_height, expected_width)   )
  									_UpperCAmelCase =						max(a_ ,						key=lambda a_   : item[0]   )[0]
  									_UpperCAmelCase =						max(a_ ,						key=lambda a_   : item[1]   )[1]
  
  								return expected_height, expected_width
  
  
  
  
  
  @require_torch
  @require_vision
  class    _lowerCAmelCase						(						lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     Union[str, Any]       =							ViltImageProcessor if is_vision_available() else None
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						ViltImageProcessingTester(self   )
  
  
  
  							@property
  							def       _a   (      self   )							->							Tuple:
  								return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						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"   )   )
  								self.assertTrue(hasattr(a_ ,						"size_divisor"   )   )
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						self.image_processing_class.from_dict(self.image_processor_dict   )
  								self.assertEqual(image_processor.size ,						{"shortest_edge": 30}   )
  
  								_UpperCAmelCase =						self.image_processing_class.from_dict(self.image_processor_dict ,						size=42   )
  								self.assertEqual(image_processor.size ,						{"shortest_edge": 42}   )
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								pass
  
  
  
  							def       _a   (      self   )							->							Dict:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random PIL images
  								_UpperCAmelCase =						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a_   )
  								for image in image_inputs:
  									self.assertIsInstance(a_ ,						Image.Image   )
  
  								# Test not batched input
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_ ,						batched=a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        expected_height,
  								        expected_width,
  								    ) ,						)
  
  
  
  							def       _a   (      self   )							->							Dict:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random numpy tensors
  								_UpperCAmelCase =						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
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_ ,						batched=a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        expected_height,
  								        expected_width,
  								    ) ,						)
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random PyTorch tensors
  								_UpperCAmelCase =						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
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.image_processor_tester.get_expected_values(a_ ,						batched=a_   )
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        expected_height,
  								        expected_width,
  								    ) ,						)
  
  
  
  
  
style_context: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("recursive_lucas_number accepts only integer arguments."      )
  	if n_th_number == 0:
  		return 2
  	if n_th_number == 1:
  		return 1
  
  	return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
  	    n_th_number - 2      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("dynamic_lucas_number accepts only integer arguments."      )
  	_UpperCAmelCase ,      _UpperCAmelCase =						2, 1
  	for _ in range(UpperCamelCase__      ):
  		_UpperCAmelCase ,      _UpperCAmelCase =						b, a + b
  	return a
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  			__magic_name__		       =				int(input('''Enter the number of terms in lucas series:\n''').strip())
  			print('''Using recursive function to calculate lucas series:''')
  			print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
  			print('''\nUsing dynamic function to calculate lucas series:''')
  			print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))
  
  
  
  
  