
"""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,
								    ) ,						)






"""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)))




