"""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 MobileViTImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  int	,    snake_case__    :  str	,    snake_case__    :  List[Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  int=30	,    snake_case__    :  Union[str, Any]=400	,    snake_case__    :  str=True	,    snake_case__    :  Optional[int]=None	,    snake_case__    :  str=True	,    snake_case__    :  Dict=None	,    snake_case__    :  int=True	,    ):
							lowerCAmelCase__   = size if size is not None else {"""shortest_edge""": 20}
							lowerCAmelCase__   = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = do_resize
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_center_crop
							lowerCAmelCase__   = crop_size
							lowerCAmelCase__   = do_flip_channel_order




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}

@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Union[str, Any]      =     MobileViTImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = MobileViTImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_resize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """size"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_center_crop"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """center_crop"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_flip_channel_order"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = 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}  )

							lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							pass




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

							# Test not batched input
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = image_processing(snake_case__	,    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     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							# Initialize image_processing
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    numpify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    np.ndarray  )

							# Test not batched input
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = image_processing(snake_case__	,    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     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							# Initialize image_processing
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    torchify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    torch.Tensor  )

							# Test not batched input
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = image_processing(snake_case__	,    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"""




from collections.abc import Generator







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = 0, 1
		while True:
				lowerCAmelCase__ ,       lowerCAmelCase__   = b, a + b
				yield b







def 				_UpperCAmelCase		(							lowerCamelCase__ = 1000  ):





		"""simple docstring"""




		lowerCAmelCase__   = 1
		lowerCAmelCase__   = fibonacci_generator()
		while len(str(next(lowerCamelCase__  )  )  ) < n:
				answer += 1
		return answer + 1


if __name__ == "__main__":
			print(solution(int(str(input()).strip())))




