"""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 ConvNextImageProcessor


class 			_lowercase		(   unittest.TestCase  ):

				def __init__(     self       :    Any       ,    _lowercase       :    Optional[int]       ,    _lowercase       :    int=7       ,    _lowercase       :    int=3       ,    _lowercase       :    List[Any]=1_8       ,    _lowercase       :    Union[str, Any]=3_0       ,    _lowercase       :    Any=4_0_0       ,    _lowercase       :    int=True       ,    _lowercase       :    List[str]=None       ,    _lowercase       :    Optional[int]=0.875       ,    _lowercase       :    List[Any]=True       ,    _lowercase       :    int=[0.5, 0.5, 0.5]       ,    _lowercase       :    List[Any]=[0.5, 0.5, 0.5]       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		size if size is not None else {"""shortest_edge""": 2_0}
										UpperCAmelCase__						=		parent
										UpperCAmelCase__						=		batch_size
										UpperCAmelCase__						=		num_channels
										UpperCAmelCase__						=		image_size
										UpperCAmelCase__						=		min_resolution
										UpperCAmelCase__						=		max_resolution
										UpperCAmelCase__						=		do_resize
										UpperCAmelCase__						=		size
										UpperCAmelCase__						=		crop_pct
										UpperCAmelCase__						=		do_normalize
										UpperCAmelCase__						=		image_mean
										UpperCAmelCase__						=		image_std

				def        A (     self       :    Dict     ):


										'''simple docstring'''
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}


@require_torch
@require_vision
class 			_lowercase		(   __lowerCamelCase      ,      unittest.TestCase  ):
				lowerCamelCase__						=       ConvNextImageProcessor if is_vision_available() else None

				def        A (     self       :    Optional[int]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		ConvNextImageProcessingTester(self     )

				@property
				def        A (     self       :    Optional[Any]     ):


										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()

				def        A (     self       :    str     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(_lowercase       ,    """do_resize"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """size"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """crop_pct"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """do_normalize"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """image_mean"""     )     )
										self.assertTrue(hasattr(_lowercase       ,    """image_std"""     )     )

				def        A (     self       :    Any     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size       ,    {"""shortest_edge""": 2_0}     )

										UpperCAmelCase__						=		self.image_processing_class.from_dict(self.image_processor_dict       ,    size=4_2     )
										self.assertEqual(image_processor.size       ,    {"""shortest_edge""": 4_2}     )

				def        A (     self       :    List[str]     ):


										'''simple docstring'''
										pass

				def        A (     self       :    List[str]     ):


										'''simple docstring'''
										# 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=_lowercase     )
										for image in image_inputs:
																self.assertIsInstance(_lowercase       ,    Image.Image     )

										# Test not batched input
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )

										# Test batched
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )

				def        A (     self       :    Optional[Any]     ):


										'''simple docstring'''
										# 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=_lowercase       ,    numpify=_lowercase     )
										for image in image_inputs:
																self.assertIsInstance(_lowercase       ,    np.ndarray     )

										# Test not batched input
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )

										# Test batched
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )

				def        A (     self       :    int     ):


										'''simple docstring'''
										# 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=_lowercase       ,    torchify=_lowercase     )
										for image in image_inputs:
																self.assertIsInstance(_lowercase       ,    torch.Tensor     )

										# Test not batched input
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )

										# Test batched
										UpperCAmelCase__						=		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.size["""shortest_edge"""],
										        self.image_processor_tester.size["""shortest_edge"""],
										    )       ,    )


"""simple docstring"""







from __future__ import annotations

import sys


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						if n < 0:
												raise ValueError("""Negative arguments are not supported"""			)
						return _fib(UpperCAmelCase__			)[0]


def   a__      (		UpperCAmelCase__       :			int			)->	tuple[int, int]:
						if n == 0:  # (F(0), F(1))
												return (0, 1)

						# F(2n) = F(n)[2F(n+1) − F(n)]
						# F(2n+1) = F(n+1)^2+F(n)^2
						UpperCAmelCase__	,      UpperCAmelCase__						=		_fib(n // 2			)
						UpperCAmelCase__						=		a * (b * 2 - a)
						UpperCAmelCase__						=		a * a + b * b
						return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						UpperCamelCase_       :       int							   =		int(sys.argv[1])
						print(F"""fibonacci({n}) is {fibonacci(n)}""")

