




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)


if is_vision_available():
		import PIL



class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=["pixel_values"]



						def __init__( self  :      Any				,       snake_case__  :      bool = True				,       snake_case__  :      Dict[str, int] = None				,       snake_case__  :      PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__  :      bool = True				,       snake_case__  :      bool = False				,       snake_case__  :      bool = True				,       snake_case__  :      bool = True				,       snake_case__  :      Union[int, float] = 1 / 2_55				,       snake_case__  :      bool = True				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       **snake_case__  :      int				,       ):

								"""simple docstring"""

								super().__init__(**snake_case__     )

								SCREAMING_SNAKE_CASE_							=       size if size is not None else {'height': 25_60, 'width': 19_20}
								if isinstance(snake_case__				,       (tuple, list)     ):
										# The previous feature extractor size parameter was in (width, height) format
										SCREAMING_SNAKE_CASE_							=       size[::-1]
								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       do_resize
								SCREAMING_SNAKE_CASE_							=       size
								SCREAMING_SNAKE_CASE_							=       resample
								SCREAMING_SNAKE_CASE_							=       do_thumbnail
								SCREAMING_SNAKE_CASE_							=       do_align_long_axis
								SCREAMING_SNAKE_CASE_							=       do_pad
								SCREAMING_SNAKE_CASE_							=       do_rescale
								SCREAMING_SNAKE_CASE_							=       rescale_factor
								SCREAMING_SNAKE_CASE_							=       do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								SCREAMING_SNAKE_CASE_							=       image_std if image_std is not None else IMAGENET_STANDARD_STD



						def   __a				( self  :      List[Any]				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']

								if (output_width < output_height and input_width > input_height) or (
								    output_width > output_height and input_width < input_height
								):
										SCREAMING_SNAKE_CASE_							=       np.rotaa(snake_case__				,       3     )

								if data_format is not None:
										SCREAMING_SNAKE_CASE_							=       to_channel_dimension_format(snake_case__				,       snake_case__     )

								return image



						def   __a				( self  :      Any				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      bool = False				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       output_width - input_width
								SCREAMING_SNAKE_CASE_							=       output_height - input_height

								if random_padding:
										SCREAMING_SNAKE_CASE_							=       np.random.randint(low=0				,       high=delta_height + 1     )
										SCREAMING_SNAKE_CASE_							=       np.random.randint(low=0				,       high=delta_width + 1     )
								else:
										SCREAMING_SNAKE_CASE_							=       delta_height // 2
										SCREAMING_SNAKE_CASE_							=       delta_width // 2

								SCREAMING_SNAKE_CASE_							=       delta_height - pad_top
								SCREAMING_SNAKE_CASE_							=       delta_width - pad_left

								SCREAMING_SNAKE_CASE_							=       ((pad_top, pad_bottom), (pad_left, pad_right))
								return pad(snake_case__				,       snake_case__				,       data_format=snake_case__     )



						def   __a				( self  :      List[Any]				,       *snake_case__  :      Any				,       **snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'     )
								return self.pad_image(*snake_case__				,       **snake_case__     )



						def   __a				( self  :      Dict				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      PILImageResampling = PILImageResampling.BICUBIC				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      Tuple				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_image_size(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size['height'], size['width']

								# We always resize to the smallest of either the input or output size.
								SCREAMING_SNAKE_CASE_							=       min(snake_case__				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       min(snake_case__				,       snake_case__     )

								if height == input_height and width == input_width:
										return image

								if input_height > input_width:
										SCREAMING_SNAKE_CASE_							=       int(input_width * height / input_height     )
								elif input_width > input_height:
										SCREAMING_SNAKE_CASE_							=       int(input_height * width / input_width     )

								return resize(
								    snake_case__				,       size=(height, width)				,       resample=snake_case__				,       reducing_gap=2.0				,       data_format=snake_case__				,       **snake_case__     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      np.ndarray				,       snake_case__  :      Dict[str, int]				,       snake_case__  :      PILImageResampling = PILImageResampling.BICUBIC				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      List[Any]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       min(size['height']				,       size['width']     )
								SCREAMING_SNAKE_CASE_							=       get_resize_output_image_size(snake_case__				,       size=snake_case__				,       default_to_square=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       resize(snake_case__				,       size=snake_case__				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__     )
								return resized_image



						def   __a				( self  :      str				,       snake_case__  :      np.ndarray				,       snake_case__  :      Union[int, float]				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      str				,       ):

								"""simple docstring"""

								return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__     )



						def   __a				( self  :      Dict				,       snake_case__  :      np.ndarray				,       snake_case__  :      Union[float, List[float]]				,       snake_case__  :      Union[float, List[float]]				,       snake_case__  :      Optional[Union[str, ChannelDimension]] = None				,       **snake_case__  :      Optional[Any]				,       ):

								"""simple docstring"""

								return normalize(snake_case__				,       mean=snake_case__				,       std=snake_case__				,       data_format=snake_case__				,       **snake_case__     )






						def   __a				( self  :      int				,       snake_case__  :      ImageInput				,       snake_case__  :      bool = None				,       snake_case__  :      Dict[str, int] = None				,       snake_case__  :      PILImageResampling = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = None				,       snake_case__  :      bool = False				,       snake_case__  :      bool = None				,       snake_case__  :      float = None				,       snake_case__  :      bool = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[float, List[float]]] = None				,       snake_case__  :      Optional[Union[str, TensorType]] = None				,       snake_case__  :      Optional[ChannelDimension] = ChannelDimension.FIRST				,       **snake_case__  :      int				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE_							=       size if size is not None else self.size
								if isinstance(snake_case__				,       (tuple, list)     ):
										# Previous feature extractor had size in (width, height) format
										SCREAMING_SNAKE_CASE_							=       size[::-1]
								SCREAMING_SNAKE_CASE_							=       get_size_dict(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE_							=       do_thumbnail if do_thumbnail is not None else self.do_thumbnail
								SCREAMING_SNAKE_CASE_							=       do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
								SCREAMING_SNAKE_CASE_							=       do_pad if do_pad is not None else self.do_pad
								SCREAMING_SNAKE_CASE_							=       do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE_							=       rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE_							=       do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE_							=       image_std if image_std is not None else self.image_std

								SCREAMING_SNAKE_CASE_							=       make_list_of_images(snake_case__     )

								if not valid_images(snake_case__     ):
										raise ValueError(
										    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
										    'torch.Tensor, tf.Tensor or jax.ndarray.'     )

								if do_resize and size is None:
										raise ValueError('Size must be specified if do_resize is True.'     )

								if do_rescale and rescale_factor is None:
										raise ValueError('Rescale factor must be specified if do_rescale is True.'     )

								if do_pad and size is None:
										raise ValueError('Size must be specified if do_pad is True.'     )

								if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('Image mean and std must be specified if do_normalize is True.'     )

								# All transformations expect numpy arrays.
								SCREAMING_SNAKE_CASE_							=       [to_numpy_array(snake_case__     ) for image in images]

								if do_align_long_axis:
										SCREAMING_SNAKE_CASE_							=       [self.align_long_axis(snake_case__				,       size=snake_case__     ) for image in images]

								if do_resize:
										SCREAMING_SNAKE_CASE_							=       [self.resize(image=snake_case__				,       size=snake_case__				,       resample=snake_case__     ) for image in images]

								if do_thumbnail:
										SCREAMING_SNAKE_CASE_							=       [self.thumbnail(image=snake_case__				,       size=snake_case__     ) for image in images]

								if do_pad:
										SCREAMING_SNAKE_CASE_							=       [self.pad_image(image=snake_case__				,       size=snake_case__				,       random_padding=snake_case__     ) for image in images]

								if do_rescale:
										SCREAMING_SNAKE_CASE_							=       [self.rescale(image=snake_case__				,       scale=snake_case__     ) for image in images]

								if do_normalize:
										SCREAMING_SNAKE_CASE_							=       [self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__     ) for image in images]

								SCREAMING_SNAKE_CASE_							=       [to_channel_dimension_format(snake_case__				,       snake_case__     ) for image in images]

								SCREAMING_SNAKE_CASE_							=       {'pixel_values': images}
								return BatchFeature(data=snake_case__				,       tensor_type=snake_case__     )





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  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
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


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