



'''simple docstring'''




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a   :			List[str]					  =    logging.get_logger(__name__)

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = ['pixel_values']





	def __init__(  self		,		A = True		,		A = 1 / 255		,		A = True		,		A = 8		,		**A		,		)			->			None:
								super().__init__(**A    )

								UpperCAmelCase     :		Tuple							     =		do_rescale
								UpperCAmelCase     :		Optional[Any]							     =		rescale_factor
								UpperCAmelCase     :		List[str]							     =		do_pad
								UpperCAmelCase     :		int							     =		pad_size





	def  _lowercase(  self		,		A		,		A		,		A = None		,		**A    )			->			np.ndarray:
								return rescale(A		,		scale=A		,		data_format=A		,		**A    )





	def  _lowercase(  self		,		A		,		A		,		A = None    )			->			Optional[Any]:
								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		get_image_size(A    )
								UpperCAmelCase     :		Optional[Any]							     =		(old_height // size + 1) * size - old_height
								UpperCAmelCase     :		Any							     =		(old_width // size + 1) * size - old_width

								return pad(A		,		((0, pad_height), (0, pad_width))		,		mode="""symmetric"""		,		data_format=A    )






	def  _lowercase(  self		,		A		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = ChannelDimension.FIRST		,		**A		,		)			->			List[Any]:
								UpperCAmelCase     :		str							     =		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase     :		Optional[Any]							     =		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase     :		int							     =		do_pad if do_pad is not None else self.do_pad
								UpperCAmelCase     :		Dict							     =		pad_size if pad_size is not None else self.pad_size

								UpperCAmelCase     :		List[Any]							     =		make_list_of_images(A    )

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

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

								# All transformations expect numpy arrays.
								UpperCAmelCase     :		int							     =		[to_numpy_array(A    ) for image in images]

								if do_rescale:
															UpperCAmelCase     :		Any							     =		[self.rescale(image=A		,		scale=A    ) for image in images]

								if do_pad:
															UpperCAmelCase     :		int							     =		[self.pad(A		,		size=A    ) for image in images]

								UpperCAmelCase     :		str							     =		[to_channel_dimension_format(A		,		A    ) for image in images]

								UpperCAmelCase     :		List[str]							     =		{"""pixel_values""": images}
								return BatchFeature(data=A		,		tensor_type=A    )






'''simple docstring'''




from itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

