




"""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_      =    logging.get_logger(__name__)






class  lowercase(					__a			):







  '''simple docstring'''





  lowercase__			 = ["pixel_values"]


  def __init__(    self:		Union[str, Any],				a_:		bool = True,				a_:		Union[int, float] = 1 / 255,				a_:		bool = True,				a_:		int = 8,				**a_:		List[str],				):




    '''simple docstring'''



    super().__init__(**a_		)

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


  def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		np.ndarray,				a_:		float,				a_:		Optional[Union[str, ChannelDimension]] = None,				**a_:		str		):




    '''simple docstring'''



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


  def 			UpperCamelCase_       (    self:		Any,				a_:		np.ndarray,				a_:		int,				a_:		Optional[Union[str, ChannelDimension]] = None		):




    '''simple docstring'''



    _snake_case							:		str								=  get_image_size(a_		)
    _snake_case							:		str								=  (old_height // size + 1) * size - old_height
    _snake_case							:		Optional[Any]								=  (old_width // size + 1) * size - old_width

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


  def 			UpperCamelCase_       (    self:		Optional[int],				a_:		ImageInput,				a_:		Optional[bool] = None,				a_:		Optional[float] = None,				a_:		Optional[bool] = None,				a_:		Optional[int] = None,				a_:		Optional[Union[str, TensorType]] = None,				a_:		Union[str, ChannelDimension] = ChannelDimension.FIRST,				**a_:		str,				):




    '''simple docstring'''



    _snake_case							:		Dict								=  do_rescale if do_rescale is not None else self.do_rescale
    _snake_case							:		Union[str, Any]								=  rescale_factor if rescale_factor is not None else self.rescale_factor
    _snake_case							:		str								=  do_pad if do_pad is not None else self.do_pad
    _snake_case							:		Optional[int]								=  pad_size if pad_size is not None else self.pad_size

    _snake_case							:		Union[str, 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.
    _snake_case							:		List[Any]								=  [to_numpy_array(a_		) for image in images]

    if do_rescale:
      _snake_case							:		int								=  [self.rescale(image=a_,				scale=a_		) for image in images]

    if do_pad:
      _snake_case							:		List[str]								=  [self.pad(a_,				size=a_		) for image in images]

    _snake_case							:		Dict								=  [to_channel_dimension_format(a_,				a_		) for image in images]

    _snake_case							:		int								=  {"""pixel_values""": images}
    return BatchFeature(data=a_,				tensor_type=a_		)








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


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


