





from __future__ import annotations







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    bool:
  __snake_case:						Tuple   =						str(__UpperCAmelCase)
  return len(__UpperCAmelCase) == 9 and set(__UpperCAmelCase) == set("""123456789""")







def 			A__							(    )   ->    int | None:
  for base_num in range(9999					,					4999					,					-1):
    __snake_case:						List[str]   =						10_0002 * base_num
    if is_9_pandigital(__UpperCAmelCase):
      return candidate

  for base_num in range(333					,					99					,					-1):
    __snake_case:						Dict   =						100_2003 * base_num
    if is_9_pandigital(__UpperCAmelCase):
      return candidate

  return None


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













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

