code: 
  from __future__ import annotations
  
  import math
  from itertools import permutations
  
  def    __snake_case       (  __magic_name__	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				if 1 < number < 4:
  								# 2 and 3 are primes
  								return True
  				elif number < 2 or number % 2 == 0 or number % 3 == 0:
  								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  								return False
  
  				# All primes number are in format of 6k +/- 1
  				for i in range(5		,						int(math.sqrt(__magic_name__	) + 1	)		,						6	):
  								if number % i == 0 or number % (i + 2) == 0:
  												return False
  				return True
  
  def    __snake_case       (  __magic_name__ = 7	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				lowercase						    =			"".join(str(__magic_name__	) for i in range(1		,						n + 1	)	)
  				lowercase						    =			[int("".join(__magic_name__	)	) for i in permutations(__magic_name__		,						__magic_name__	)]
  				lowercase						    =			[num for num in perm_list if is_prime(__magic_name__	)]
  				return max(__magic_name__	) if pandigitals else 0
  
  
  if __name__ == "__main__":
  						print(F"{solution() = }")
  
style_context: 
  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
  
  
  _snake_case			:       Tuple      =						logging.get_logger(__name__)
  
  
  if is_vision_available():
  						import PIL
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']
  
  
  
  
  
  						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
  										super().__init__(**lowerCAmelCase__    )
  
  										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# The previous feature extractor size parameter was in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  
  										lowercase						    =			do_resize
  										lowercase						    =			size
  										lowercase						    =			resample
  										lowercase						    =			do_thumbnail
  										lowercase						    =			do_align_long_axis
  										lowercase						    =			do_pad
  										lowercase						    =			do_rescale
  										lowercase						    =			rescale_factor
  										lowercase						    =			do_normalize
  										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										if (output_width < output_height and input_width > input_height) or (
  										    output_width > output_height and input_width < input_height
  										):
  														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )
  
  										if data_format is not None:
  														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										return image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  
  										lowercase						    =			output_width - input_width
  										lowercase						    =			output_height - input_height
  
  										if random_padding:
  														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
  														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
  										else:
  														lowercase						    =			delta_height // 2
  														lowercase						    =			delta_width // 2
  
  										lowercase						    =			delta_height - pad_top
  										lowercase						    =			delta_width - pad_left
  
  										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
  										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
  										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
  										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										# We always resize to the smallest of either the input or output size.
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										if height == input_height and width == input_width:
  														return image
  
  										if input_height > input_width:
  														lowercase						    =			int(input_width * height / input_height    )
  										elif input_width > input_height:
  														lowercase						    =			int(input_height * width / input_width    )
  
  										return resize(
  										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			min(size["height"]		,						size["width"]    )
  										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
  										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  										return resized_image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
  										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
  										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
  										lowercase						    =			do_resize if do_resize is not None else self.do_resize
  										lowercase						    =			size if size is not None else self.size
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# Previous feature extractor had size in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			resample if resample is not None else self.resample
  										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  										lowercase						    =			do_pad if do_pad is not None else self.do_pad
  										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
  										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
  										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
  										lowercase						    =			image_mean if image_mean is not None else self.image_mean
  										lowercase						    =			image_std if image_std is not None else self.image_std
  
  										lowercase						    =			make_list_of_images(lowerCAmelCase__    )
  
  										if not valid_images(lowerCAmelCase__    ):
  														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.
  										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]
  
  										if do_align_long_axis:
  														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_resize:
  														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]
  
  										if do_thumbnail:
  														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_pad:
  														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]
  
  										if do_rescale:
  														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]
  
  										if do_normalize:
  														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			{"pixel_values": images}
  										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
  