


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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case			:      Union[str, Any] 							=   logging.get_logger(__name__)



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						["pixel_values"]





							def __init__(    self				, A_ = True				, A_ = None				, A_ = PILImageResampling.BILINEAR				, A_ = True				, A_ = 1 / 255				, A_ = True				, A_ = None				, A_ = None				, **A_				, )->	None:
											super().__init__(**A_	)
											_SCREAMING_SNAKE_CASE      = size if size is not None else {'height': 224, 'width': 224}
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)
											_SCREAMING_SNAKE_CASE      = do_resize
											_SCREAMING_SNAKE_CASE      = do_rescale
											_SCREAMING_SNAKE_CASE      = do_normalize
											_SCREAMING_SNAKE_CASE      = size
											_SCREAMING_SNAKE_CASE      = resample
											_SCREAMING_SNAKE_CASE      = rescale_factor
											_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 		__magic_name__			(    self				, A_				, A_				, A_ = PILImageResampling.BILINEAR				, A_ = None				, **A_				, )->	np.ndarray:
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)
											if "height" not in size or "width" not in size:
															raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
											return resize(
											    A_				, size=(size['height'], size['width'])				, resample=A_				, data_format=A_				, **A_	)





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





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_ = None				, **A_				, )->	np.ndarray:
											return normalize(A_				, mean=A_				, std=A_				, data_format=A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = ChannelDimension.FIRST				, **A_				, )->	List[Any]:
											_SCREAMING_SNAKE_CASE      = do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE      = do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE      = do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE      = resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE      = rescale_factor if rescale_factor is not None else self.rescale_factor
											_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      = size if size is not None else self.size
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)

											_SCREAMING_SNAKE_CASE      = 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_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.'	)

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

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

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

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

											_SCREAMING_SNAKE_CASE      = [to_channel_dimension_format(A_				, A_	) for image in images]

											_SCREAMING_SNAKE_CASE      = {'pixel_values': images}
											return BatchFeature(data=A_				, tensor_type=A_	)








from __future__ import annotations

from collections import Counter


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [True] * n
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = True

				for i in range(3   ,int(n**0.5 + 1							)   ,2							):
								_SCREAMING_SNAKE_CASE      = i * 2
								while index < n:
												_SCREAMING_SNAKE_CASE      = False
												_SCREAMING_SNAKE_CASE      = index + i

				_SCREAMING_SNAKE_CASE      = [2]

				for i in range(3   ,UpperCAmelCase__   ,2							):
								if is_prime[i]:
												primes.append(UpperCAmelCase__							)

				return primes


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = str(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = []
				_SCREAMING_SNAKE_CASE      = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

				for duplicate in Counter(UpperCAmelCase__							) - Counter(set(UpperCAmelCase__							)							):
								_SCREAMING_SNAKE_CASE      = [int(number_str.replace(UpperCAmelCase__   ,UpperCAmelCase__							)							) for digit in digits]
								replacements.append(UpperCAmelCase__							)

				return replacements


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__ = 8							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()

				# Filter primes with less than 3 replaceable digits
				_SCREAMING_SNAKE_CASE      = {
				    x for x in set(prime_sieve(100_0000							)							) if len(str(UpperCAmelCase__							)							) - len(set(str(UpperCAmelCase__							)							)							) >= 3
				}

				for prime in primes:
								if prime in numbers_checked:
												continue

								_SCREAMING_SNAKE_CASE      = digit_replacements(UpperCAmelCase__							)

								for family in replacements:
												numbers_checked.update(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = primes.intersection(UpperCAmelCase__							)

												if len(UpperCAmelCase__							) != family_length:
																continue

												return min(UpperCAmelCase__							)

				return -1


if __name__ == "__main__":
							print(solution())
