




"""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


lowercase__     						=						logging.get_logger(__name__)






class lowerCAmelCase__	(     __lowerCAmelCase						):






			'''simple docstring'''



			lowerCamelCase__      =			['''pixel_values''']
			def __init__(  self  ,      lowercase = True  ,      lowercase = 1 / 255  ,      lowercase = True  ,      lowercase = 8  ,      **lowercase  ,      ):
							super().__init__(**lowerCAmelCase_  )

							_lowerCamelCase     :			Union[str, Any]    =    do_rescale
							_lowerCamelCase     :			str    =    rescale_factor
							_lowerCamelCase     :			List[str]    =    do_pad
							_lowerCamelCase     :			List[Any]    =    pad_size
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      **lowercase  ):
							return rescale(lowerCAmelCase_  ,      scale=lowerCAmelCase_  ,      data_format=lowerCAmelCase_  ,      **lowerCAmelCase_  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = None  ):
							_lowerCamelCase, _lowerCamelCase     :			Any    =    get_image_size(lowerCAmelCase_  )
							_lowerCamelCase     :			Optional[int]    =    (old_height // size + 1) * size - old_height
							_lowerCamelCase     :			Union[str, Any]    =    (old_width // size + 1) * size - old_width

							return pad(lowerCAmelCase_  ,      ((0, pad_height), (0, pad_width))  ,      mode='symmetric'  ,      data_format=lowerCAmelCase_  )






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = ChannelDimension.FIRST  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    do_rescale if do_rescale is not None else self.do_rescale
							_lowerCamelCase     :			Any    =    rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowerCamelCase     :			str    =    do_pad if do_pad is not None else self.do_pad
							_lowerCamelCase     :			List[Any]    =    pad_size if pad_size is not None else self.pad_size

							_lowerCamelCase     :			Any    =    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_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

							# All transformations expect numpy arrays.
							_lowerCamelCase     :			Any    =    [to_numpy_array(lowerCAmelCase_  ) for image in images]

							if do_rescale:
											_lowerCamelCase     :			List[Any]    =    [self.rescale(image=lowerCAmelCase_  ,      scale=lowerCAmelCase_  ) for image in images]

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

							_lowerCamelCase     :			Tuple    =    [to_channel_dimension_format(lowerCAmelCase_  ,      lowerCAmelCase_  ) for image in images]

							_lowerCamelCase     :			Union[str, Any]    =    {'pixel_values': images}
							return BatchFeature(data=lowerCAmelCase_  ,      tensor_type=lowerCAmelCase_  )
from math import isqrt






def  snake_case  (       snake_case__			:int)						->   bool:
       return all(number % divisor != 0 for divisor in range(2   ,	isqrt(snake_case__) + 1))






def  snake_case  (       snake_case__			:int = 10**6)						->   int:
       _A        =  0
       _A        =  1
       _A        =  7
       while prime_candidate < max_prime:
              primes_count += is_prime(snake_case__)

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


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




