

'''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


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







class 							A__    (       A__  ):
  A__		 =				['pixel_values']





  def __init__(		self      :	Dict							,					_a      :	bool = True							,					_a      :	Union[int, float] = 1 / 255							,					_a      :	bool = True							,					_a      :	int = 8							,					**_a      :	int							,					)	->    None:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =do_rescale
       _SCREAMING_SNAKE_CASE							    =rescale_factor
       _SCREAMING_SNAKE_CASE							    =do_pad
       _SCREAMING_SNAKE_CASE							    =pad_size





  def 						A  (		self      :	int							,					_a      :	np.ndarray							,					_a      :	float							,					_a      :	Optional[Union[str, ChannelDimension]] = None							,					**_a      :	Optional[int] )	->    np.ndarray:





       '''simple docstring'''






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





  def 						A  (		self      :	str							,					_a      :	np.ndarray							,					_a      :	int							,					_a      :	Optional[Union[str, ChannelDimension]] = None )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_image_size(_a )
       _SCREAMING_SNAKE_CASE							    =(old_height // size + 1) * size - old_height
       _SCREAMING_SNAKE_CASE							    =(old_width // size + 1) * size - old_width

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





  def 						A  (		self      :	str							,					_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      :	Dict							,					)	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE							    =rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE							    =do_pad if do_pad is not None else self.do_pad
       _SCREAMING_SNAKE_CASE							    =pad_size if pad_size is not None else self.pad_size

       _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_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_rescale:
            _SCREAMING_SNAKE_CASE							    =[self.rescale(image=_a							,					scale=_a ) for image in images]

       if do_pad:
            _SCREAMING_SNAKE_CASE							    =[self.pad(_a							,					size=_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 )



'''simple docstring'''
from __future__ import annotations

import math


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """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(_UpperCamelCase				) + 1				)							, 6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =str(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =[n]
     for i in range(1							, len(_UpperCamelCase				)				):
          list_nums.append(int(str_num[i:]				)				)
          list_nums.append(int(str_num[:-i]				)				)
     return list_nums


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			bool:
     """simple docstring"""




     if len(str(_UpperCamelCase				)				) > 3:
          if not is_prime(int(str(_UpperCamelCase				)[-3:]				)				) or not is_prime(int(str(_UpperCamelCase				)[:3]				)				):
               return False
     return True


def        _lowerCAmelCase					(						_UpperCamelCase						:     int = 11				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =13
     while len(_UpperCamelCase				) != count:
          if validate(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =list_truncated_nums(_UpperCamelCase				)
               if all(is_prime(_UpperCamelCase				) for i in list_nums				):
                    list_truncated_primes.append(_UpperCamelCase				)
          num += 2
     return list_truncated_primes


def        _lowerCAmelCase					(						)      ->			int:
     """simple docstring"""




     return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(f'''{sum(compute_truncated_primes(1_1)) = }''')
