





"""simple docstring"""



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


a_					=     logging.get_logger(__name__)






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 32   ,		__lowerCamelCase=PILImageResampling.BILINEAR   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		List[str]							=	do_resize
     __A       :		Dict							=	do_rescale
     __A       :		List[str]							=	size_divisor
     __A       :		Union[str, Any]							=	resample
     super().__init__(**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A					,			__A       :		Optional[int]							=	get_image_size(__lowerCamelCase    )
     # Rounds the height and width down to the closest multiple of size_divisor
     __A       :		Tuple							=	height // size_divisor * size_divisor
     __A       :		Any							=	width // size_divisor * size_divisor
     __A       :		Dict							=	resize(__lowerCamelCase   ,		(new_h, new_w)   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )
     return image

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     return rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase=None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		int							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Dict							=	do_rescale if do_rescale is not None else self.do_rescale
     __A       :		Optional[int]							=	size_divisor if size_divisor is not None else self.size_divisor
     __A       :		Optional[int]							=	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'''    )

     __A       :		List[Any]							=	make_list_of_images(__lowerCamelCase    )

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

     # All transformations expect numpy arrays.
     __A       :		int							=	[to_numpy_array(__lowerCamelCase    ) for img in images]

     if do_resize:
         __A       :		Tuple							=	[self.resize(__lowerCamelCase   ,		size_divisor=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_rescale:
         __A       :		str							=	[self.rescale(__lowerCamelCase   ,		scale=1 / 255    ) for image in images]

     __A       :		Optional[int]							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int = 2000000		)  ->int:



    '''simple docstring'''







    __A       :		List[Any]							=	[0 for i in range(n + 1		)]
    __A       :		str							=	1
    __A       :		Dict							=	1

    for i in range(2							,int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i							,n + 1							,snake_case_		):
                __A       :		List[Any]							=	1
    __A       :		Union[str, Any]							=	0
    for i in range(snake_case_		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


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




