





"""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


lowerCAmelCase__   	=	logging.get_logger(__name__)





class 				_lowerCamelCase					(				snake_case__       ):
						UpperCAmelCase_       =	["""pixel_values"""]


						def __init__(self      ,							__a = True      ,							__a = 32      ,							__a=PILImageResampling.BILINEAR      ,							__a = True      ,							**__a      ,							)       ->      List[str]:
								UpperCamelCase      =		do_resize
								UpperCamelCase      =		do_rescale
								UpperCamelCase      =		size_divisor
								UpperCamelCase      =		resample
								super().__init__(**_A						)


						def 			snake_case_      (self      ,							__a      ,							__a      ,							__a      ,							__a = None      ,							**__a						)       ->      Optional[int]:
								UpperCamelCase      , UpperCamelCase      =		get_image_size(_A						)
								# Rounds the height and width down to the closest multiple of size_divisor
								UpperCamelCase      =		height // size_divisor * size_divisor
								UpperCamelCase      =		width // size_divisor * size_divisor
								UpperCamelCase      =		resize(_A      ,							(new_h, new_w)      ,							resample=_A      ,							data_format=_A      ,							**_A						)
								return image


						def 			snake_case_      (self      ,							__a      ,							__a      ,							__a = None      ,							**__a						)       ->      Tuple:
								return rescale(image=_A      ,							scale=_A      ,							data_format=_A      ,							**_A						)




						def 			snake_case_      (self      ,							__a      ,							__a = None      ,							__a = None      ,							__a=None      ,							__a = None      ,							__a = None      ,							__a = ChannelDimension.FIRST      ,							**__a      ,							)       ->      Any:
								UpperCamelCase      =		do_resize if do_resize is not None else self.do_resize
								UpperCamelCase      =		do_rescale if do_rescale is not None else self.do_rescale
								UpperCamelCase      =		size_divisor if size_divisor is not None else self.size_divisor
								UpperCamelCase      =		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"						)

								UpperCamelCase      =		make_list_of_images(_A						)

								if not valid_images(_A						):
										raise ValueError("Invalid image(s)"						)

								# All transformations expect numpy arrays.
								UpperCamelCase      =		[to_numpy_array(_A						) for img in images]

								if do_resize:
										UpperCamelCase      =		[self.resize(_A      ,							size_divisor=_A      ,							resample=_A						) for image in images]

								if do_rescale:
										UpperCamelCase      =		[self.rescale(_A      ,							scale=1 / 2_55						) for image in images]

								UpperCamelCase      =		[to_channel_dimension_format(_A      ,							_A						) for image in images]

								UpperCamelCase      =		{"pixel_values": images}
								return BatchFeature(data=_A      ,							tensor_type=_A						)












def 	_a      (						SCREAMING_SNAKE_CASE_							:				int = 1_00_00_00     ):
					__lowerCAmelCase				   =       [i - 1 for i in range(limit + 1     )]

					for i in range(2      ,      limit + 1     ):
										if phi[i] == i - 1:
															for j in range(2 * i      ,      limit + 1      ,      SCREAMING_SNAKE_CASE_     ):
																				phi[j] -= phi[j] // i

					return sum(phi[2 : limit + 1]     )


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




