




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


UpperCamelCase__    			=			logging.get_logger(__name__)
class 			a__    (					snake_case__      ):
						_a							:    Union[str, Any]					=    ["""pixel_values"""]






						def __init__(   self		,			_A = True		,			_A = 3_2		,			_A=PILImageResampling.BILINEAR		,			_A = True		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       do_rescale
											__lowerCAmelCase				   =       size_divisor
											__lowerCAmelCase				   =       resample
											super().__init__(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       get_image_size(_A			)
											# Rounds the height and width down to the closest multiple of size_divisor
											__lowerCAmelCase				   =       height // size_divisor * size_divisor
											__lowerCAmelCase				   =       width // size_divisor * size_divisor
											__lowerCAmelCase				   =       resize(_A		,			(new_h, new_w)		,			resample=_A		,			data_format=_A		,			**_A			)
											return image






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None		,			_A=None		,			_A = None		,			_A = None		,			_A = ChannelDimension.FIRST		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase				   =       do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase				   =       size_divisor if size_divisor is not None else self.size_divisor
											__lowerCAmelCase				   =       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"			)

											__lowerCAmelCase				   =       make_list_of_images(_A			)

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

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

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

											if do_rescale:
																__lowerCAmelCase				   =       [self.rescale(_A		,			scale=1 / 2_5_5			) for image in images]

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

											__lowerCAmelCase				   =       {"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())




