





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


UpperCamelCase_           =logging.get_logger(__name__)




class  _a						(      _lowerCAmelCase				):
				UpperCamelCase								=    ['''pixel_values''']






				def __init__( self		:    str, lowerCAmelCase__		:    bool = True, lowerCAmelCase__		:    Dict[str, int] = None, lowerCAmelCase__		:    PILImageResampling = PIL.Image.NEAREST, lowerCAmelCase__		:    bool = False, lowerCAmelCase__		:    Dict[str, int] = None, lowerCAmelCase__		:    Union[int, float] = 1 / 2_5_5, lowerCAmelCase__		:    bool = False, lowerCAmelCase__		:    bool = True, lowerCAmelCase__		:    bool = True, lowerCAmelCase__		:    Optional[Union[float, List[float]]] = None, lowerCAmelCase__		:    Optional[Union[float, List[float]]] = None, lowerCAmelCase__		:    bool = True, **lowerCAmelCase__		:    Any, )	->      None:




										'''simple docstring'''



										super().__init__(**lowerCAmelCase__							)
										_UpperCamelCase			:					List[str]     =    size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
										_UpperCamelCase			:					List[Any]     =    get_size_dict(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
										_UpperCamelCase			:					Optional[Any]     =    get_size_dict(lowerCAmelCase__, param_name='''crop_size'''							)

										_UpperCamelCase			:					int     =    do_resize
										_UpperCamelCase			:					List[Any]     =    size
										_UpperCamelCase			:					List[str]     =    resample
										_UpperCamelCase			:					Optional[int]     =    do_center_crop
										_UpperCamelCase			:					Optional[int]     =    crop_size
										_UpperCamelCase			:					int     =    do_rescale
										_UpperCamelCase			:					Tuple     =    rescale_factor
										_UpperCamelCase			:					int     =    rescale_offset
										_UpperCamelCase			:					int     =    do_normalize
										_UpperCamelCase			:					int     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCamelCase			:					Tuple     =    image_std if image_std is not None else IMAGENET_STANDARD_STD
										_UpperCamelCase			:					Dict     =    include_top






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    np.ndarray, lowerCAmelCase__		:    Dict[str, int], lowerCAmelCase__		:    PILImageResampling = PIL.Image.NEAREST, lowerCAmelCase__		:    Optional[Union[str, ChannelDimension]] = None, **lowerCAmelCase__		:    Tuple, )	->      np.ndarray:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    get_size_dict(lowerCAmelCase__							)
										if "height" not in size or "width" not in size:
																raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""							)
										return resize(
										    lowerCAmelCase__, size=(size['''height'''], size['''width''']), resample=lowerCAmelCase__, data_format=lowerCAmelCase__, **lowerCAmelCase__							)






				def 				snake_case	( self		:    Optional[int], lowerCAmelCase__		:    np.ndarray, lowerCAmelCase__		:    Dict[str, int], lowerCAmelCase__		:    Optional[Union[str, ChannelDimension]] = None, **lowerCAmelCase__		:    Tuple, )	->      np.ndarray:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    get_size_dict(lowerCAmelCase__							)
										if "height" not in size or "width" not in size:
																raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""							)
										return center_crop(lowerCAmelCase__, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase__, **lowerCAmelCase__							)






				def 				snake_case	( self		:    int, lowerCAmelCase__		:    np.ndarray, lowerCAmelCase__		:    Union[int, float], lowerCAmelCase__		:    bool = True, lowerCAmelCase__		:    Optional[Union[str, ChannelDimension]] = None, **lowerCAmelCase__		:    Any, )	->      Dict:




										'''simple docstring'''



										if offset:
																_UpperCamelCase			:					Union[str, Any]     =    (image - 127.5) * scale
																if data_format is not None:
																						_UpperCamelCase			:					Dict     =    to_channel_dimension_format(lowerCAmelCase__, lowerCAmelCase__							)
																_UpperCamelCase			:					List[str]     =    rescaled_image.astype(np.floataa							)
										else:
																_UpperCamelCase			:					Optional[int]     =    rescale(lowerCAmelCase__, scale=lowerCAmelCase__, data_format=lowerCAmelCase__, **lowerCAmelCase__							)
										return rescaled_image






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    np.ndarray, lowerCAmelCase__		:    Union[float, List[float]], lowerCAmelCase__		:    Union[float, List[float]], lowerCAmelCase__		:    Optional[Union[str, ChannelDimension]] = None, **lowerCAmelCase__		:    Optional[int], )	->      np.ndarray:




										'''simple docstring'''



										return normalize(lowerCAmelCase__, mean=lowerCAmelCase__, std=lowerCAmelCase__, data_format=lowerCAmelCase__, **lowerCAmelCase__							)






				def 				snake_case	( self		:    Union[str, Any], lowerCAmelCase__		:    ImageInput, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    Dict[str, int] = None, lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    Dict[str, int] = None, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    float = None, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    Optional[Union[float, List[float]]] = None, lowerCAmelCase__		:    Optional[Union[float, List[float]]] = None, lowerCAmelCase__		:    bool = None, lowerCAmelCase__		:    Optional[Union[str, TensorType]] = None, lowerCAmelCase__		:    ChannelDimension = ChannelDimension.FIRST, **lowerCAmelCase__		:    Optional[int], )	->      PIL.Image.Image:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase			:					int     =    resample if resample is not None else self.resample
										_UpperCamelCase			:					Dict     =    do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCamelCase			:					str     =    do_rescale if do_rescale is not None else self.do_rescale
										_UpperCamelCase			:					Any     =    rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCamelCase			:					Tuple     =    rescale_offset if rescale_offset is not None else self.rescale_offset
										_UpperCamelCase			:					Tuple     =    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCamelCase			:					Any     =    image_mean if image_mean is not None else self.image_mean
										_UpperCamelCase			:					str     =    image_std if image_std is not None else self.image_std
										_UpperCamelCase			:					Dict     =    include_top if include_top is not None else self.include_top

										_UpperCamelCase			:					List[Any]     =    size if size is not None else self.size
										_UpperCamelCase			:					List[Any]     =    get_size_dict(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    crop_size if crop_size is not None else self.crop_size
										_UpperCamelCase			:					str     =    get_size_dict(lowerCAmelCase__, param_name='''crop_size'''							)

										_UpperCamelCase			:					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_resize and size is None or resample is None:
																raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

										if do_center_crop and crop_size is None:
																raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

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

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

										# All transformations expect numpy arrays.
										_UpperCamelCase			:					str     =    [to_numpy_array(lowerCAmelCase__							) for image in images]

										if do_resize:
																_UpperCamelCase			:					Tuple     =    [self.resize(image=lowerCAmelCase__, size=lowerCAmelCase__, resample=lowerCAmelCase__							) for image in images]

										if do_center_crop:
																_UpperCamelCase			:					Dict     =    [self.center_crop(image=lowerCAmelCase__, size=lowerCAmelCase__							) for image in images]

										if do_rescale:
																_UpperCamelCase			:					Tuple     =    [self.rescale(image=lowerCAmelCase__, scale=lowerCAmelCase__, offset=lowerCAmelCase__							) for image in images]

										if do_normalize:
																_UpperCamelCase			:					List[Any]     =    [self.normalize(image=lowerCAmelCase__, mean=lowerCAmelCase__, std=lowerCAmelCase__							) for image in images]

										if include_top:
																_UpperCamelCase			:					str     =    [self.normalize(image=lowerCAmelCase__, mean=[0, 0, 0], std=lowerCAmelCase__							) for image in images]

										_UpperCamelCase			:					Tuple     =    [to_channel_dimension_format(lowerCAmelCase__, lowerCAmelCase__							) for image in images]

										_UpperCamelCase			:					Optional[int]     =    {'''pixel_values''': images}
										return BatchFeature(data=lowerCAmelCase__, tensor_type=lowerCAmelCase__							)










"""simple docstring"""




def        a_				(							_lowercase = 10**6   ):

						if n <= 0:
												raise ValueError('''Please enter an integer greater than 0'''   )

						_UpperCamelCase			:					Any     =    list(range(n + 1   )   )
						for number in range(2       ,						n + 1   ):
												if phi[number] == number:
																		phi[number] -= 1
																		for multiple in range(number * 2       ,						n + 1       ,						_lowercase   ):
																								_UpperCamelCase			:					List[str]     =    (phi[multiple] // number) * (number - 1)

						_UpperCamelCase			:					Union[str, Any]     =    1
						for number in range(1       ,						n + 1   ):
												if (answer / phi[answer]) < (number / phi[number]):
																		_UpperCamelCase			:					List[str]     =    number

						return answer


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



