




'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)


if is_vision_available():
       import PIL
class      __UpperCAmelCase							(    _a							):



     '''simple docstring'''





     _UpperCamelCase  	=		['pixel_values']



     def __init__(  self    :	str			,					_lowercase    :	bool = True			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	PILImageResampling = PILImageResampling.BICUBIC			,					_lowercase    :	bool = True			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	bool = True			,					_lowercase    :	Union[int, float] = 1 / 255			,					_lowercase    :	bool = True			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	bool = True			,					**_lowercase    :	Any			,					)						->       None:
        super().__init__(**_lowercase)
        A_       		=   size if size is not None else {'shortest_edge': 224}
        A_       		=   get_size_dict(_lowercase			,					default_to_square=_lowercase)
        A_       		=   crop_size if crop_size is not None else {'height': 224, 'width': 224}
        A_       		=   get_size_dict(_lowercase)

        A_       		=   do_resize
        A_       		=   size
        A_       		=   resample
        A_       		=   do_center_crop
        A_       		=   crop_size
        A_       		=   do_rescale
        A_       		=   rescale_factor
        A_       		=   do_normalize
        A_       		=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_       		=   image_std if image_std is not None else OPENAI_CLIP_STD
        A_       		=   do_convert_rgb



     def 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	np.ndarray			,					_lowercase    :	Dict[str, int]			,					_lowercase    :	PILImageResampling = PILImageResampling.BICUBIC			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	Any			,					)						->       np.ndarray:
        A_       		=   get_size_dict(_lowercase			,					default_to_square=_lowercase)
        A_       		=   get_resize_output_image_size(
            _lowercase			,					size=(size['height'], size['width'])			,					default_to_square=_lowercase)
        return resize(_lowercase			,					size=_lowercase			,					resample=_lowercase			,					data_format=_lowercase			,					**_lowercase)



     def 						__snake_case	(  self    :	List[Any]			,					_lowercase    :	np.ndarray			,					_lowercase    :	Dict[str, int]			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	int			,					)						->       np.ndarray:
        A_       		=   get_size_dict(_lowercase)
        return center_crop(_lowercase			,					size=(size['height'], size['width'])			,					data_format=_lowercase			,					**_lowercase)



     def 						__snake_case	(  self    :	int			,					_lowercase    :	np.ndarray			,					_lowercase    :	Union[int, float]			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	Tuple			,					)						->       Dict:
        return rescale(_lowercase			,					scale=_lowercase			,					data_format=_lowercase			,					**_lowercase)



     def 						__snake_case	(  self    :	int			,					_lowercase    :	np.ndarray			,					_lowercase    :	Union[float, List[float]]			,					_lowercase    :	Union[float, List[float]]			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	List[str]			,					)						->       np.ndarray:
        return normalize(_lowercase			,					mean=_lowercase			,					std=_lowercase			,					data_format=_lowercase			,					**_lowercase)



     def 						__snake_case	(  self    :	Dict			,					_lowercase    :	ImageInput			,					_lowercase    :	bool = None			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	PILImageResampling = None			,					_lowercase    :	bool = None			,					_lowercase    :	int = None			,					_lowercase    :	bool = None			,					_lowercase    :	float = None			,					_lowercase    :	bool = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	bool = None			,					_lowercase    :	Optional[Union[str, TensorType]] = None			,					_lowercase    :	Optional[ChannelDimension] = ChannelDimension.FIRST			,					**_lowercase    :	List[str]			,					)						->       PIL.Image.Image:
        A_       		=   do_resize if do_resize is not None else self.do_resize
        A_       		=   size if size is not None else self.size
        A_       		=   get_size_dict(_lowercase			,					default_to_square=_lowercase)
        A_       		=   resample if resample is not None else self.resample
        A_       		=   do_center_crop if do_center_crop is not None else self.do_center_crop
        A_       		=   crop_size if crop_size is not None else self.crop_size
        A_       		=   get_size_dict(_lowercase)
        A_       		=   do_rescale if do_rescale is not None else self.do_rescale
        A_       		=   rescale_factor if rescale_factor is not None else self.rescale_factor
        A_       		=   do_normalize if do_normalize is not None else self.do_normalize
        A_       		=   image_mean if image_mean is not None else self.image_mean
        A_       		=   image_std if image_std is not None else self.image_std
        A_       		=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_       		=   make_list_of_images(_lowercase)

        if not valid_images(_lowercase):
           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:
           raise ValueError('Size 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.')

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
           A_       		=   [convert_to_rgb(_lowercase) for image in images]

        # All transformations expect numpy arrays.
        A_       		=   [to_numpy_array(_lowercase) for image in images]

        if do_resize:
           A_       		=   [self.resize(image=_lowercase			,					size=_lowercase			,					resample=_lowercase) for image in images]

        if do_center_crop:
           A_       		=   [self.center_crop(image=_lowercase			,					size=_lowercase) for image in images]

        if do_rescale:
           A_       		=   [self.rescale(image=_lowercase			,					scale=_lowercase) for image in images]

        if do_normalize:
           A_       		=   [self.normalize(image=_lowercase			,					mean=_lowercase			,					std=_lowercase) for image in images]

        A_       		=   [to_channel_dimension_format(_lowercase			,					_lowercase) for image in images]

        A_       		=   {'pixel_values': images}
        return BatchFeature(data=_lowercase			,					tensor_type=_lowercase)










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




