




"""simple docstring"""
import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a_					:     Optional[Any]					    =  3





def     UpperCAmelCase					(      A__:     int  )	->  int:
			print('Generating primitive root of p'  )
			while True:
						__lowerCamelCase       :						Tuple													=					random.randrange(3				,					_A  )
						if pow(_A				,					2				,					_A  ) == 1:
									continue
						if pow(_A				,					_A				,					_A  ) == 1:
									continue
						return g





def     UpperCAmelCase					(      A__:     int  )	->  tuple[tuple[int, int, int, int], tuple[int, int]]:
			print('Generating prime p...'  )
			__lowerCamelCase       :						Optional[int]													=					rabin_miller.generate_large_prime(_A  )  # select large prime number.
			__lowerCamelCase       :						int													=					primitive_root(_A  )  # one primitive root on modulo p.
			__lowerCamelCase       :						List[Any]													=					random.randrange(3				,					_A  )  # private_key -> have to be greater than 2 for safety.
			__lowerCamelCase       :						Optional[Any]													=					cryptomath.find_mod_inverse(pow(_A				,					_A				,					_A  )				,					_A  )

			__lowerCamelCase       :						Union[str, Any]													=					(key_size, e_a, e_a, p)
			__lowerCamelCase       :						int													=					(key_size, d)

			return public_key, private_key





def     UpperCAmelCase					(      A__:     str				,					A__:     int  )	->  None:


			if os.path.exists(f'''{name}_pubkey.txt'''  ) or os.path.exists(f'''{name}_privkey.txt'''  ):
						print('\nWARNING:'  )
						print(
						    f'''\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n'''
						    'Use a different name or delete these files and re-run this program.'  )
						sys.exit()

			__lowerCamelCase							,   __lowerCamelCase       :						Optional[Any]													=					generate_key(_A  )
			print(f'''\nWriting public key to file {name}_pubkey.txt...'''  )
			with open(f'''{name}_pubkey.txt'''				,					'w'  ) as fo:
						fo.write(f'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'''  )

			print(f'''Writing private key to file {name}_privkey.txt...'''  )
			with open(f'''{name}_privkey.txt'''				,					'w'  ) as fo:
						fo.write(f'''{private_key[0]},{private_key[1]}'''  )





def     UpperCAmelCase					(      )	->  None:
			print('Making key files...'  )
			make_key_files('elgamal'				,					2048  )
			print('Key files generation successful'  )


if __name__ == "__main__":
					main()




'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
					return resize(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    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_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

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

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

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

					if do_color_quantize:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       ChannelDimension.LAST) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							lowerCAmelCase     =    np.array(__lowerCAmelCase)
							lowerCAmelCase     =    color_quantize(__lowerCAmelCase			,       __lowerCAmelCase).reshape(images.shape[:-1])

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							lowerCAmelCase     =    list(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




