






'''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


__lowerCAmelCase									=   logging.get_logger(__name__)


if is_vision_available():
		import PIL





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					["pixel_values"]






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

							a_		:	int									=   do_resize
							a_		:	str									=   size
							a_		:	Union[str, Any]									=   resample
							a_		:	str									=   do_center_crop
							a_		:	int									=   crop_size
							a_		:	Any									=   do_rescale
							a_		:	List[str]									=   rescale_factor
							a_		:	Dict									=   do_normalize
							a_		:	str									=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							a_		:	Optional[int]									=   image_std if image_std is not None else OPENAI_CLIP_STD
							a_		:	Tuple									=   do_convert_rgb






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int]	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = PILImageResampling.BICUBIC	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      str	,    )      ->      np.ndarray:
							a_		:	Dict									=   get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
							if "shortest_edge" not in size:
											raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'       )
							a_		:	Dict									=   get_resize_output_image_size(__SCREAMING_SNAKE_CASE	,    size=size['''shortest_edge''']	,    default_to_square=__SCREAMING_SNAKE_CASE       )
							return resize(__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]	,    )      ->      np.ndarray:
							a_		:	List[str]									=   get_size_dict(__SCREAMING_SNAKE_CASE       )
							if "height" not in size or "width" not in size:
											raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'       )
							return center_crop(__SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Union[int, float]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      str	,    )      ->      str:
							return rescale(__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






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







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

							a_		:	Union[str, Any]									=   make_list_of_images(__SCREAMING_SNAKE_CASE       )

							if not valid_images(__SCREAMING_SNAKE_CASE       ):
											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_		:	List[str]									=   [convert_to_rgb(__SCREAMING_SNAKE_CASE       ) for image in images]

							# All transformations expect numpy arrays.
							a_		:	Tuple									=   [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_resize:
											a_		:	str									=   [self.resize(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_center_crop:
											a_		:	List[str]									=   [self.center_crop(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_rescale:
											a_		:	Optional[Any]									=   [self.rescale(image=__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE       ) for image in images]

							if do_normalize:
											a_		:	Optional[Any]									=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE       ) for image in images]

							a_		:	int									=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for image in images]

							a_		:	Union[str, Any]									=   {'''pixel_values''': images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE	,    tensor_type=__SCREAMING_SNAKE_CASE       )












'''simple docstring'''







from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__lowerCAmelCase									=   {
    # 1536-bit
    5: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 2048-bit
    14: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 3072-bit
    15: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 4096-bit
    16: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
            + 'FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 6144-bit
    17: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
            + '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
            + '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
            + 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
            + '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
            + 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
            + '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
            + '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
            + '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
            + 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
            + '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
            + 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
            + '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
            + '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
            + '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
            + 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
            + 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
            + 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
            + '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
            + 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
            + 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
            + 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
            + '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
            + '6DCC4024FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 8192-bit
    18: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
            + 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
            + '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
            + 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
            + '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
            + 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
            + '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
            + '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
            + 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
            + '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
            + '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
            + '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
            + '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
            + '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
            + '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
            + '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
            + '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
            + 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
            + '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
            + '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
            + '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
}





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      None:
							if group not in primes:
											raise ValueError('''Unsupported Group'''       )
							a_		:	Union[str, Any]									=   primes[group]['''prime''']
							a_		:	List[str]									=   primes[group]['''generator''']

							a_		:	str									=   int(hexlify(urandom(32       )       )	,    base=16       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							return hex(self.__private_key       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							a_		:	Dict									=   pow(self.generator	,    self.__private_key	,    self.prime       )
							return hex(__SCREAMING_SNAKE_CASE       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= key <= self.prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (self.prime - 1) // 2	,    self.prime       ) == 1
							)






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							a_		:	str									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    self.__private_key	,    self.prime       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()






			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= remote_public_key_str <= prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (prime - 1) // 2	,    __SCREAMING_SNAKE_CASE       ) == 1
							)







			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      str:
							a_		:	Tuple									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	Any									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	List[str]									=   primes[group]['''prime''']
							if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()




