






import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812

def      lowerCAmelCase__	(    ):
		print("Making key files..."    )
		make_key_files("rsa"  ,      10_24    )
		print("Key files generation successful."    )

def      lowerCAmelCase__	(    _a       :			int    ):
		print("Generating prime p..."    )
		snake_case_				:							int						 =       rabinMiller.generate_large_prime(_a    )
		print("Generating prime q..."    )
		snake_case_				:							Union[str, Any]						 =       rabinMiller.generate_large_prime(_a    )
		snake_case_				:							Tuple						 =       p * q

		print("Generating e that is relatively prime to (p - 1) * (q - 1)..."    )
		while True:
				snake_case_				:							int						 =       random.randrange(2 ** (key_size - 1)  ,      2 ** (key_size)    )
				if cryptoMath.gcd(_a  ,      (p - 1) * (q - 1)    ) == 1:
						break

		print("Calculating d that is mod inverse of e..."    )
		snake_case_				:							str						 =       cryptoMath.find_mod_inverse(_a  ,      (p - 1) * (q - 1)    )

		snake_case_				:							Optional[Any]						 =       (n, e)
		snake_case_				:							Union[str, Any]						 =       (n, d)
		return (public_key, private_key)

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		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()

		snake_case_ ,       snake_case_				:							Optional[int]						 =       generate_key(_a    )
		print(F'''\nWriting public key to file {name}_pubkey.txt...'''    )
		with open(F'''{name}_pubkey.txt'''  ,      "w"    ) as out_file:
				out_file.write(F'''{key_size},{public_key[0]},{public_key[1]}'''    )

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


if __name__ == "__main__":
							main()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[Any]						 =       initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
