import os
import random
import sys

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



def 	__A		(      ):



 """simple docstring"""


 print("Making key files..."       )
 make_key_files("rsa"				,					1024       )
 print("Key files generation successful."       )



def 	__A		(      _A       ):



 """simple docstring"""


 print("Generating prime p..."       )
 __a							=	rabinMiller.generate_large_prime(_A       )
 print("Generating prime q..."       )
 __a							=	rabinMiller.generate_large_prime(_A       )
 __a							=	p * q

 print("Generating e that is relatively prime to (p - 1) * (q - 1)..."       )
 while True:
  __a							=	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..."       )
 __a							=	cryptoMath.find_mod_inverse(_A				,					(p - 1) * (q - 1)       )

 __a							=	(n, e)
 __a							=	(n, d)
 return (public_key, private_key)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 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()

 __a							,  __a							=	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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


