
'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class 			lowerCAmelCase :
  a							:					str       =					field(
      metadata={"""help""": """The output directory where the model will be written."""}		,     )
  a							:					str       =					field(
      metadata={
          """help""": (
              """The encoder model checkpoint for weights initialization."""
              """Don't set if you want to train an encoder model from scratch."""
          )
      }		,     )
  a							:					str       =					field(
      metadata={
          """help""": (
              """The decoder model checkpoint for weights initialization."""
              """Don't set if you want to train a decoder model from scratch."""
          )
      }		,     )
  a							:					Optional[str]       =					field(
      default=__lowerCAmelCase		,     metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}			)
  a							:					Optional[str]       =					field(
      default=__lowerCAmelCase		,     metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}			)



def 			_a		(	):
      _SCREAMING_SNAKE_CASE				= HfArgumentParser((ModelArguments,)  )
      (_SCREAMING_SNAKE_CASE				)				= parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

      # Use explicit specified encoder config
      if model_args.encoder_config_name:
            _SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(model_args.encoder_config_name  )
      # Use pretrained encoder model's config
      else:
            _SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

      # Use explicit specified decoder config
      if model_args.decoder_config_name:
            _SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(model_args.decoder_config_name  )
      # Use pretrained decoder model's config
      else:
            _SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

      # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
      _SCREAMING_SNAKE_CASE				= True
      _SCREAMING_SNAKE_CASE				= True

      _SCREAMING_SNAKE_CASE				= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
          encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,			decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,			encoder_config=_SCREAMING_SNAKE_CASE	,			decoder_config=_SCREAMING_SNAKE_CASE	,			)

      # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
      _SCREAMING_SNAKE_CASE				= decoder_config.decoder_start_token_id
      _SCREAMING_SNAKE_CASE				= decoder_config.pad_token_id
      if decoder_start_token_id is None:
            _SCREAMING_SNAKE_CASE				= decoder_config.bos_token_id
      if pad_token_id is None:
            _SCREAMING_SNAKE_CASE				= decoder_config.eos_token_id

      # This is necessary to make Flax's generate() work
      _SCREAMING_SNAKE_CASE				= decoder_config.eos_token_id
      _SCREAMING_SNAKE_CASE				= decoder_start_token_id
      _SCREAMING_SNAKE_CASE				= pad_token_id

      _SCREAMING_SNAKE_CASE				= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

      _SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
      _SCREAMING_SNAKE_CASE				= tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

      model.save_pretrained(model_args.output_dir  )
      image_processor.save_pretrained(model_args.output_dir  )
      tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
      main()

'''simple docstring'''







import os
import random
import sys

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



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						print("Generating prime p..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						print("Generating prime q..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= p * q

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

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   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()

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= generate_key(_SCREAMING_SNAKE_CASE  )
						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()