

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					Optional[Any]:
	__lowerCamelCase    :    List[str] 	=			OmegaConf.load(lowerCamelCase__      )
	if display:
		print(yaml.dump(OmegaConf.to_container(lowerCamelCase__      )      )      )
	return config






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	if conf_path is None:
		__lowerCamelCase    :    Dict 	=			'./model_checkpoints/vqgan_only.yaml'
	__lowerCamelCase    :    str 	=			load_config(lowerCamelCase__     ,			display=lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			VQModel(**config.model.params      )
	if ckpt_path is None:
		__lowerCamelCase    :    Any 	=			'./model_checkpoints/vqgan_only.pt'
	__lowerCamelCase    :    Optional[int] 	=			torch.load(lowerCamelCase__     ,			map_location=lowerCamelCase__      )
	if ".ckpt" in ckpt_path:
		__lowerCamelCase    :    Optional[Any] 	=			sd['state_dict']
	model.load_state_dict(lowerCamelCase__     ,			strict=lowerCamelCase__      )
	model.to(lowerCamelCase__      )
	del sd
	return model






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			model.encode(lowerCamelCase__      )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
	__lowerCamelCase    :    Dict 	=			model.decode(lowerCamelCase__      )
	return xrec






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					str:
	__lowerCamelCase	,			__lowerCamelCase    :    str 	=			string.rsplit('.'     ,			1      )
	if reload:
		__lowerCamelCase    :    int 	=			importlib.import_module(lowerCamelCase__      )
		importlib.reload(lowerCamelCase__      )
	return getattr(importlib.import_module(lowerCamelCase__     ,			package=lowerCamelCase__      )     ,			cls      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Tuple:
	if "target" not in config:
		raise KeyError('Expected key `target` to instantiate.'      )
	return get_obj_from_str(config['target']      )(**config.get('params'     ,			{}      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=True     ,			lowerCamelCase__=True      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			instantiate_from_config(lowerCamelCase__      )
	if sd is not None:
		model.load_state_dict(lowerCamelCase__      )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	# load the specified checkpoint
	if ckpt:
		__lowerCamelCase    :    Dict 	=			torch.load(lowerCamelCase__     ,			map_location='cpu'      )
		__lowerCamelCase    :    Any 	=			pl_sd['global_step']
		print(F"loaded model from global step {global_step}."      )
	else:
		__lowerCamelCase    :    int 	=			{'state_dict': None}
		__lowerCamelCase    :    Optional[Any] 	=			None
	__lowerCamelCase    :    Optional[Any] 	=			load_model_from_config(config.model     ,			pl_sd['state_dict']     ,			gpu=lowerCamelCase__     ,			eval_mode=lowerCamelCase__      )['model']
	return model, global_step







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a		=3






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






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

	__lowerCamelCase    :    int 	=			(key_size, e_a, e_a, p)
	__lowerCamelCase    :    str 	=			(key_size, d)

	return public_key, private_key






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					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    :    List[Any] 	=			generate_key(lowerCamelCase__      )
	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        SCREAMING_SNAKE_CASE__	(  )  ->					None:
	print('Making key files...'      )
	make_key_files('elgamal'     ,			2_0_4_8      )
	print('Key files generation successful'      )


if __name__ == "__main__":
					main()




