

import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class        a__       (   _snake_case       ):







		"""simple docstring"""




		def __init__(			self    : Tuple				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : int				,				)					->Union[str, Any]:
								"""simple docstring"""







								super().__init__()
								SCREAMING_SNAKE_CASE :      str       =   value_function
								SCREAMING_SNAKE_CASE :      str       =   unet
								SCREAMING_SNAKE_CASE :      List[str]       =   scheduler
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   env
								SCREAMING_SNAKE_CASE :      List[Any]       =   env.get_dataset()
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   {}
								for key in self.data.keys():
														try:
																				SCREAMING_SNAKE_CASE :      int       =   self.data[key].mean()
														except:  # noqa: E722
																				pass
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   {}
								for key in self.data.keys():
														try:
																				SCREAMING_SNAKE_CASE :      str       =   self.data[key].std()
														except:  # noqa: E722
																				pass
								SCREAMING_SNAKE_CASE :      Any       =   env.observation_space.shape[0]
								SCREAMING_SNAKE_CASE :      str       =   env.action_space.shape[0]


		def  _lowercase      (			self    : int				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : List[str]		)					->int:
								"""simple docstring"""







								return (x_in - self.means[key]) / self.stds[key]


		def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Union[str, Any]		)					->Dict:
								"""simple docstring"""







								return x_in * self.stds[key] + self.means[key]


		def  _lowercase      (			self    : int				,				UpperCAmelCase__    : int		)					->Union[str, Any]:
								"""simple docstring"""







								if type(snake_case_		) is dict:
														return {k: self.to_torch(snake_case_		) for k, v in x_in.items()}
								elif torch.is_tensor(snake_case_		):
														return x_in.to(self.unet.device		)
								return torch.tensor(snake_case_				,				device=self.unet.device		)


		def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Any		)					->str:
								"""simple docstring"""







								for key, val in cond.items():
														SCREAMING_SNAKE_CASE :      Optional[int]       =   val.clone()
								return x_in


		def  _lowercase      (			self    : int				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Tuple		)					->Tuple:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[int]       =   x.shape[0]
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   None
								for i in tqdm.tqdm(self.scheduler.timesteps		):
														# create batch of timesteps to pass into model
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.full((batch_size,)				,				snake_case_				,				device=self.unet.device				,				dtype=torch.long		)
														for _ in range(snake_case_		):
																				with torch.enable_grad():
																										x.requires_grad_()

																										# permute to match dimension for pre-trained models
																										SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.value_function(x.permute(0				,				2				,				1		)				,				snake_case_		).sample
																										SCREAMING_SNAKE_CASE :      Tuple       =   torch.autograd.grad([y.sum()]				,				[x]		)[0]

																										SCREAMING_SNAKE_CASE :      Optional[int]       =   self.scheduler._get_variance(snake_case_		)
																										SCREAMING_SNAKE_CASE :      Dict       =   torch.exp(0.5 * posterior_variance		)
																										SCREAMING_SNAKE_CASE :      int       =   model_std * grad

																				SCREAMING_SNAKE_CASE :      List[Any]       =   0
																				SCREAMING_SNAKE_CASE :      int       =   x.detach()
																				SCREAMING_SNAKE_CASE :      int       =   x + scale * grad
																				SCREAMING_SNAKE_CASE :      Dict       =   self.reset_xa(snake_case_				,				snake_case_				,				self.action_dim		)

														SCREAMING_SNAKE_CASE :      List[Any]       =   self.unet(x.permute(0				,				2				,				1		)				,				snake_case_		).sample.permute(0				,				2				,				1		)

														# TODO: verify deprecation of this kwarg
														SCREAMING_SNAKE_CASE :      List[str]       =   self.scheduler.step(snake_case_				,				snake_case_				,				snake_case_				,				predict_epsilon=snake_case_		)["prev_sample"]

														# apply conditions to the trajectory (set the initial state)
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.reset_xa(snake_case_				,				snake_case_				,				self.action_dim		)
														SCREAMING_SNAKE_CASE :      int       =   self.to_torch(snake_case_		)
								return x, y




		def __call__(			self    : List[str]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Any=6_4				,				UpperCAmelCase__    : Optional[Any]=3_2				,				UpperCAmelCase__    : Optional[Any]=2				,				UpperCAmelCase__    : Optional[int]=0.1		)					->Dict:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Dict       =   self.normalize(snake_case_				,				"""observations"""		)
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   obs[None].repeat(snake_case_				,				axis=0		)

								SCREAMING_SNAKE_CASE :      Dict       =   {0: self.to_torch(snake_case_		)}
								SCREAMING_SNAKE_CASE :      Optional[int]       =   (batch_size, planning_horizon, self.state_dim + self.action_dim)

								# generate initial noise and apply our conditions (to make the trajectories start at current state)
								SCREAMING_SNAKE_CASE :      List[str]       =   randn_tensor(snake_case_				,				device=self.unet.device		)
								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.reset_xa(snake_case_				,				snake_case_				,				self.action_dim		)
								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.to_torch(snake_case_		)

								# run the diffusion process
								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.run_diffusion(snake_case_				,				snake_case_				,				snake_case_				,				snake_case_		)

								# sort output trajectories by value
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   y.argsort(0				,				descending=snake_case_		).squeeze()
								SCREAMING_SNAKE_CASE :      List[str]       =   x[sorted_idx]
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   sorted_values[:, :, : self.action_dim]
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   actions.detach().cpu().numpy()
								SCREAMING_SNAKE_CASE :      List[Any]       =   self.de_normalize(snake_case_				,				key="""actions"""		)

								# select the action with the highest value
								if y is not None:
														SCREAMING_SNAKE_CASE :      Dict       =   0
								else:
														# if we didn't run value guiding, select a random action
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.random.randint(0				,				snake_case_		)

								SCREAMING_SNAKE_CASE :      List[Any]       =   denorm_actions[selected_index, 0]
								return denorm_actions







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__ :     Optional[Any]				    =    3






def 					__lowercase      (     _A			)     ->						int:
      print("""Generating primitive root of p"""			)
      while True:
            SCREAMING_SNAKE_CASE :      int       =   random.randrange(3						,    _A			)
            if pow(_A						,    2						,    _A			) == 1:
                  continue
            if pow(_A						,    _A						,    _A			) == 1:
                  continue
            return g






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

      SCREAMING_SNAKE_CASE :      str       =   (key_size, e_a, e_a, p)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (key_size, d)

      return public_key, private_key






def 					__lowercase      (     _A						,    _A			)     ->						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()

      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[str]       =   generate_key(_A			)
      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 					__lowercase      (     )     ->						None:
      print("""Making key files..."""			)
      make_key_files("""elgamal"""						,    2048			)
      print("""Key files generation successful"""			)


if __name__ == "__main__":
  main()




