



"""simple docstring"""







import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase:  int			     =				128
UpperCAmelCase:  Dict			     =				256


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase = DEFAULT_BLOCK_SIZE      ):
		_lowercase				: Any     =   message.encode("""ascii"""      )

		_lowercase				: Dict     =   []
		for block_start in range(0   ,				len(__UpperCAmelCase      )   ,				__UpperCAmelCase      ):
				_lowercase				: int     =   0
				for i in range(__UpperCAmelCase   ,				min(block_start + block_size   ,				len(__UpperCAmelCase      )      )      ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(__UpperCAmelCase      )
		return block_ints


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase = DEFAULT_BLOCK_SIZE      ):
		_lowercase				: list[str]     =   []
		for block_int in block_ints:
				_lowercase				: list[str]     =   []
				for i in range(block_size - 1   ,				-1   ,				-1      ):
						if len(__UpperCAmelCase      ) + i < message_length:
								_lowercase				: Optional[int]     =   block_int // (BYTE_SIZE**i)
								_lowercase				: List[Any]     =   block_int % (BYTE_SIZE**i)
								block_message.insert(0   ,				chr(__UpperCAmelCase      )      )
				message.extend(__UpperCAmelCase      )
		return "".join(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase = DEFAULT_BLOCK_SIZE      ):
		_lowercase				: int     =   []
		_lowercase     ,  _lowercase				: List[Any]     =   key

		for block in get_blocks_from_text(__UpperCAmelCase   ,				__UpperCAmelCase      ):
				encrypted_blocks.append(pow(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )      )
		return encrypted_blocks


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase = DEFAULT_BLOCK_SIZE   ,				):
		_lowercase				: Tuple     =   []
		_lowercase     ,  _lowercase				: Optional[int]     =   key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )      )
		return get_text_from_blocks(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		with open(__UpperCAmelCase      ) as fo:
				_lowercase				: Optional[Any]     =   fo.read()
		_lowercase     ,  _lowercase     ,  _lowercase				: Optional[Any]     =   content.split(""","""      )
		return (int(__UpperCAmelCase      ), int(__UpperCAmelCase      ), int(__UpperCAmelCase      ))


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase = DEFAULT_BLOCK_SIZE   ,				):
		_lowercase     ,  _lowercase     ,  _lowercase				: Optional[Any]     =   read_key_file(__UpperCAmelCase      )
		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Either decrease the block size or use different keys.""".format(
				        block_size * 8   ,				__UpperCAmelCase      )      )

		_lowercase				: str     =   [str(__UpperCAmelCase      ) for i in encrypt_message(__UpperCAmelCase   ,				(n, e)   ,				__UpperCAmelCase      )]

		_lowercase				: List[str]     =   """,""".join(__UpperCAmelCase      )
		_lowercase				: List[Any]     =   F"""{len(__UpperCAmelCase      )}_{block_size}_{encrypted_content}"""
		with open(__UpperCAmelCase   ,				"""w"""      ) as fo:
				fo.write(__UpperCAmelCase      )
		return encrypted_content


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase     ,  _lowercase     ,  _lowercase				: List[Any]     =   read_key_file(__UpperCAmelCase      )
		with open(__UpperCAmelCase      ) as fo:
				_lowercase				: List[Any]     =   fo.read()
		_lowercase     ,  _lowercase     ,  _lowercase				: int     =   content.split("""_"""      )
		_lowercase				: Union[str, Any]     =   int(__UpperCAmelCase      )
		_lowercase				: Union[str, Any]     =   int(__UpperCAmelCase      )

		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Did you specify the correct key file and encrypted file?""".format(
				        block_size * 8   ,				__UpperCAmelCase      )      )

		_lowercase				: Any     =   []
		for block in encrypted_message.split(""","""      ):
				encrypted_blocks.append(int(__UpperCAmelCase      )      )

		return decrypt_message(__UpperCAmelCase   ,				__UpperCAmelCase   ,				(n, d)   ,				__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Optional[int]     =   """encrypted_file.txt"""
		_lowercase				: Tuple     =   input(R"""Encrypt\Decrypt [e\d]: """      )

		if response.lower().startswith("""e"""      ):
				_lowercase				: Union[str, Any]     =   """encrypt"""
		elif response.lower().startswith("""d"""      ):
				_lowercase				: Any     =   """decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt"""      ):
						rkg.make_key_files("""rsa"""   ,				1024      )

				_lowercase				: Optional[int]     =   input("""\nEnter message: """      )
				_lowercase				: Any     =   """rsa_pubkey.txt"""
				print(F"""Encrypting and writing to {filename}..."""      )
				_lowercase				: Optional[int]     =   encrypt_and_write_to_file(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )

				print("""\nEncrypted text:"""      )
				print(__UpperCAmelCase      )

		elif mode == "decrypt":
				_lowercase				: Any     =   """rsa_privkey.txt"""
				print(F"""Reading from {filename} and decrypting..."""      )
				_lowercase				: List[str]     =   read_from_file_and_decrypt(__UpperCAmelCase   ,				__UpperCAmelCase      )
				print("""writing decryption to rsa_decryption.txt..."""      )
				with open("""rsa_decryption.txt"""   ,				"""w"""      ) as dec:
						dec.write(__UpperCAmelCase      )

				print("""\nDecryption:"""      )
				print(__UpperCAmelCase      )


if __name__ == "__main__":
		main()




import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=states.reshape(1		,       -1		,       model.config.state_dim    )
      UpperCAmelCase     :			Optional[int]     	=actions.reshape(1		,       -1		,       model.config.act_dim    )
      UpperCAmelCase     :			Optional[Any]     	=returns_to_go.reshape(1		,       -1		,       1    )
      UpperCAmelCase     :			Optional[int]     	=timesteps.reshape(1		,       -1    )

      if model.config.max_length is not None:
            UpperCAmelCase     :			str     	=states[:, -model.config.max_length :]
            UpperCAmelCase     :			Any     	=actions[:, -model.config.max_length :]
            UpperCAmelCase     :			List[Any]     	=returns_to_go[:, -model.config.max_length :]
            UpperCAmelCase     :			List[str]     	=timesteps[:, -model.config.max_length :]

            # pad all tokens to sequence length
            UpperCAmelCase     :			Optional[int]     	=torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]    ), torch.ones(states.shape[1]    )]    )
            UpperCAmelCase     :			int     	=attention_mask.to(dtype=torch.long		,       device=states.device    ).reshape(1		,       -1    )
            UpperCAmelCase     :			Optional[int]     	=torch.cat(
                [
                    torch.zeros(
                        (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)		,       device=states.device		,       ),
                    states,
                ]		,       dim=1		,       ).to(dtype=torch.floataa    )
            UpperCAmelCase     :			Tuple     	=torch.cat(
                [
                    torch.zeros(
                        (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)		,       device=actions.device		,       ),
                    actions,
                ]		,       dim=1		,       ).to(dtype=torch.floataa    )
            UpperCAmelCase     :			Tuple     	=torch.cat(
                [
                    torch.zeros(
                        (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)		,       device=returns_to_go.device		,       ),
                    returns_to_go,
                ]		,       dim=1		,       ).to(dtype=torch.floataa    )
            UpperCAmelCase     :			Union[str, Any]     	=torch.cat(
                [
                    torch.zeros(
                        (timesteps.shape[0], model.config.max_length - timesteps.shape[1])		,       device=timesteps.device    ),
                    timesteps,
                ]		,       dim=1		,       ).to(dtype=torch.long    )
      else:
            UpperCAmelCase     :			Tuple     	=None

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      return action_preds[0, -1]


# build the environment

__snake_case         =     gym.make('''Hopper-v3''')
__snake_case         =     env.observation_space.shape[0]
__snake_case         =     env.action_space.shape[0]
__snake_case         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
__snake_case         =     np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
__snake_case         =     torch.from_numpy(state_mean).to(device=device)
__snake_case         =     torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case         =     DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
__snake_case         =     model.to(device)
model.eval()

for ep in range(10):
     __snake_case   ,	__snake_case         =     0, 0
     __snake_case         =     env.reset()
     __snake_case         =     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     __snake_case         =     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     __snake_case         =     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     __snake_case         =     torch.zeros(0, device=device, dtype=torch.floataa)

     __snake_case         =     torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
     for t in range(max_ep_len):
          env.render()
          # add padding
          __snake_case         =     torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
          __snake_case         =     torch.cat([rewards, torch.zeros(1, device=device)])

          __snake_case         =     get_action(
              model,
              (states.to(dtype=torch.floataa) - state_mean) / state_std,
              actions.to(dtype=torch.floataa),
              rewards.to(dtype=torch.floataa),
              target_return.to(dtype=torch.floataa),
              timesteps.to(dtype=torch.long),
          )
          __snake_case         =     action
          __snake_case         =     action.detach().cpu().numpy()

          __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case         =     env.step(action)

          __snake_case         =     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
          __snake_case         =     torch.cat([states, cur_state], dim=0)
          __snake_case         =     reward

          __snake_case         =     target_return[0, -1] - (reward / scale)
          __snake_case         =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          __snake_case         =     torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

          episode_return += reward
          episode_length += 1

          if done:
               break
