
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  a_	( __lowercase     :			Any ,      __lowercase     :			Tuple ,      __lowercase     :			Optional[int] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			List[str] ,      __lowercase     :			Tuple				)				->						str:
			_snake_case						=    states.reshape(1 ,      -1 ,      model.config.state_dim				)
			_snake_case						=    actions.reshape(1 ,      -1 ,      model.config.act_dim				)
			_snake_case						=    returns_to_go.reshape(1 ,      -1 ,      1				)
			_snake_case						=    timesteps.reshape(1 ,      -1				)

			if model.config.max_length is not None:
						_snake_case						=    states[:, -model.config.max_length :]
						_snake_case						=    actions[:, -model.config.max_length :]
						_snake_case						=    returns_to_go[:, -model.config.max_length :]
						_snake_case						=    timesteps[:, -model.config.max_length :]

						# pad all tokens to sequence length
						_snake_case						=    torch.cat(
						    [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
						_snake_case						=    attention_mask.to(dtype=torch.long ,      device=states.device				).reshape(1 ,      -1				)
						_snake_case						=    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				)
						_snake_case						=    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				)
						_snake_case						=    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				)
						_snake_case						=    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:
						_snake_case						=    None

			_snake_case						=    model(
			    states=_UpperCAmelCase ,      actions=_UpperCAmelCase ,      rewards=_UpperCAmelCase ,      returns_to_go=_UpperCAmelCase ,      timesteps=_UpperCAmelCase ,      attention_mask=_UpperCAmelCase ,      return_dict=_UpperCAmelCase ,      )

			return action_preds[0, -1]


# build the environment

_lowerCamelCase :       Dict     					=    gym.make('''Hopper-v3''')
_lowerCamelCase :       Any     					=    env.observation_space.shape[0]
_lowerCamelCase :       Optional[Any]     					=    env.action_space.shape[0]
_lowerCamelCase :       Any     					=    1_000
_lowerCamelCase :       Tuple     					=    '''cuda'''
_lowerCamelCase :       Optional[int]     					=    1_000.0  # normalization for rewards/returns
_lowerCamelCase :       Optional[Any]     					=    3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase :       str     					=    np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_lowerCamelCase :       Dict     					=    np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_lowerCamelCase :       int     					=    torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase :       Tuple     					=    torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCamelCase :       List[Any]     					=    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_lowerCamelCase :       Any     					=    model.to(device)
model.eval()

for ep in range(10):
			_lowerCamelCase :       Optional[int]     					=    0, 0
			_lowerCamelCase :       List[str]     					=    env.reset()
			_lowerCamelCase :       Optional[int]     					=    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			_lowerCamelCase :       str     					=    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			_lowerCamelCase :       Optional[int]     					=    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			_lowerCamelCase :       Any     					=    torch.zeros(0, device=device, dtype=torch.floataa)

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

						_lowerCamelCase :       int     					=    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),
						)
						_lowerCamelCase :       List[str]     					=    action
						_lowerCamelCase :       List[str]     					=    action.detach().cpu().numpy()

						_lowerCamelCase :       List[str]     					=    env.step(action)

						_lowerCamelCase :       Union[str, Any]     					=    torch.from_numpy(state).to(device=device).reshape(1, state_dim)
						_lowerCamelCase :       Any     					=    torch.cat([states, cur_state], dim=0)
						_lowerCamelCase :       Dict     					=    reward

						_lowerCamelCase :       str     					=    target_return[0, -1] - (reward / scale)
						_lowerCamelCase :       Dict     					=    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						_lowerCamelCase :       List[Any]     					=    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

import random
import sys

_lowerCamelCase :       Tuple     					=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def  a_	( )				->						None:
			_snake_case						=    input('Enter message: '				)
			_snake_case						=    'LFWOAYUISVKMNXPBDCRJTQEGHZ'
			_snake_case						=    input('Encrypt/Decrypt [e/d]: '				)

			check_valid_key(__lowercase				)

			if resp.lower().startswith('e'				):
						_snake_case						=    'encrypt'
						_snake_case						=    encrypt_message(__lowercase ,      __lowercase				)
			elif resp.lower().startswith('d'				):
						_snake_case						=    'decrypt'
						_snake_case						=    decrypt_message(__lowercase ,      __lowercase				)

			print(f'''\n{mode.title()}ion: \n{translated}'''				)






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    list(__lowercase				)
			_snake_case						=    list(__lowercase				)
			key_list.sort()
			letters_list.sort()

			if key_list != letters_list:
						sys.exit('Error in the key or symbol set.'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'encrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'decrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			_snake_case						=    ''
			_snake_case						=    LETTERS
			_snake_case						=    key

			if mode == "decrypt":
						_snake_case			,      _snake_case						=    chars_b, chars_a

			for symbol in message:
						if symbol.upper() in chars_a:
									_snake_case						=    chars_a.find(symbol.upper()				)
									if symbol.isupper():
												translated += chars_b[sym_index].upper()
									else:
												translated += chars_b[sym_index].lower()
						else:
									translated += symbol

			return translated






def  a_	( )				->						str:
			_snake_case						=    list(__lowercase				)
			random.shuffle(__lowercase				)
			return "".join(__lowercase				)


if __name__ == "__main__":
			main()