





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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # we don't care about the past rewards in this model

    SCREAMING_SNAKE_CASE__       							= states.reshape(1   ,       -1   ,       model.config.state_dim  )
    SCREAMING_SNAKE_CASE__       							= actions.reshape(1   ,       -1   ,       model.config.act_dim  )
    SCREAMING_SNAKE_CASE__       							= returns_to_go.reshape(1   ,       -1   ,       1  )
    SCREAMING_SNAKE_CASE__       							= timesteps.reshape(1   ,       -1  )

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

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

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= 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

_A				:							List[str]							= gym.make("""Hopper-v3""")
_A				:							Dict							= env.observation_space.shape[0]
_A				:							str							= env.action_space.shape[0]
_A				:							Dict							= 10_00
_A				:							Dict							= """cuda"""
_A				:							Tuple							= 10_00.0  # normalization for rewards/returns
_A				:							str							= 36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_A				:							List[Any]							= np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_A				:							int							= np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_A				:							Union[str, Any]							= torch.from_numpy(state_mean).to(device=device)
_A				:							List[str]							= torch.from_numpy(state_std).to(device=device)

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

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

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

    _A				:							Any							= 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),
    )
    _A				:							Dict							= action
    _A				:							Dict							= action.detach().cpu().numpy()

    _A				:							Tuple							= env.step(action)

    _A				:							Dict							= torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    _A				:							Optional[int]							= torch.cat([states, cur_state], dim=0)
    _A				:							Dict							= reward

    _A				:							List[Any]							= target_return[0, -1] - (reward / scale)
    _A				:							List[str]							= torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    _A				:							Union[str, 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 io
import json
import subprocess


_A				:							str							= [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_A				:							int							= 8


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= {}
    for src, tgt in pairs:
        SCREAMING_SNAKE_CASE__       							= f'''{src}-{tgt}'''
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
    return text


_A				:							Optional[Any]							= get_all_data(pairs, n_objs)
_A				:							Optional[Any]							= """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
  _A				:							int							= json.dump(text, f, indent=2, ensure_ascii=False)
