

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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Any:






    '''simple docstring'''
    __UpperCAmelCase			   =     states.reshape(1    ,				-1    ,				model.config.state_dim )
    __UpperCAmelCase			   =     actions.reshape(1    ,				-1    ,				model.config.act_dim )
    __UpperCAmelCase			   =     returns_to_go.reshape(1    ,				-1    ,				1 )
    __UpperCAmelCase			   =     timesteps.reshape(1    ,				-1 )

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

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

    __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     model(
        states=SCREAMING_SNAKE_CASE    ,				actions=SCREAMING_SNAKE_CASE    ,				rewards=SCREAMING_SNAKE_CASE    ,				returns_to_go=SCREAMING_SNAKE_CASE    ,				timesteps=SCREAMING_SNAKE_CASE    ,				attention_mask=SCREAMING_SNAKE_CASE    ,				return_dict=SCREAMING_SNAKE_CASE    ,				)

    return action_preds[0, -1]


# build the environment

A_			  =				gym.make('Hopper-v3')
A_			  =				env.observation_space.shape[0]
A_			  =				env.action_space.shape[0]
A_			  =				1000
A_			  =				'cuda'
A_			  =				1000.0  # normalization for rewards/returns
A_			  =				3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A_			  =				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,
    ]
)
A_			  =				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,
    ]
)
A_			  =				torch.from_numpy(state_mean).to(device=device)
A_			  =				torch.from_numpy(state_std).to(device=device)

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

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

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

              A_			  =				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_			  =				action
              A_			  =				action.detach().cpu().numpy()

              A_			  =				env.step(action)

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

              A_			  =				target_return[0, -1] - (reward / scale)
              A_			  =				torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              A_			  =				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







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    {
        "sld_warmup_steps": 15,
        "sld_guidance_scale": 20,
        "sld_threshold": 0.0,
        "sld_momentum_scale": 0.0,
        "sld_mom_beta": 0.0,
    }
    a__       							=    {
        "sld_warmup_steps": 10,
        "sld_guidance_scale": 10_00,
        "sld_threshold": 0.01,
        "sld_momentum_scale": 0.3,
        "sld_mom_beta": 0.4,
    }
    a__       							=    {
        "sld_warmup_steps": 7,
        "sld_guidance_scale": 20_00,
        "sld_threshold": 0.0_25,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }
    a__       							=    {
        "sld_warmup_steps": 0,
        "sld_guidance_scale": 50_00,
        "sld_threshold": 1.0,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }






@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    42
    a__       							=    42
    a__       							=    42
    a__       							=    42


if is_transformers_available() and is_torch_available():
       from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
       from .safety_checker import SafeStableDiffusionSafetyChecker




