



"""simple docstring"""


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


snake_case_								=			1000







class  A_     :



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Union[str, Any]=1.2  ,    lowercase_					:List[Any]="g"  ,    lowercase_					:Tuple=0.5  ,    lowercase_					:int="-"  ,    lowercase_					:Optional[int]="test_out.jpg"  ,    lowercase_					:Optional[Any]=True  ,    lowercase_					:Union[str, Any]=False  ,    lowercase_					:Union[str, Any]=None  ,    lowercase_					:Tuple=None  ,    lowercase_					:List[str]=0.7  ,    )							->     Optional[int]:
												if isinstance(lowercase_  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					img.numpy().astype('np.uint8'   )
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					img_tensorize(lowercase_   )
												assert isinstance(lowercase_  ,    np.ndarray   )

												UpperCAmelCase ,       UpperCAmelCase			 =					img.shape[1], img.shape[0]
												UpperCAmelCase			 =					mplfigure.Figure(frameon=lowercase_   )
												UpperCAmelCase			 =					fig.get_dpi()
												UpperCAmelCase			 =					(width * scale + 1E-2) / dpi
												UpperCAmelCase			 =					(height * scale + 1E-2) / dpi
												fig.set_size_inches(lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
												ax.axis('off'   )
												ax.set_xlim(0.0  ,    lowercase_   )
												ax.set_ylim(lowercase_   )

												UpperCAmelCase			 =					saveas
												UpperCAmelCase			 =					rgb
												UpperCAmelCase			 =					pynb
												UpperCAmelCase			 =					img
												UpperCAmelCase			 =					edgecolor
												UpperCAmelCase			 =					0.5
												UpperCAmelCase			 =					linestyle
												UpperCAmelCase			 =					int(np.sqrt(min(lowercase_  ,    lowercase_   )   ) * scale // 3   )
												UpperCAmelCase			 =					width
												UpperCAmelCase			 =					height
												UpperCAmelCase			 =					scale
												UpperCAmelCase			 =					fig
												UpperCAmelCase			 =					ax
												UpperCAmelCase			 =					pad
												UpperCAmelCase			 =					idaobj
												UpperCAmelCase			 =					idaattr
												UpperCAmelCase			 =					FigureCanvasAgg(lowercase_   )






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]=None   )							->     Dict:
												if color is None:
																		UpperCAmelCase			 =					self.edgecolor
												((UpperCAmelCase) ,       (UpperCAmelCase) ,       (UpperCAmelCase) ,       (UpperCAmelCase))			 =					box
												UpperCAmelCase			 =					xa - xa
												UpperCAmelCase			 =					ya - ya
												self.ax.add_patch(
												    mpl.patches.Rectangle(
												        (xa, ya)  ,    lowercase_  ,    lowercase_  ,    fill=lowercase_  ,    edgecolor=lowercase_  ,    linewidth=self.font_size // 3  ,    alpha=self.alpha  ,    linestyle=self.linestyle  ,    )   )






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:Any=None  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:Optional[int]=None   )							->     int:
												if len(boxes.shape   ) > 2:
																		UpperCAmelCase			 =					boxes[0]
												if len(obj_ids.shape   ) > 1:
																		UpperCAmelCase			 =					obj_ids[0]
												if len(obj_scores.shape   ) > 1:
																		UpperCAmelCase			 =					obj_scores[0]
												if len(attr_ids.shape   ) > 1:
																		UpperCAmelCase			 =					attr_ids[0]
												if len(attr_scores.shape   ) > 1:
																		UpperCAmelCase			 =					attr_scores[0]
												if isinstance(lowercase_  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					boxes.numpy()
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					np.array(lowercase_   )
												assert isinstance(lowercase_  ,    np.ndarray   )
												UpperCAmelCase			 =					np.prod(boxes[:, 2:] - boxes[:, :2]  ,    axis=1   )
												UpperCAmelCase			 =					np.argsort(-areas   ).tolist()
												UpperCAmelCase			 =					boxes[sorted_idxs] if boxes is not None else None
												UpperCAmelCase			 =					obj_ids[sorted_idxs] if obj_ids is not None else None
												UpperCAmelCase			 =					obj_scores[sorted_idxs] if obj_scores is not None else None
												UpperCAmelCase			 =					attr_ids[sorted_idxs] if attr_ids is not None else None
												UpperCAmelCase			 =					attr_scores[sorted_idxs] if attr_scores is not None else None

												UpperCAmelCase			 =					[self._random_color(maximum=1   ) for _ in range(len(lowercase_   )   )]
												UpperCAmelCase			 =					[assigned_colors[idx] for idx in sorted_idxs]
												if obj_ids is not None:
																		UpperCAmelCase			 =					self._create_text_labels_attr(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
																		for i in range(len(lowercase_   )   ):
																								UpperCAmelCase			 =					assigned_colors[i]
																								self.add_box(boxes[i]  ,    lowercase_   )
																								self.draw_labels(labels[i]  ,    boxes[i]  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:List[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Tuple   )							->     List[str]:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					box
												UpperCAmelCase			 =					(xa, ya)
												UpperCAmelCase			 =					(ya - ya) * (xa - xa)
												UpperCAmelCase			 =					_SMALL_OBJ * self.scale
												if instance_area < small or ya - ya < 40 * self.scale:
																		if ya >= self.height - 5:
																								UpperCAmelCase			 =					(xa, ya)
																		else:
																								UpperCAmelCase			 =					(xa, ya)

												UpperCAmelCase			 =					(ya - ya) / np.sqrt(self.height * self.width   )
												UpperCAmelCase			 =					self._change_color_brightness(lowercase_  ,    brightness_factor=0.7   )
												UpperCAmelCase			 =					np.clip((height_ratio - 0.02) / 0.08 + 1  ,    1.2  ,    2   )
												font_size *= 0.75 * self.font_size

												self.draw_text(
												    text=lowercase_  ,    position=lowercase_  ,    color=lowercase_  ,    )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]="g"  ,    lowercase_					:List[Any]="left"  ,    )							->     Any:
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					self.font_size
												UpperCAmelCase			 =					np.maximum(list(mplc.to_rgb(lowercase_   )   )  ,    0.2   )
												UpperCAmelCase			 =					max(0.8  ,    np.max(lowercase_   )   )
												UpperCAmelCase			 =					{
												    'facecolor': 'black',
												    'alpha': self.alpha,
												    'pad': self.pad,
												    'edgecolor': 'none',
												}
												UpperCAmelCase ,       UpperCAmelCase			 =					position
												self.ax.text(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    size=font_size * self.scale  ,    family='sans-serif'  ,    bbox=lowercase_  ,    verticalalignment='top'  ,    horizontalalignment=lowercase_  ,    color=lowercase_  ,    zorder=10  ,    rotation=lowercase_  ,    )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Tuple=None   )							->     Tuple:
												if saveas is None:
																		UpperCAmelCase			 =					self.saveas
												if saveas.lower().endswith('.jpg'   ) or saveas.lower().endswith('.png'   ):
																		cva.imwrite(
																		    lowercase_  ,    self._get_buffer()[:, :, ::-1]  ,    )
												else:
																		self.fig.savefig(lowercase_   )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:str  ,    lowercase_					:str  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[Any]   )							->     Tuple:
												UpperCAmelCase			 =					[self.idaobj[i] for i in classes]
												UpperCAmelCase			 =					[self.idaattr[i] for i in attr_classes]
												UpperCAmelCase			 =					[
												    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
												    for label, score, attr, attr_score in zip(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												]
												return labels






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:str  ,    lowercase_					:List[str]   )							->     int:
												UpperCAmelCase			 =					[self.idaobj[i] for i in classes]
												if scores is not None:
																		if labels is None:
																								UpperCAmelCase			 =					['{:.0f}%'.format(s * 1_00   ) for s in scores]
																		else:
																								UpperCAmelCase			 =					['{} {:.0f}%'.format(lowercase_  ,    s * 1_00   ) for li, s in zip(lowercase_  ,    lowercase_   )]
												return labels






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:int=2_55   )							->     Any:
												UpperCAmelCase			 =					np.random.randint(0  ,    len(_COLORS   )   )
												UpperCAmelCase			 =					_COLORS[idx] * maximum
												if not self.rgb:
																		UpperCAmelCase			 =					ret[::-1]
												return ret






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												if not self.pynb:
																		UpperCAmelCase ,       UpperCAmelCase			 =					self.canvas.print_to_buffer()
																		if (width, height) != (self.width, self.height):
																								UpperCAmelCase			 =					cva.resize(self.img  ,    (width, height)   )
																		else:
																								UpperCAmelCase			 =					self.img
												else:
																		UpperCAmelCase			 =					io.BytesIO()  # works for cairo backend
																		self.canvas.print_rgba(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase			 =					self.width, self.height
																		UpperCAmelCase			 =					buf.getvalue()
																		UpperCAmelCase			 =					self.img

												UpperCAmelCase			 =					np.frombuffer(lowercase_  ,    dtype='uint8'   )
												UpperCAmelCase			 =					buffer.reshape(lowercase_  ,    lowercase_  ,    4   )
												UpperCAmelCase ,       UpperCAmelCase			 =					np.split(lowercase_  ,    [3]  ,    axis=2   )

												try:
																		import numexpr as ne  # fuse them with numexpr

																		UpperCAmelCase			 =					ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'   )
												except ImportError:
																		UpperCAmelCase			 =					alpha.astype('float32'   ) / 255.0
																		UpperCAmelCase			 =					img * (1 - alpha) + rgb * alpha

												return visualized_image.astype('uint8'   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     Optional[Any]:
												assert brightness_factor >= -1.0 and brightness_factor <= 1.0
												UpperCAmelCase			 =					mplc.to_rgb(lowercase_   )
												UpperCAmelCase			 =					colorsys.rgb_to_hls(*mplc.to_rgb(lowercase_   )   )
												UpperCAmelCase			 =					polygon_color[1] + (brightness_factor * polygon_color[1])
												UpperCAmelCase			 =					0.0 if modified_lightness < 0.0 else modified_lightness
												UpperCAmelCase			 =					1.0 if modified_lightness > 1.0 else modified_lightness
												UpperCAmelCase			 =					colorsys.hls_to_rgb(polygon_color[0]  ,    lowercase_  ,    polygon_color[2]   )
												return modified_color


snake_case_								=			(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






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__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

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

       A_      ,							A_      ,							A_							:       List[str]	      =				model(
           states=a  ,   actions=a  ,   rewards=a  ,   returns_to_go=a  ,   timesteps=a  ,   attention_mask=a  ,   return_dict=a  ,   )

       return action_preds[0, -1]


# build the environment

_lowerCAmelCase        =       gym.make('Hopper-v3')
_lowerCAmelCase        =       env.observation_space.shape[0]
_lowerCAmelCase        =       env.action_space.shape[0]
_lowerCAmelCase        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase        =       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,
    ]
)
_lowerCAmelCase        =       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,
    ]
)
_lowerCAmelCase        =       torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase        =       torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
    _lowerCAmelCase  ,			_lowerCAmelCase        =       0, 0
    _lowerCAmelCase        =       env.reset()
    _lowerCAmelCase        =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    _lowerCAmelCase        =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros(0, device=device, dtype=torch.floataa)

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

        _lowerCAmelCase        =       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        =       action
        _lowerCAmelCase        =       action.detach().cpu().numpy()

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

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

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


