



"""simple docstring"""
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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   # we don't care about the past rewards in this model

   SCREAMING_SNAKE_CASE_:	Dict            =states.reshape(1							,     -1							,     model.config.state_dim	)
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =actions.reshape(1							,     -1							,     model.config.act_dim	)
   SCREAMING_SNAKE_CASE_:	Tuple            =returns_to_go.reshape(1							,     -1							,     1	)
   SCREAMING_SNAKE_CASE_:	str            =timesteps.reshape(1							,     -1	)

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

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

   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =model(
       states=lowercase							,     actions=lowercase							,     rewards=lowercase							,     returns_to_go=lowercase							,     timesteps=lowercase							,     attention_mask=lowercase							,     return_dict=lowercase							,     )

   return action_preds[0, -1]


# build the environment

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

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

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

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

      _UpperCAmelCase       =     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),
      )
      _UpperCAmelCase       =     action
      _UpperCAmelCase       =     action.detach().cpu().numpy()

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

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

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








"""simple docstring"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class    a		:



  def __init__(  self	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]
     for img in imgs:
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =img.shape[:2]
        # later: provide list and randomly choose index for resize
        SCREAMING_SNAKE_CASE_:	Dict            =np.random.randint(self.short_edge_length[0]    ,						self.short_edge_length[1] + 1						)
        if size == 0:
           return img
        SCREAMING_SNAKE_CASE_:	Tuple            =size * 1.0 / min(lowerCAmelCase    ,						lowerCAmelCase						)
        if h < w:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =size, scale * w
        else:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[Any]            =scale * h, size
        if max(lowerCAmelCase    ,						lowerCAmelCase						) > self.max_size:
           SCREAMING_SNAKE_CASE_:	Tuple            =self.max_size * 1.0 / max(lowerCAmelCase    ,						lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =newh * scale
           SCREAMING_SNAKE_CASE_:	str            =neww * scale
        SCREAMING_SNAKE_CASE_:	Dict            =int(neww + 0.5						)
        SCREAMING_SNAKE_CASE_:	List[Any]            =int(newh + 0.5						)

        if img.dtype == np.uinta:
           SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	List[str]            =pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR						)
           SCREAMING_SNAKE_CASE_:	Dict            =np.asarray(lowerCAmelCase						)
        else:
           SCREAMING_SNAKE_CASE_:	str            =img.permute(2    ,						0    ,						1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =nn.functional.interpolate(
               lowerCAmelCase    ,						(newh, neww)    ,						mode=self.interp_method    ,						align_corners=lowerCAmelCase						).squeeze(0						)
        img_augs.append(lowerCAmelCase						)

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,						cfg.INPUT.MAX_SIZE_TEST						)
     SCREAMING_SNAKE_CASE_:	int            =cfg.INPUT.FORMAT
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =cfg.SIZE_DIVISIBILITY
     SCREAMING_SNAKE_CASE_:	Dict            =cfg.PAD_VALUE
     SCREAMING_SNAKE_CASE_:	List[str]            =cfg.INPUT.MAX_SIZE_TEST
     SCREAMING_SNAKE_CASE_:	Optional[int]            =cfg.MODEL.DEVICE
     SCREAMING_SNAKE_CASE_:	Any            =torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =lambda lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =tuple(max(lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[
         nn.functional.pad(
             lowerCAmelCase    ,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,						value=self.pad_value    ,						)
         for size, im in zip(lowerCAmelCase    ,						lowerCAmelCase						)
     ]

     return torch.stack(lowerCAmelCase						), torch.tensor(lowerCAmelCase						)





  def __call__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]=False						)       ->   Optional[Any]:


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[images]
        if single_image:
           assert len(lowerCAmelCase						) == 1
        for i in range(len(lowerCAmelCase						)						):
           if isinstance(images[i]    ,						torch.Tensor						):
              images.insert(lowerCAmelCase    ,						images.pop(lowerCAmelCase						).to(self.device						).float()						)
           elif not isinstance(images[i]    ,						torch.Tensor						):
              images.insert(
                  lowerCAmelCase    ,						torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase						)    ,						input_format=self.input_format						)						)
                  .to(self.device						)
                  .float()    ,						)
            # resize smallest edge
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =self.aug(lowerCAmelCase						)
        # transpose images and convert to torch tensors
        # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
        # now normalize before pad to avoid useless arithmetic
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

        if self.size_divisibility > 0:
           raise NotImplementedError()
        # pad
        SCREAMING_SNAKE_CASE_:	List[Any]            =torch.true_divide(lowerCAmelCase    ,						lowerCAmelCase						)
        if single_image:
           return images[0], sizes[0], scales_yx[0]
        else:
           return images, sizes, scales_yx







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)



