

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	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]					,      SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[Any]:

 """simple docstring"""






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

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

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

 __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     int     			=   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	 =     1_0_0_0
_a	 =     '''cuda'''
_a	 =     1_000.0  # normalization for rewards/returns
_a	 =     3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_a	 =     np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
_a	 =     np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
_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(1_0):
  _a ,			_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 ,			_a ,			_a ,			_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 typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase    ,						text_encoder=UpperCAmelCase    ,						tokenizer=UpperCAmelCase    ,						unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





