from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class       __snake_case (  lowerCamelCase__ ):
 def __init__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = 0.93		, snake_case__ = True		, )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=mixing_coeff
       UpperCAmelCase     :			str     	=leapfrog_steps

       super().__init__()
       self.register_modules(
           vae=snake_case__		, text_encoder=snake_case__		, tokenizer=snake_case__		, unet=snake_case__		, scheduler=snake_case__		, )

       UpperCAmelCase     :			Any     	=2 ** (len(self.vae.config.block_out_channels    ) - 1)
       UpperCAmelCase     :			List[Any]     	=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None		, snake_case__ = False    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.tokenizer(
           snake_case__		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, truncation=snake_case__		, return_tensors='''pt'''		, )

       UpperCAmelCase     :			Tuple     	=self.text_encoder(text_inputs.input_ids.to(self.device    )    ).last_hidden_state

       UpperCAmelCase     :			List[Any]     	=prompt_embeds.to(dtype=self.text_encoder.dtype		, device=self.device    )

       if do_classifier_free_guidance:
             UpperCAmelCase     :			List[str]     	='''''' if negative_prompt is None else negative_prompt

             UpperCAmelCase     :			Dict     	=self.tokenizer(
                 snake_case__		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, truncation=snake_case__		, return_tensors='''pt'''		, )

             UpperCAmelCase     :			List[str]     	=self.text_encoder(uncond_input.input_ids.to(self.device    )    ).last_hidden_state

             UpperCAmelCase     :			int     	=torch.cat([negative_prompt_embeds, prompt_embeds]    )

       return prompt_embeds
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       UpperCAmelCase     :			int     	=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       UpperCAmelCase     :			Union[str, Any]     	=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=int(snake_case__    )

       UpperCAmelCase     :			Dict     	=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=self._get_alpha_and_beta(snake_case__    )
       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=self._get_alpha_and_beta(snake_case__    )

       UpperCAmelCase     :			int     	=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       UpperCAmelCase     :			Optional[Any]     	=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       UpperCAmelCase     :			Tuple     	=(base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=self._get_alpha_and_beta(snake_case__    )
       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=self._get_alpha_and_beta(snake_case__    )

       UpperCAmelCase     :			str     	=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       UpperCAmelCase     :			Optional[Any]     	=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       UpperCAmelCase     :			List[str]     	=a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype    )
 @torch.no_grad()
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=1 / self.vae.config.scaling_factor * latents
       UpperCAmelCase     :			List[Any]     	=self.vae.decode(snake_case__    ).sample
       UpperCAmelCase     :			str     	=(image / 2 + 0.5).clamp(0		, 1    )
       return image
 @torch.no_grad()
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = None		, )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=guidance_scale > 1.0

       UpperCAmelCase     :			str     	=image.to(device=self.device		, dtype=text_embeds.dtype    )
       UpperCAmelCase     :			Union[str, Any]     	=self.vae.encode(snake_case__    ).latent_dist.sample(snake_case__    )

       UpperCAmelCase     :			Optional[Any]     	=self.vae.config.scaling_factor * latent

       UpperCAmelCase     :			Optional[int]     	=[latent.clone(), latent.clone()]

       for i, t in tqdm(enumerate(snake_case__    )		, total=len(snake_case__    )    ):
             UpperCAmelCase     :			List[Any]     	=self.noise_mixing_layer(x=coupled_latents[0]		, y=coupled_latents[1]    )

             # j - model_input index, k - base index
             for j in range(2    ):
                   UpperCAmelCase     :			Optional[Any]     	=j ^ 1

                   if self.leapfrog_steps:
                         if i % 2 == 0:
                               UpperCAmelCase	,    UpperCAmelCase     :			str     	=j, k

                   UpperCAmelCase     :			Any     	=coupled_latents[j]
                   UpperCAmelCase     :			Tuple     	=coupled_latents[k]

                   UpperCAmelCase     :			List[Any]     	=torch.cat([model_input] * 2    ) if do_classifier_free_guidance else model_input

                   UpperCAmelCase     :			Any     	=self.unet(snake_case__		, snake_case__		, encoder_hidden_states=snake_case__    ).sample

                   if do_classifier_free_guidance:
                         UpperCAmelCase	,    UpperCAmelCase     :			Dict     	=noise_pred.chunk(2    )
                         UpperCAmelCase     :			str     	=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=self.noise_step(
                       base=snake_case__		, model_input=snake_case__		, model_output=snake_case__		, timestep=snake_case__		, )

                   UpperCAmelCase     :			str     	=model_input

       return coupled_latents






 @torch.no_grad()
 def __call__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = 3.0		, snake_case__ = 50		, snake_case__ = 0.8		, snake_case__ = None		, snake_case__ = None		, snake_case__ = "pil"		, )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=guidance_scale > 1.0

       UpperCAmelCase     :			List[str]     	=self.image_processor.preprocess(snake_case__    )

       UpperCAmelCase     :			str     	=self._encode_prompt(snake_case__		, snake_case__		, snake_case__    )
       UpperCAmelCase     :			List[Any]     	=self._encode_prompt(snake_case__		, snake_case__		, snake_case__    )

       self.scheduler.set_timesteps(snake_case__		, self.device    )

       UpperCAmelCase     :			int     	=num_inference_steps - int(num_inference_steps * strength    )
       UpperCAmelCase     :			Union[str, Any]     	=self.scheduler.timesteps[t_limit:]
       UpperCAmelCase     :			Any     	=fwd_timesteps.flip(0    )

       UpperCAmelCase     :			List[Any]     	=self.prepare_latents(snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )

       for i, t in tqdm(enumerate(snake_case__    )		, total=len(snake_case__    )    ):
             # j - model_input index, k - base index
             for k in range(2    ):
                   UpperCAmelCase     :			Optional[int]     	=k ^ 1

                   if self.leapfrog_steps:
                         if i % 2 == 1:
                               UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=j, k

                   UpperCAmelCase     :			Optional[Any]     	=coupled_latents[j]
                   UpperCAmelCase     :			str     	=coupled_latents[k]

                   UpperCAmelCase     :			Any     	=torch.cat([model_input] * 2    ) if do_classifier_free_guidance else model_input

                   UpperCAmelCase     :			List[str]     	=self.unet(snake_case__		, snake_case__		, encoder_hidden_states=snake_case__    ).sample

                   if do_classifier_free_guidance:
                         UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=noise_pred.chunk(2    )
                         UpperCAmelCase     :			List[Any]     	=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   UpperCAmelCase	,    UpperCAmelCase     :			int     	=self.denoise_step(
                       base=snake_case__		, model_input=snake_case__		, model_output=snake_case__		, timestep=snake_case__		, )

                   UpperCAmelCase     :			int     	=model_input

             UpperCAmelCase     :			Union[str, Any]     	=self.denoise_mixing_layer(x=coupled_latents[0]		, y=coupled_latents[1]    )

       # either one is fine
       UpperCAmelCase     :			List[str]     	=coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
             UpperCAmelCase     :			Dict     	=(
                 f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                 '''`pil`, `np`, `pt`, `latent`'''
             )
             deprecate('''Unsupported output_type'''		, '''1.0.0'''		, snake_case__		, standard_warn=snake_case__    )
             UpperCAmelCase     :			Optional[int]     	='''np'''

       if output_type == "latent":
             UpperCAmelCase     :			Dict     	=final_latent
       else:
             UpperCAmelCase     :			Optional[int]     	=self.decode_latents(snake_case__    )
             UpperCAmelCase     :			Tuple     	=self.image_processor.postprocess(snake_case__		, output_type=snake_case__    )

       return image

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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=states.reshape(1		,       -1		,       model.config.state_dim    )
      UpperCAmelCase     :			Optional[int]     	=actions.reshape(1		,       -1		,       model.config.act_dim    )
      UpperCAmelCase     :			Optional[Any]     	=returns_to_go.reshape(1		,       -1		,       1    )
      UpperCAmelCase     :			Optional[int]     	=timesteps.reshape(1		,       -1    )

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

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

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=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

__snake_case         =     gym.make('''Hopper-v3''')
__snake_case         =     env.observation_space.shape[0]
__snake_case         =     env.action_space.shape[0]
__snake_case         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     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,
    ]
)
__snake_case         =     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,
    ]
)
__snake_case         =     torch.from_numpy(state_mean).to(device=device)
__snake_case         =     torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

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