




'''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       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			# we don't care about the past rewards in this model

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

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

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

			__UpperCamelCase				:      int									=       model(
			    states=snake_case__ ,				actions=snake_case__ ,				rewards=snake_case__ ,				returns_to_go=snake_case__ ,				timesteps=snake_case__ ,				attention_mask=snake_case__ ,				return_dict=snake_case__ ,				)

			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				     = 1000
_lowerCAmelCase				     = '''cuda'''
_lowerCAmelCase				     = 1000.0  # normalization for rewards/returns
_lowerCAmelCase				     = 3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase				     = 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,
    ]
)
_lowerCAmelCase				     = 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,
    ]
)
_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(10):
					_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









'''simple docstring'''






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_lowerCAmelCase				     = logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["safety_checker", "feature_extractor"]
				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = True			,  )     ->	List[str]:
							super().__init__()
							if hasattr(scheduler.config			,  "steps_offset"      ) and scheduler.config.steps_offset != 1:
										__UpperCamelCase				:      List[str]									=       (
										    f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
										    f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
										    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
										    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
										    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
										    " file"
										)
										deprecate("steps_offset!=1"			,  "1.0.0"			,  _UpperCAmelCase			,  standard_warn=_UpperCAmelCase      )
										__UpperCamelCase				:      str									=       dict(scheduler.config      )
										__UpperCamelCase				:      Optional[Any]									=       1
										__UpperCamelCase				:      Dict									=       FrozenDict(_UpperCAmelCase      )

							self.register_modules(
							    vae=_UpperCAmelCase			,  text_encoder=_UpperCAmelCase			,  tokenizer=_UpperCAmelCase			,  unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			,  feature_extractor=_UpperCAmelCase			,  )
							self.register_to_config(requires_safety_checker=_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict[str, Any]:
							return {k: getattr(self			,  _UpperCAmelCase      ) for k in self.config.keys() if not k.startswith("_"      )}
				def 	a_	(self			,  _UpperCAmelCase = "auto"      )     ->	List[str]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										__UpperCamelCase				:      Dict									=       self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase      )
				def 	a_	(self      )     ->	Dict:
							self.enable_attention_slicing(_UpperCAmelCase      )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	List[str]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionInpaintPipelineLegacy(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  mask_image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  **_UpperCAmelCase			,  )     ->	Optional[Any]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionImgaImgPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )





				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	Union[str, Any]:
							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
							return StableDiffusionPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  height=_UpperCAmelCase			,  width=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  latents=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )



