




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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	states.reshape(1					,						-1					,						model.config.state_dim   )
			SCREAMING_SNAKE_CASE_			=	actions.reshape(1					,						-1					,						model.config.act_dim   )
			SCREAMING_SNAKE_CASE_			=	returns_to_go.reshape(1					,						-1					,						1   )
			SCREAMING_SNAKE_CASE_			=	timesteps.reshape(1					,						-1   )

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

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

			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	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

SCREAMING_SNAKE_CASE__    :    Tuple								=					gym.make("Hopper-v3")
SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					env.observation_space.shape[0]
SCREAMING_SNAKE_CASE__    :    List[Any]								=					env.action_space.shape[0]
SCREAMING_SNAKE_CASE__    :    int								=					10_00
SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					"cuda"
SCREAMING_SNAKE_CASE__    :    Any								=					1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE__    :    List[Any]								=					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,
    ]
)
SCREAMING_SNAKE_CASE__    :    Dict								=					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,
    ]
)
SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
				SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__    :    List[Any]								=					0, 0
				SCREAMING_SNAKE_CASE__    :    List[Any]								=					env.reset()
				SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
				SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
				SCREAMING_SNAKE_CASE__    :    Tuple								=					torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					torch.zeros(0, device=device, dtype=torch.floataa)

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

								SCREAMING_SNAKE_CASE__    :    List[Any]								=					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),
								)
								SCREAMING_SNAKE_CASE__    :    List[Any]								=					action
								SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					action.detach().cpu().numpy()

								SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__    :    Tuple								=					env.step(action)

								SCREAMING_SNAKE_CASE__    :    Dict								=					torch.from_numpy(state).to(device=device).reshape(1, state_dim)
								SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					torch.cat([states, cur_state], dim=0)
								SCREAMING_SNAKE_CASE__    :    List[Any]								=					reward

								SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					target_return[0, -1] - (reward / scale)
								SCREAMING_SNAKE_CASE__    :    str								=					torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
								SCREAMING_SNAKE_CASE__    :    int								=					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






import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[images]
				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)

				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[..., None]

				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(images.transpose(0			,    3			,    1			,    2			)			)
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				return 2.0 * images - 1.0





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				return (images / 2 + 0.5).clamp(0			,    1			)





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:




				"""simple docstring"""



				if height is None:
							SCREAMING_SNAKE_CASE_			=	image.height
				if width is None:
							SCREAMING_SNAKE_CASE_			=	image.width

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(
				    x - x % self.config.vae_scale_factor for x in (width, height)
				)  # resize to integer multiple of vae_scale_factor
				SCREAMING_SNAKE_CASE_			=	image.resize((width, height)			,    resample=PIL_INTERPOLATION[self.config.resample]			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[image]
				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
							raise ValueError(
							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)

				if isinstance(image[0]			,    PIL.Image.Image			):
							if self.config.do_convert_rgb:
										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
							if self.config.do_resize:
										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt

				elif isinstance(image[0]			,    np.ndarray			):
							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

				elif isinstance(image[0]			,    torch.Tensor			):
							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
										return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

        # expected range [0,1], normalize to [-1,1]
				SCREAMING_SNAKE_CASE_			=	self.config.do_normalize
				if image.min() < 0:
							warnings.warn(
							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
							    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'			,    SCREAMING_SNAKE_CASE			,    )
							SCREAMING_SNAKE_CASE_			=	False

				if do_normalize:
							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)

				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if output_type == "latent":
							return image

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				if output_type == "pt":
							return image

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							return image

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)




class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""simple docstring"""



				return image[:, :, 1] * 2**8 + image[:, :, 2]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
				if images.shape[-1] == 6:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
							SCREAMING_SNAKE_CASE_			=	[
							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
							]
				elif images.shape[-1] == 4:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
				else:
							raise Exception('Not supported'			)

				return pil_images





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							if image.shape[-1] == 6:
										SCREAMING_SNAKE_CASE_			=	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,    axis=0			)
							else:
										SCREAMING_SNAKE_CASE_			=	image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
				else:
							raise Exception(f'This type {output_type} is not supported'			)
