


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

				_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=a__		,actions=a__		,rewards=a__		,returns_to_go=a__		,timesteps=a__		,attention_mask=a__		,return_dict=a__		,)

				return action_preds[0, -1]


# build the environment

snake_case_  :	Tuple							  =							gym.make('''Hopper-v3''')
snake_case_  :	Optional[Any]							  =							env.observation_space.shape[0]
snake_case_  :	Union[str, Any]							  =							env.action_space.shape[0]
snake_case_  :	Union[str, Any]							  =							10_00
snake_case_  :	Optional[int]							  =							'''cuda'''
snake_case_  :	Tuple							  =							1000.0  # normalization for rewards/returns
snake_case_  :	Optional[Any]							  =							36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case_  :	Optional[int]							  =							np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
snake_case_  :	Tuple							  =							np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
snake_case_  :	Dict							  =							torch.from_numpy(state_mean).to(device=device)
snake_case_  :	List[str]							  =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case_  :	Optional[int]							  =							DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
snake_case_  :	List[Any]							  =							model.to(device)
model.eval()

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

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

														snake_case_  :	str							  =							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_  :	Optional[Any]							  =							action
														snake_case_  :	List[Any]							  =							action.detach().cpu().numpy()

														snake_case_  , snake_case_  , snake_case_  , snake_case_  :	Union[str, Any]							  =							env.step(action)

														snake_case_  :	Optional[Any]							  =							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
														snake_case_  :	Union[str, Any]							  =							torch.cat([states, cur_state], dim=0)
														snake_case_  :	Tuple							  =							reward

														snake_case_  :	Dict							  =							target_return[0, -1] - (reward / scale)
														snake_case_  :	str							  =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														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







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
														return resize(
														    lowercase_			,			size=(size['height'], size['width'])			,			resample=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

														return rescale(lowercase_			,			scale=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														return normalize(lowercase_			,			mean=lowercase_			,			std=lowercase_			,			data_format=lowercase_			,			**lowercase_   )






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std

														lowerCAmelCase_										=     make_list_of_images(lowercase_   )

														if not valid_images(lowercase_   ):
																					raise ValueError(
																					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																					    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

														if do_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														if do_resize and size is None:
																					raise ValueError('Size must be specified if do_resize is True.'   )

														if do_rescale and rescale_factor is None:
																					raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

														# All transformations expect numpy arrays.
														lowerCAmelCase_										=     [to_numpy_array(lowercase_   ) for image in images]

														if do_center_crop:
																					lowerCAmelCase_										=     [self.center_crop(lowercase_			,			lowercase_			,			size=lowercase_   ) for image in images]

														if do_resize:
																					lowerCAmelCase_										=     [self.resize(image=lowercase_			,			size=lowercase_			,			resample=lowercase_   ) for image in images]

														if do_rescale:
																					lowerCAmelCase_										=     [self.rescale(image=lowercase_			,			scale=lowercase_   ) for image in images]

														if do_normalize:
																					lowerCAmelCase_										=     [self.normalize(image=lowercase_			,			mean=lowercase_			,			std=lowercase_   ) for image in images]

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'pixel_values': images}
														return BatchFeature(data=lowercase_			,			tensor_type=lowercase_   )



