






"""simple docstring"""



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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case							:   List[str]	  =       logging.get_logger(__name__)


if is_vision_available():
						import PIL




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Tuple=							["""pixel_values"""]






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = False     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = 1 / 2_55     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	None:




											"""simple docstring"""





											super().__init__(**_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=size if size is not None else {'height': 25_60, 'width': 19_20}
											if isinstance(_SCREAMING_SNAKE_CASE     ,    (tuple, list)):
																# The previous feature extractor size parameter was in (width, height) format
																lowerCamelCase__					=size[::-1]
											lowerCamelCase__					=get_size_dict(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=do_resize
											lowerCamelCase__					=size
											lowerCamelCase__					=resample
											lowerCamelCase__					=do_thumbnail
											lowerCamelCase__					=do_align_long_axis
											lowerCamelCase__					=do_pad
											lowerCamelCase__					=do_rescale
											lowerCamelCase__					=rescale_factor
											lowerCamelCase__					=do_normalize
											lowerCamelCase__					=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCamelCase__					=image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None)      ->	np.ndarray:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=get_image_size(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					,  lowerCamelCase__					=size['height'], size['width']

											if (output_width < output_height and input_width > input_height) or (
											    output_width > output_height and input_width < input_height
											):
																lowerCamelCase__					=np.rotaa(_SCREAMING_SNAKE_CASE     ,    3)

											if data_format is not None:
																lowerCamelCase__					=to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

											return image






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = False     ,    _SCREAMING_SNAKE_CASE = None     ,    )      ->	np.ndarray:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=size['height'], size['width']
											lowerCamelCase__					,  lowerCamelCase__					=get_image_size(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=output_width - input_width
											lowerCamelCase__					=output_height - input_height

											if random_padding:
																lowerCamelCase__					=np.random.randint(low=0     ,    high=delta_height + 1)
																lowerCamelCase__					=np.random.randint(low=0     ,    high=delta_width + 1)
											else:
																lowerCamelCase__					=delta_height // 2
																lowerCamelCase__					=delta_width // 2

											lowerCamelCase__					=delta_height - pad_top
											lowerCamelCase__					=delta_width - pad_left

											lowerCamelCase__					=((pad_top, pad_bottom), (pad_left, pad_right))
											return pad(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    data_format=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





											logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.')
											return self.pad_image(*_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	np.ndarray:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=get_image_size(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					,  lowerCamelCase__					=size['height'], size['width']

											# We always resize to the smallest of either the input or output size.
											lowerCamelCase__					=min(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=min(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

											if height == input_height and width == input_width:
																return image

											if input_height > input_width:
																lowerCamelCase__					=int(input_width * height / input_height)
											elif input_width > input_height:
																lowerCamelCase__					=int(input_height * width / input_width)

											return resize(
											    _SCREAMING_SNAKE_CASE     ,    size=(height, width)     ,    resample=_SCREAMING_SNAKE_CASE     ,    reducing_gap=2.0     ,    data_format=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	np.ndarray:




											"""simple docstring"""





											lowerCamelCase__					=get_size_dict(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=min(size['height']     ,    size['width'])
											lowerCamelCase__					=get_resize_output_image_size(_SCREAMING_SNAKE_CASE     ,    size=_SCREAMING_SNAKE_CASE     ,    default_to_square=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=resize(_SCREAMING_SNAKE_CASE     ,    size=_SCREAMING_SNAKE_CASE     ,    resample=_SCREAMING_SNAKE_CASE     ,    data_format=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)
											return resized_image






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	int:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	np.ndarray:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = False     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	PIL.Image.Image:




											"""simple docstring"""





											lowerCamelCase__					=do_resize if do_resize is not None else self.do_resize
											lowerCamelCase__					=size if size is not None else self.size
											if isinstance(_SCREAMING_SNAKE_CASE     ,    (tuple, list)):
																# Previous feature extractor had size in (width, height) format
																lowerCamelCase__					=size[::-1]
											lowerCamelCase__					=get_size_dict(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=resample if resample is not None else self.resample
											lowerCamelCase__					=do_thumbnail if do_thumbnail is not None else self.do_thumbnail
											lowerCamelCase__					=do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
											lowerCamelCase__					=do_pad if do_pad is not None else self.do_pad
											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(_SCREAMING_SNAKE_CASE)

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

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

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

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

											if do_align_long_axis:
																lowerCamelCase__					=[self.align_long_axis(_SCREAMING_SNAKE_CASE     ,    size=_SCREAMING_SNAKE_CASE) for image in images]

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

											if do_thumbnail:
																lowerCamelCase__					=[self.thumbnail(image=_SCREAMING_SNAKE_CASE     ,    size=_SCREAMING_SNAKE_CASE) for image in images]

											if do_pad:
																lowerCamelCase__					=[self.pad_image(image=_SCREAMING_SNAKE_CASE     ,    size=_SCREAMING_SNAKE_CASE     ,    random_padding=_SCREAMING_SNAKE_CASE) for image in images]

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

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

											lowerCamelCase__					=[to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE) for image in images]

											lowerCamelCase__					={'pixel_values': images}
											return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,    tensor_type=_SCREAMING_SNAKE_CASE)







"""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_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

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

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

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

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=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							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       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							:   Tuple	  =       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							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_snake_case							:   List[Any]	  =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
_snake_case							:   List[str]	  =       model.to(device)
model.eval()

for ep in range(10):
						_snake_case  ,    _snake_case							:   List[str]	  =       0, 0
						_snake_case							:   Any	  =       env.reset()
						_snake_case							:   int	  =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_snake_case							:   Optional[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							:   Tuple	  =       torch.zeros(0, device=device, dtype=torch.floataa)

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

												_snake_case							:   List[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							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

												_snake_case  ,    _snake_case  ,    _snake_case  ,    _snake_case							:   List[str]	  =       env.step(action)

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

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