






"""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 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     :     Optional[int]            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     Dict				,       snake_case__	:     bool = True				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     bool = True				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BICUBIC				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     int				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Union[str, Any]				=				crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
        UpperCAmelCase__		:  List[Any]				=				get_size_dict(snake_case__				,       param_name="crop_size"				)
        UpperCAmelCase__		:  Union[str, Any]				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  List[str]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  Dict				=				do_center_crop
        UpperCAmelCase__		:  Optional[Any]				=				crop_size
        UpperCAmelCase__		:  Tuple				=				do_resize
        UpperCAmelCase__		:  Union[str, Any]				=				size
        UpperCAmelCase__		:  int				=				resample
        UpperCAmelCase__		:  Union[str, Any]				=				do_rescale
        UpperCAmelCase__		:  str				=				rescale_factor
        UpperCAmelCase__		:  Tuple				=				do_normalize
        UpperCAmelCase__		:  Tuple				=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        UpperCAmelCase__		:  Any				=				image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 	__a				(						self	:     str				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     Optional[int] = None				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[str]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				self.size if size is None else size
        UpperCAmelCase__		:  Dict				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  int				=				get_size_dict(snake_case__				,       param_name="crop_size"				)

        UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[Any]				=				get_image_size(snake_case__				)
        UpperCAmelCase__		:  List[str]				=				min(snake_case__				,       snake_case__				)
        UpperCAmelCase__		:  List[str]				=				(size["height"] / crop_size["height"]) * min_dim
        UpperCAmelCase__		:  Dict				=				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(snake_case__				,       size=(cropped_height, cropped_width)				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PIL.Image.BICUBIC				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[str]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        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(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[int, float]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     int				,       ):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     List[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				,       ):


        '''simple docstring'''
        return normalize(snake_case__				,       mean=snake_case__				,       std=snake_case__				,       data_format=snake_case__				,       **snake_case__				)




   def 	__a				(						self	:     Tuple				,       snake_case__	:     ImageInput				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[float] = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[str, TensorType]] = None				,       snake_case__	:     ChannelDimension = ChannelDimension.FIRST				,       **snake_case__	:     Optional[Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				do_center_crop if do_center_crop is not None else self.do_center_crop
        UpperCAmelCase__		:  Optional[int]				=				crop_size if crop_size is not None else self.crop_size
        UpperCAmelCase__		:  List[str]				=				get_size_dict(snake_case__				,       param_name="crop_size"				)
        UpperCAmelCase__		:  List[Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  str				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Union[str, Any]				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  Union[str, Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  Any				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Union[str, Any]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Optional[Any]				=				make_list_of_images(snake_case__				)

        if not valid_images(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_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.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

        if do_center_crop:
             UpperCAmelCase__		:  Union[str, Any]				=				[self.center_crop(snake_case__				,       snake_case__				,       size=snake_case__				) for image in images]

        if do_resize:
             UpperCAmelCase__		:  Optional[int]				=				[self.resize(image=snake_case__				,       size=snake_case__				,       resample=snake_case__				) for image in images]

        if do_rescale:
             UpperCAmelCase__		:  int				=				[self.rescale(image=snake_case__				,       scale=snake_case__				) for image in images]

        if do_normalize:
             UpperCAmelCase__		:  Optional[int]				=				[self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__				) for image in images]

        UpperCAmelCase__		:  str				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

        UpperCAmelCase__		:  Optional[Any]				=				{"pixel_values": images}
        return BatchFeature(data=snake_case__				,       tensor_type=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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



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

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

          # pad all tokens to sequence length
          UpperCAmelCase__		:  Optional[Any]				=				torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
          UpperCAmelCase__		:  Optional[Any]				=				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__		:  Optional[Any]				=				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__		:  Any				=				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__		:  str				=				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__		:  Any				=				None

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				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     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

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

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

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

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

  _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase     :     int            =	env.step(action)

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

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


