





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

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

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

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

     _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							model(
         states=__lowerCamelCase,				actions=__lowerCamelCase,				rewards=__lowerCamelCase,				returns_to_go=__lowerCamelCase,				timesteps=__lowerCamelCase,				attention_mask=__lowerCamelCase,				return_dict=__lowerCamelCase,				)

     return action_preds[0, -1]


# build the environment

UpperCamelCase__          =gym.make('Hopper-v3')
UpperCamelCase__          =env.observation_space.shape[0]
UpperCamelCase__          =env.action_space.shape[0]
UpperCamelCase__          =1000
UpperCamelCase__          ='cuda'
UpperCamelCase__          =1000.0  # normalization for rewards/returns
UpperCamelCase__          =3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__          =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,
    ]
)
UpperCamelCase__          =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,
    ]
)
UpperCamelCase__          =torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__          =torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase__          =DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
UpperCamelCase__          =model.to(device)
model.eval()

for ep in range(10):
       UpperCamelCase__       ,   UpperCamelCase__          =0, 0
       UpperCamelCase__          =env.reset()
       UpperCamelCase__          =torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       UpperCamelCase__          =torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       UpperCamelCase__          =torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       UpperCamelCase__          =torch.zeros(0, device=device, dtype=torch.floataa)

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

              UpperCamelCase__          =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),
              )
              UpperCamelCase__          =action
              UpperCamelCase__          =action.detach().cpu().numpy()

              UpperCamelCase__       ,   UpperCamelCase__       ,   UpperCamelCase__       ,   UpperCamelCase__          =env.step(action)

              UpperCamelCase__          =torch.from_numpy(state).to(device=device).reshape(1, state_dim)
              UpperCamelCase__          =torch.cat([states, cur_state], dim=0)
              UpperCamelCase__          =reward

              UpperCamelCase__          =target_return[0, -1] - (reward / scale)
              UpperCamelCase__          =torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              UpperCamelCase__          =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

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Dict														=		None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'					, UpperCamelCase					, )
      _snake_case      : Dict														=		kwargs.pop('feature_extractor'							)

    _snake_case      : Optional[Any]														=		image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'							)
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'							)

    super().__init__(UpperCamelCase					, UpperCamelCase							)







  def __call__(					self					:    int					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					, UpperCamelCase					:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					, UpperCamelCase					:    Union[List[List[int]], List[List[List[int]]]] = None					, UpperCamelCase					:    Optional[Union[List[int], List[List[int]]]] = None					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Union[bool, str, PaddingStrategy] = False					, UpperCamelCase					:    Union[bool, str, TruncationStrategy] = False					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    int = 0					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Optional[Union[str, TensorType]] = None					, **UpperCamelCase					:    Optional[Any]					, ):


    '''simple docstring'''





    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'							)

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'							)

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'							)

    # first, apply the image processor
    _snake_case      : Optional[Any]														=		self.image_processor(images=UpperCamelCase					, return_tensors=UpperCamelCase							)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : List[Any]														=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
      _snake_case      : Dict														=		features['words']

    _snake_case      : Optional[int]														=		self.tokenizer(
        text=text if text is not None else features['words']					, text_pair=text_pair if text_pair is not None else None					, boxes=boxes if boxes is not None else features['boxes']					, word_labels=UpperCamelCase					, add_special_tokens=UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, max_length=UpperCamelCase					, stride=UpperCamelCase					, pad_to_multiple_of=UpperCamelCase					, return_token_type_ids=UpperCamelCase					, return_attention_mask=UpperCamelCase					, return_overflowing_tokens=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, return_offsets_mapping=UpperCamelCase					, return_length=UpperCamelCase					, verbose=UpperCamelCase					, return_tensors=UpperCamelCase					, **UpperCamelCase					, )

    # add pixel values
    _snake_case      : Optional[int]														=		features.pop('pixel_values'							)
    if return_overflowing_tokens is True:
      _snake_case      : Any														=		self.get_overflowing_images(UpperCamelCase					, encoded_inputs['overflow_to_sample_mapping']							)
    _snake_case      : int														=		images

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]							)

    if len(UpperCamelCase							) != len(UpperCamelCase							):
      raise ValueError(
          'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
          f""" {len(UpperCamelCase							)} and {len(UpperCamelCase							)}"""							)

    return images_with_overflow







  def UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase					, **UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase					, **UpperCamelCase							)







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					, UpperCamelCase					, )
    return self.image_processor_class







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					, UpperCamelCase					, )
    return self.image_processor




