






"""simple docstring"""





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  UpperCamelCase					=     True
except ImportError:
  UpperCamelCase					=     False


try:
  from torch.hub import _get_torch_home

  UpperCamelCase					=     _get_torch_home()
except ImportError:
  UpperCamelCase					=     os.path.expanduser(
      os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
  )

UpperCamelCase					=     os.path.join(torch_cache_home, """transformers""")

UpperCamelCase					=     """https://cdn.huggingface.co"""
UpperCamelCase					=     """https://s3.amazonaws.com/models.huggingface.co/bert"""
UpperCamelCase					=     """/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
UpperCamelCase					=     os.path.join(PATH, """config.yaml""")
UpperCamelCase					=     os.path.join(PATH, """attributes.txt""")
UpperCamelCase					=     os.path.join(PATH, """objects.txt""")
UpperCamelCase					=     os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
UpperCamelCase					=     os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
UpperCamelCase					=     os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
UpperCamelCase					=     """pytorch_model.bin"""
UpperCamelCase					=     """config.yaml"""






def 							A(    snake_case_=OBJECTS		,  snake_case_=ATTRIBUTES						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     []
       with open(snake_case_						) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(","						)[0].lower().strip()						)

       lowercase__:							Tuple         =     []
       with open(snake_case_						) as f:
              for object in f.readlines():
                     vg_attrs.append(object.split(","						)[0].lower().strip()						)
       return vg_classes, vg_attrs






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Tuple         =     OrderedDict()
       with open(snake_case_		,  "rb"						) as f:
              lowercase__:							Any         =     pkl.load(snake_case_						)["model"]
       for k in copy.deepcopy(list(ckp.keys()						)						):
              lowercase__:							Optional[Any]         =     ckp.pop(snake_case_						)
              if isinstance(snake_case_		,  np.ndarray						):
                     lowercase__:							str         =     torch.tensor(snake_case_						)
              else:
                     assert isinstance(snake_case_		,  torch.tensor						), type(snake_case_						)
              lowercase__:							Tuple         =     v
       return r







class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							{}





   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_ = "root"					, UpperCAmelCase_=0)			->   str:




          '''simple docstring'''




          lowercase__:							List[Any]         =     name
          lowercase__:							Dict         =     level
          lowercase__:							str         =     {}
          for k, v in dictionary.items():
                 if v is None:
                        raise ValueError()
                 lowercase__:							Optional[Any]         =     copy.deepcopy(UpperCAmelCase_)
                 lowercase__:							List[str]         =     copy.deepcopy(UpperCAmelCase_)
                 if isinstance(UpperCAmelCase_					, UpperCAmelCase_):
                        lowercase__:							Optional[Any]         =     Config(UpperCAmelCase_					, name=UpperCAmelCase_					, level=level + 1)
                 lowercase__:							List[Any]         =     v
                 setattr(self					, UpperCAmelCase_					, UpperCAmelCase_)

          lowercase__:							Dict         =     d





   def __repr__(       self)			->   Tuple:




          '''simple docstring'''




          return str(list((self._pointer.keys())))





   def __setattr__(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          lowercase__:							Tuple         =     val
          lowercase__:							List[Any]         =     val
          lowercase__:							int         =     key.split(".")
          lowercase__:							int         =     len(UpperCAmelCase_) - 1
          lowercase__:							Dict         =     self._pointer
          if len(UpperCAmelCase_) > 1:
                 for i, l in enumerate(UpperCAmelCase_):
                        if hasattr(self					, UpperCAmelCase_) and isinstance(getattr(self					, UpperCAmelCase_)					, UpperCAmelCase_):
                               setattr(getattr(self					, UpperCAmelCase_)					, ".".join(levels[i:])					, UpperCAmelCase_)
                        if l == last_level:
                               lowercase__:							List[Any]         =     val
                        else:
                               lowercase__:							int         =     pointer[l]





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          return self._pointer





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




          with open(F"""{file_name}"""					, "w") as stream:
                 dump(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




          with open(F"""{file_name}"""					, "w") as stream:
                 json.dump(UpperCAmelCase_					, UpperCAmelCase_)





   @staticmethod
   def __lowercase							(       UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          with open(UpperCAmelCase_) as stream:
                 lowercase__:							List[str]         =     load(UpperCAmelCase_					, Loader=UpperCAmelCase_)
          return data





   def __str__(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     "    "
          if self._name != "root":
                 lowercase__:							List[str]         =     F"""{t * (self._level-1)}{self._name}:\n"""
          else:
                 lowercase__:							Optional[Any]         =     ""
          lowercase__:							Optional[int]         =     self._level
          for i, (k, v) in enumerate(self._pointer.items()):
                 if isinstance(UpperCAmelCase_					, UpperCAmelCase_):
                        r += F"""{t * (self._level)}{v}\n"""
                        self._level += 1
                 else:
                        r += F"""{t * (self._level)}{k}: {v} ({type(UpperCAmelCase_).__name__})\n"""
                 lowercase__:							Any         =     level
          return r[:-1]





   @classmethod
   def __lowercase							(       cls					, UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Tuple         =     cls.get_config_dict(UpperCAmelCase_					, **UpperCAmelCase_)
          return cls(UpperCAmelCase_)





   @classmethod
   def __lowercase							(       cls					, UpperCAmelCase_					, **UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     kwargs.pop("cache_dir"					, UpperCAmelCase_)
          lowercase__:							Optional[int]         =     kwargs.pop("force_download"					, UpperCAmelCase_)
          lowercase__:							str         =     kwargs.pop("resume_download"					, UpperCAmelCase_)
          lowercase__:							Optional[Any]         =     kwargs.pop("proxies"					, UpperCAmelCase_)
          lowercase__:							List[Any]         =     kwargs.pop("local_files_only"					, UpperCAmelCase_)

          if os.path.isdir(UpperCAmelCase_):
                 lowercase__:							int         =     os.path.join(UpperCAmelCase_					, UpperCAmelCase_)
          elif os.path.isfile(UpperCAmelCase_) or is_remote_url(UpperCAmelCase_):
                 lowercase__:							Tuple         =     pretrained_model_name_or_path
          else:
                 lowercase__:							Union[str, Any]         =     hf_bucket_url(UpperCAmelCase_					, filename=UpperCAmelCase_					, use_cdn=UpperCAmelCase_)

          try:
                 # Load from URL or cache if already cached
                 lowercase__:							int         =     cached_path(
                     UpperCAmelCase_					, cache_dir=UpperCAmelCase_					, force_download=UpperCAmelCase_					, proxies=UpperCAmelCase_					, resume_download=UpperCAmelCase_					, local_files_only=UpperCAmelCase_					, )
                 # Load config dict
                 if resolved_config_file is None:
                        raise EnvironmentError

                 lowercase__:							Optional[Any]         =     Config.load_yaml(UpperCAmelCase_)

          except EnvironmentError:
                 lowercase__:							Dict         =     "Can't load config for"
                 raise EnvironmentError(UpperCAmelCase_)

          if resolved_config_file == config_file:
                 print("loading configuration file from path")
          else:
                 print("loading configuration file cache")

          return Config.load_yaml(UpperCAmelCase_), kwargs







def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     torch.load("dump.pt"		,  map_location=in_tensor.device						)
       lowercase__:							int         =     in_tensor.numpy()
       lowercase__:							List[str]         =     out_tensor.numpy()[0]
       print(na.shape		,  na[0, 0, :5]						)
       print(na.shape		,  na[0, 0, :5]						)
       assert np.allclose(snake_case_		,  snake_case_		,  rtol=0.01		,  atol=0.1						), (
           F"""{sum([1 for x in np.isclose(snake_case_		,  snake_case_		,  rtol=0.01		,  atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*100:.4f} %"""
           " element-wise mismatch"
       )
       raise Exception("tensors are all good"						)

       # Hugging face functions below






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     urlparse(snake_case_						)
       return parsed.scheme in ("http", "https")






def 							A(    snake_case_		,  snake_case_		,  snake_case_=True						):
       """simple docstring"""





       lowercase__:							List[Any]         =     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       lowercase__:							List[Any]         =     "/" not in model_id
       if legacy_format:
              return F"""{endpoint}/{model_id}-{filename}"""
       else:
              return F"""{endpoint}/{model_id}/{filename}"""






def 							A(    snake_case_		,  snake_case_		,  snake_case_=None		,  snake_case_=0		,  snake_case_=None		,  ):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     "python/{}".format(sys.version.split()[0]						)
       if _torch_available:
              ua += "; torch/{}".format(torch.__version__						)
       if isinstance(snake_case_		,  snake_case_						):
              ua += "; " + "; ".join("{}/{}".format(snake_case_		,  snake_case_						) for k, v in user_agent.items()						)
       elif isinstance(snake_case_		,  snake_case_						):
              ua += "; " + user_agent
       lowercase__:							Tuple         =     {"user-agent": ua}
       if resume_size > 0:
              lowercase__:							Any         =     "bytes=%d-" % (resume_size,)
       lowercase__:							List[Any]         =     requests.get(snake_case_		,  stream=snake_case_		,  proxies=snake_case_		,  headers=snake_case_						)
       if response.status_code == 416:  # Range not satisfiable
              return
       lowercase__:							Optional[int]         =     response.headers.get("Content-Length"						)
       lowercase__:							str         =     resume_size + int(snake_case_						) if content_length is not None else None
       lowercase__:							List[str]         =     tqdm(
           unit="B"		,  unit_scale=snake_case_		,  total=snake_case_		,  initial=snake_case_		,  desc="Downloading"		,  )
       for chunk in response.iter_content(chunk_size=1024						):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(snake_case_						)						)
                     temp_file.write(snake_case_						)
       progress.close()






def 							A(    snake_case_		,  snake_case_=None		,  snake_case_=False		,  snake_case_=None		,  snake_case_=10		,  snake_case_=False		,  snake_case_=None		,  snake_case_=False		,  ):
       """simple docstring"""





       if cache_dir is None:
              lowercase__:							Any         =     TRANSFORMERS_CACHE
       if isinstance(snake_case_		,  snake_case_						):
              lowercase__:							Dict         =     str(snake_case_						)

       os.makedirs(snake_case_		,  exist_ok=snake_case_						)

       lowercase__:							int         =     None
       if not local_files_only:
              try:
                     lowercase__:							int         =     requests.head(snake_case_		,  allow_redirects=snake_case_		,  proxies=snake_case_		,  timeout=snake_case_						)
                     if response.status_code == 200:
                            lowercase__:							Optional[int]         =     response.headers.get("ETag"						)
              except (EnvironmentError, requests.exceptions.Timeout):
                     # etag is already None
                     pass

       lowercase__:							List[Any]         =     url_to_filename(snake_case_		,  snake_case_						)

       # get cache path to put the file
       lowercase__:							Optional[int]         =     os.path.join(snake_case_		,  snake_case_						)

       # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
       # try to get the last downloaded one
       if etag is None:
              if os.path.exists(snake_case_						):
                     return cache_path
              else:
                     lowercase__:							str         =     [
                         file
                         for file in fnmatch.filter(os.listdir(snake_case_						)		,  filename + ".*"						)
                         if not file.endswith(".json"						) and not file.endswith(".lock"						)
                     ]
                     if len(snake_case_						) > 0:
                            return os.path.join(snake_case_		,  matching_files[-1]						)
                     else:
                            # If files cannot be found and local_files_only=True,
                            # the models might've been found if local_files_only=False
                            # Notify the user about that
                            if local_files_only:
                                   raise ValueError(
                                       "Cannot find the requested files in the cached path and outgoing traffic has been"
                                       " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
                                       " to False."						)
                            return None

    # From now on, etag is not None.
       if os.path.exists(snake_case_						) and not force_download:
              return cache_path

       # Prevent parallel downloads of the same file with a lock.
       lowercase__:							Optional[Any]         =     cache_path + ".lock"
       with FileLock(snake_case_						):
              # If the download just completed while the lock was activated.
              if os.path.exists(snake_case_						) and not force_download:
                     # Even if returning early like here, the lock will be released.
                     return cache_path

              if resume_download:
                     lowercase__:							Any         =     cache_path + ".incomplete"

                     @contextmanager
                     def _resumable_file_manager():
                            with open(snake_case_		,  "a+b"						) as f:
                                   yield f

                     lowercase__:							Optional[int]         =     _resumable_file_manager
                     if os.path.exists(snake_case_						):
                            lowercase__:							Dict         =     os.stat(snake_case_						).st_size
                     else:
                            lowercase__:							List[str]         =     0
              else:
                     lowercase__:							List[str]         =     partial(tempfile.NamedTemporaryFile		,  dir=snake_case_		,  delete=snake_case_						)
                     lowercase__:							Optional[int]         =     0

              # Download to temporary file, then copy to cache dir once finished.
              # Otherwise you get corrupt cache entries if the download gets interrupted.
              with temp_file_manager() as temp_file:
                     print(
                         "%s not found in cache or force_download set to True, downloading to %s"		,  snake_case_		,  temp_file.name		,  )

                     http_get(
                         snake_case_		,  snake_case_		,  proxies=snake_case_		,  resume_size=snake_case_		,  user_agent=snake_case_		,  )

              os.replace(temp_file.name		,  snake_case_						)

              lowercase__:							Tuple         =     {"url": url, "etag": etag}
              lowercase__:							Any         =     cache_path + ".json"
              with open(snake_case_		,  "w"						) as meta_file:
                     json.dump(snake_case_		,  snake_case_						)

       return cache_path






def 							A(    snake_case_		,  snake_case_=None						):
       """simple docstring"""





       lowercase__:							Any         =     url.encode("utf-8"						)
       lowercase__:							int         =     shaaaa(snake_case_						)
       lowercase__:							List[Any]         =     url_hash.hexdigest()

       if etag:
              lowercase__:							Any         =     etag.encode("utf-8"						)
              lowercase__:							Optional[int]         =     shaaaa(snake_case_						)
              filename += "." + etag_hash.hexdigest()

       if url.endswith(".h5"						):
              filename += ".h5"

       return filename






def 							A(    snake_case_		,  snake_case_=None		,  snake_case_=False		,  snake_case_=None		,  snake_case_=False		,  snake_case_=None		,  snake_case_=False		,  snake_case_=False		,  snake_case_=False		,  ):
       """simple docstring"""





       if cache_dir is None:
              lowercase__:							List[Any]         =     TRANSFORMERS_CACHE
       if isinstance(snake_case_		,  snake_case_						):
              lowercase__:							Dict         =     str(snake_case_						)
       if isinstance(snake_case_		,  snake_case_						):
              lowercase__:							str         =     str(snake_case_						)

       if is_remote_url(snake_case_						):
              # URL, so get it from the cache (downloading if necessary)
              lowercase__:							Tuple         =     get_from_cache(
                  snake_case_		,  cache_dir=snake_case_		,  force_download=snake_case_		,  proxies=snake_case_		,  resume_download=snake_case_		,  user_agent=snake_case_		,  local_files_only=snake_case_		,  )
       elif os.path.exists(snake_case_						):
              # File, and it exists.
              lowercase__:							Optional[Any]         =     url_or_filename
       elif urlparse(snake_case_						).scheme == "":
              # File, but it doesn't exist.
              raise EnvironmentError("file {} not found".format(snake_case_						)						)
       else:
              # Something unknown
              raise ValueError("unable to parse {} as a URL or as a local path".format(snake_case_						)						)

       if extract_compressed_file:
              if not is_zipfile(snake_case_						) and not tarfile.is_tarfile(snake_case_						):
                     return output_path

              # Path where we extract compressed archives
              # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
              lowercase__    ,      lowercase__:							Optional[Any]         =     os.path.split(snake_case_						)
              lowercase__:							Optional[Any]         =     output_file.replace("."		,  "-"						) + "-extracted"
              lowercase__:							Dict         =     os.path.join(snake_case_		,  snake_case_						)

              if os.path.isdir(snake_case_						) and os.listdir(snake_case_						) and not force_extract:
                     return output_path_extracted

              # Prevent parallel extractions
              lowercase__:							Union[str, Any]         =     output_path + ".lock"
              with FileLock(snake_case_						):
                     shutil.rmtree(snake_case_		,  ignore_errors=snake_case_						)
                     os.makedirs(snake_case_						)
                     if is_zipfile(snake_case_						):
                            with ZipFile(snake_case_		,  "r"						) as zip_file:
                                   zip_file.extractall(snake_case_						)
                                   zip_file.close()
                     elif tarfile.is_tarfile(snake_case_						):
                            lowercase__:							Tuple         =     tarfile.open(snake_case_						)
                            tar_file.extractall(snake_case_						)
                            tar_file.close()
                     else:
                            raise EnvironmentError("Archive format of {} could not be identified".format(snake_case_						)						)

              return output_path_extracted

       return output_path






def 							A(    snake_case_		,  snake_case_=","						):
       """simple docstring"""





       assert isinstance(snake_case_		,  snake_case_						)
       if os.path.isfile(snake_case_						):
              with open(snake_case_						) as f:
                     lowercase__:							Optional[Any]         =     eval(f.read()						)
       else:
              lowercase__:							Dict         =     requests.get(snake_case_						)
              try:
                     lowercase__:							int         =     requests.json()
              except Exception:
                     lowercase__:							Optional[Any]         =     req.content.decode()
                     assert data is not None, "could not connect"
                     try:
                            lowercase__:							Union[str, Any]         =     eval(snake_case_						)
                     except Exception:
                            lowercase__:							Dict         =     data.split("\n"						)
              req.close()
       return data






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Tuple         =     requests.get(snake_case_						)
       lowercase__:							List[str]         =     np.array(Image.open(BytesIO(response.content						)						)						)
       return img






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     url.split("/"						)[-1]
       if fn not in os.listdir(os.getcwd()						):
              wget.download(snake_case_						)
       with open(snake_case_		,  "rb"						) as stream:
              lowercase__:							Any         =     pkl.load(snake_case_						)
       lowercase__:							Dict         =     weights.pop("model"						)
       lowercase__:							List[str]         =     {}
       for k, v in model.items():
              lowercase__:							Any         =     torch.from_numpy(snake_case_						)
              if "running_var" in k:
                     lowercase__:							int         =     torch.tensor([0]						)
                     lowercase__:							Optional[int]         =     k.replace("running_var"		,  "num_batches_tracked"						)
                     lowercase__:							Tuple         =     zero
       return new






def 							A(    ):
       """simple docstring"""





       print(F"""{os.path.abspath(os.path.join(snake_case_		,  os.pardir						)						)}/demo.ipynb"""						)






def 							A(    snake_case_		,  snake_case_="RGB"						):
       """simple docstring"""





       assert isinstance(snake_case_		,  snake_case_						)
       if os.path.isfile(snake_case_						):
              lowercase__:							int         =     cva.imread(snake_case_						)
       else:
              lowercase__:							Any         =     get_image_from_url(snake_case_						)
              assert img is not None, F"""could not connect to: {im}"""
       lowercase__:							List[str]         =     cva.cvtColor(snake_case_		,  cva.COLOR_BGR2RGB						)
       if input_format == "RGB":
              lowercase__:							Optional[int]         =     img[:, :, ::-1]
       return img







def 							A(    snake_case_		,  snake_case_=1						):
       """simple docstring"""





       return (images[i : i + batch] for i in range(0		,  len(snake_case_						)		,  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