





import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase__      =get_logger(__name__)


UpperCamelCase__      =Path(__file__).parent / 'model_card_template.md'
UpperCamelCase__      =uuida().hex
UpperCamelCase__      =os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__      =os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__      =HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'






def     lowerCamelCase__			(__lowerCamelCase = None	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
     if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
          return ua + "; telemetry/off"
     if is_torch_available():
          ua += f"""; torch/{_torch_version}"""
     if is_flax_available():
          ua += f"""; jax/{_jax_version}"""
          ua += f"""; flax/{_flax_version}"""
     if is_onnx_available():
          ua += f"""; onnxruntime/{_onnxruntime_version}"""
     # CI will set this value to True
     if os.environ.get("DIFFUSERS_IS_CI",       ""	).upper() in ENV_VARS_TRUE_VALUES:
          ua += "; is_ci/true"
     if isinstance(__lowerCamelCase,       __lowerCamelCase	):
          ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()	)
     elif isinstance(__lowerCamelCase,       __lowerCamelCase	):
          ua += "; " + user_agent
     return ua






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase = None,       __lowerCamelCase = None	):
     if token is None:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							HfFolder.get_token()
     if organization is None:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							whoami(__lowerCamelCase	)["name"]
          return f"""{username}/{model_id}"""
     else:
          return f"""{organization}/{model_id}"""






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     if not is_jinja_available():
          raise ValueError(
              "Modelcard rendering is based on Jinja templates."
              " Please make sure to have `jinja` installed before using `create_model_card`."
              " To install it, please run `pip install Jinja2`."	)

     if hasattr(__lowerCamelCase,       "local_rank"	) and args.local_rank not in [-1, 0]:
          return

     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							args.hub_token if hasattr(__lowerCamelCase,       "hub_token"	) else None
     _SCREAMING_SNAKE_CASE       :	Dict						    =							get_full_repo_name(__lowerCamelCase,       token=__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							ModelCard.from_template(
         card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
             language="en",       license="apache-2.0",       library_name="diffusers",       tags=[],       datasets=args.dataset_name,       metrics=[],       ),       template_path=__lowerCamelCase,       model_name=__lowerCamelCase,       repo_name=__lowerCamelCase,       dataset_name=args.dataset_name if hasattr(__lowerCamelCase,       "dataset_name"	) else None,       learning_rate=args.learning_rate,       train_batch_size=args.train_batch_size,       eval_batch_size=args.eval_batch_size,       gradient_accumulation_steps=(
             args.gradient_accumulation_steps if hasattr(__lowerCamelCase,       "gradient_accumulation_steps"	) else None
         ),       adam_betaa=args.adam_betaa if hasattr(__lowerCamelCase,       "adam_beta1"	) else None,       adam_betaa=args.adam_betaa if hasattr(__lowerCamelCase,       "adam_beta2"	) else None,       adam_weight_decay=args.adam_weight_decay if hasattr(__lowerCamelCase,       "adam_weight_decay"	) else None,       adam_epsilon=args.adam_epsilon if hasattr(__lowerCamelCase,       "adam_epsilon"	) else None,       lr_scheduler=args.lr_scheduler if hasattr(__lowerCamelCase,       "lr_scheduler"	) else None,       lr_warmup_steps=args.lr_warmup_steps if hasattr(__lowerCamelCase,       "lr_warmup_steps"	) else None,       ema_inv_gamma=args.ema_inv_gamma if hasattr(__lowerCamelCase,       "ema_inv_gamma"	) else None,       ema_power=args.ema_power if hasattr(__lowerCamelCase,       "ema_power"	) else None,       ema_max_decay=args.ema_max_decay if hasattr(__lowerCamelCase,       "ema_max_decay"	) else None,       mixed_precision=args.mixed_precision,       )

     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							os.path.join(args.output_dir,       "README.md"	)
     model_card.save(__lowerCamelCase	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase = None	):
     if resolved_file is None or commit_hash is not None:
          return commit_hash
     _SCREAMING_SNAKE_CASE       :	Any						    =							str(Path(__lowerCamelCase	).as_posix()	)
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							re.search(R"snapshots/([^/]+)/",       __lowerCamelCase	)
     if search is None:
          return None
     _SCREAMING_SNAKE_CASE       :	str						    =							search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(__lowerCamelCase	) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase__      =os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCamelCase__      =os.path.join(hf_cache_home, 'diffusers')






def     lowerCamelCase__			(__lowerCamelCase = None,       __lowerCamelCase = None	):
     if new_cache_dir is None:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							DIFFUSERS_CACHE
     if old_cache_dir is None:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							old_diffusers_cache

     _SCREAMING_SNAKE_CASE       :	Dict						    =							Path(__lowerCamelCase	).expanduser()
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							Path(__lowerCamelCase	).expanduser()
     for old_blob_path in old_cache_dir.glob("**/blobs/*"	):
          if old_blob_path.is_file() and not old_blob_path.is_symlink():
               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							new_cache_dir / old_blob_path.relative_to(__lowerCamelCase	)
               new_blob_path.parent.mkdir(parents=__lowerCamelCase,       exist_ok=__lowerCamelCase	)
               os.replace(__lowerCamelCase,       __lowerCamelCase	)
               try:
                    os.symlink(__lowerCamelCase,       __lowerCamelCase	)
               except OSError:
                    logger.warning(
                        "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."	)
     # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase__      =os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
       UpperCamelCase__      =0
else:
       with open(cache_version_file) as f:
              try:
                     UpperCamelCase__      =int(f.read())
              except ValueError:
                     UpperCamelCase__      =0

if cache_version < 1:
       UpperCamelCase__      =os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
       if old_cache_is_not_empty:
              logger.warning(
                  'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
                  'existing cached models. This is a one-time operation, you can interrupt it or run it '
                  'later by calling `diffusers.utils.hub_utils.move_cache()`.'
              )
              try:
                     move_cache()
              except Exception as e:
                     UpperCamelCase__      ='\n'.join(traceback.format_tb(e.__traceback__))
                     logger.error(
                         f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
                         'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
                         'message and we will do our best to help.'
                     )

if cache_version < 1:
       try:
              os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
              with open(cache_version_file, 'w') as f:
                     f.write('1')
       except Exception:
              logger.warning(
                  f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
                  'the directory exists and can be written to.'
              )






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase = None	):
     if variant is not None:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							weights_name.split("."	)
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							splits[:-1] + [variant] + splits[-1:]
          _SCREAMING_SNAKE_CASE       :	Dict						    =							".".join(__lowerCamelCase	)

     return weights_name






def     lowerCamelCase__			(__lowerCamelCase,       *,
    __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=None,       ):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							str(__lowerCamelCase	)
     if os.path.isfile(__lowerCamelCase	):
          return pretrained_model_name_or_path
     elif os.path.isdir(__lowerCamelCase	):
          if os.path.isfile(os.path.join(__lowerCamelCase,       __lowerCamelCase	)	):
               # Load from a PyTorch checkpoint
               _SCREAMING_SNAKE_CASE       :	int						    =							os.path.join(__lowerCamelCase,       __lowerCamelCase	)
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							os.path.join(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)
               return model_file
          else:
               raise EnvironmentError(
                   f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""	)
     else:
          # 1. First check if deprecated way of loading from branches is used
          if (
              revision in DEPRECATED_REVISION_ARGS
              and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
              and version.parse(version.parse(__lowerCamelCase	).base_version	) >= version.parse("0.20.0"	)
          ):
               try:
                    _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							hf_hub_download(
                        __lowerCamelCase,       filename=_add_variant(__lowerCamelCase,       __lowerCamelCase	),       cache_dir=__lowerCamelCase,       force_download=__lowerCamelCase,       proxies=__lowerCamelCase,       resume_download=__lowerCamelCase,       local_files_only=__lowerCamelCase,       use_auth_token=__lowerCamelCase,       user_agent=__lowerCamelCase,       subfolder=__lowerCamelCase,       revision=revision or commit_hash,       )
                    warnings.warn(
                        f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.""",       __lowerCamelCase,       )
                    return model_file
               except:  # noqa: E722
                    warnings.warn(
                        f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__lowerCamelCase,       __lowerCamelCase	)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__lowerCamelCase,       __lowerCamelCase	)}' so that the correct variant file can be added.""",       __lowerCamelCase,       )
          try:
               # 2. Load model file as usual
               _SCREAMING_SNAKE_CASE       :	Dict						    =							hf_hub_download(
                   __lowerCamelCase,       filename=__lowerCamelCase,       cache_dir=__lowerCamelCase,       force_download=__lowerCamelCase,       proxies=__lowerCamelCase,       resume_download=__lowerCamelCase,       local_files_only=__lowerCamelCase,       use_auth_token=__lowerCamelCase,       user_agent=__lowerCamelCase,       subfolder=__lowerCamelCase,       revision=revision or commit_hash,       )
               return model_file

          except RepositoryNotFoundError:
               raise EnvironmentError(
                   f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
                   "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                   "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
                   "login`."	)
          except RevisionNotFoundError:
               raise EnvironmentError(
                   f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
                   "this model name. Check the model page at "
                   f"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""	)
          except EntryNotFoundError:
               raise EnvironmentError(
                   f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""	)
          except HTTPError as err:
               raise EnvironmentError(
                   f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""	)
          except ValueError:
               raise EnvironmentError(
                   f"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
                   f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
                   f""" directory containing a file named {weights_name} or"""
                   " \nCheckout your internet connection or see how to run the library in"
                   " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."	)
          except EnvironmentError:
               raise EnvironmentError(
                   f"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
                   "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                   f"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
                   f"""containing a file named {weights_name}"""	)






import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import DecisionTransformerModel
       from transformers.models.decision_transformer.modeling_decision_transformer import (
           DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=7    ,						__lowerCamelCase=6    ,						__lowerCamelCase=1_7    ,						__lowerCamelCase=2_3    ,						__lowerCamelCase=1_1    ,						__lowerCamelCase=True    ,						)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							parent
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	int						    =							seq_length
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							act_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							state_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							max_length
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							is_training




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor((self.batch_size, self.seq_length)    ,						vocab_size=1_0_0_0							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							random_attention_mask((self.batch_size, self.seq_length)							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_config()

            return (
                config,
                states,
                actions,
                rewards,
                returns_to_go,
                timesteps,
                attention_mask,
            )




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            return DecisionTransformerConfig(
                batch_size=self.batch_size    ,						seq_length=self.seq_length    ,						act_dim=self.act_dim    ,						state_dim=self.state_dim    ,						hidden_size=self.hidden_size    ,						max_length=self.max_length    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DecisionTransformerModel(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)

            self.parent.assertEqual(result.state_preds.shape    ,						states.shape							)
            self.parent.assertEqual(result.action_preds.shape    ,						actions.shape							)
            self.parent.assertEqual(result.return_preds.shape    ,						returns_to_go.shape							)
            self.parent.assertEqual(
                result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length * 3, self.hidden_size)							)  # seq length *3 as there are 3 modelities: states, returns and actions







       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.prepare_config_and_inputs()
            (
                (
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			
            )       :	Optional[Any]						    =							config_and_inputs
            _SCREAMING_SNAKE_CASE       :	Any						    =							{
                "states": states,
                "actions": actions,
                "rewards": rewards,
                "returns_to_go": returns_to_go,
                "timesteps": timesteps,
                "attention_mask": attention_mask,
            }
            return config, inputs_dict





@require_torch
class 			lowerCAmelCase__( __lowercase  ,     __lowercase  ,     __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    (DecisionTransformerModel,) if is_torch_available() else ()
       __snake_case 			=    ()
       __snake_case 			=    {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

       # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
       __snake_case 			=    False

       # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModelTester(self							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							ConfigTester(self    ,						config_class=__lowerCamelCase    ,						hidden_size=3_7							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            self.config_tester.run_common_tests()




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Dict:
            for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _SCREAMING_SNAKE_CASE       :	str						    =							DecisionTransformerModel.from_pretrained(__lowerCamelCase							)
                 self.assertIsNotNone(__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_class(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							inspect.signature(model.forward							)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _SCREAMING_SNAKE_CASE       :	int						    =							[*signature.parameters.keys()]

                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							[
                     "states",
                     "actions",
                     "rewards",
                     "returns_to_go",
                     "timesteps",
                     "attention_mask",
                 ]

                 self.assertListEqual(arg_names[: len(__lowerCamelCase							)]    ,						__lowerCamelCase							)





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       @slow
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							2  # number of steps of autoregressive prediction we will perform
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							1_0  # defined by the RL environment, may be normalized
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model.to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model.config
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							)  # env.reset()

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor(
                [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,						device=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.tensor(__lowerCamelCase    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							).reshape(1    ,						1    ,						1							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							state
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.zeros(1    ,						0    ,						config.act_dim    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							torch.zeros(1    ,						0    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.tensor(0    ,						device=__lowerCamelCase    ,						dtype=torch.long							).reshape(1    ,						1							)

            for step in range(__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	int						    =							torch.cat([actions, torch.zeros(1    ,						1    ,						config.act_dim    ,						device=__lowerCamelCase							)]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	Any						    =							torch.cat([rewards, torch.zeros(1    ,						1    ,						device=__lowerCamelCase							)]    ,						dim=1							)

                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.ones(1    ,						states.shape[1]							).to(dtype=torch.long    ,						device=states.device							)

                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model(
                          states=__lowerCamelCase    ,						actions=__lowerCamelCase    ,						rewards=__lowerCamelCase    ,						returns_to_go=__lowerCamelCase    ,						timesteps=__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						return_dict=__lowerCamelCase    ,						)

                 self.assertEqual(action_pred.shape    ,						actions.shape							)
                 self.assertTrue(torch.allclose(action_pred[0, -1]    ,						expected_outputs[step]    ,						atol=1E-4							)							)
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							(  # env.step(action)
                     torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							),
                     1.0,
                     False,
                     {},
                 )

                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							action_pred[0, -1]
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.cat([states, state]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							returns_to_go[0, -1] - reward
                 _SCREAMING_SNAKE_CASE       :	str						    =							torch.cat([returns_to_go, pred_return.reshape(1    ,						1    ,						1							)]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.cat(
                     [timesteps, torch.ones((1, 1)    ,						device=__lowerCamelCase    ,						dtype=torch.long							) * (step + 1)]    ,						dim=1							)