



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule






class 						lowercase		(					snake_case__):






	"""simple docstring"""


	a__ :   BigBirdConfig
	a__ :   jnp.dtype						  =				jnp.floataa
	a__ :   bool						  =				True




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]	)     ->					Any:
							super().setup()
							UpperCAmelCase_=  nn.Dense(5		,    dtype=self.dtype	)




	def __call__(      self      :		Dict		,    *__UpperCAmelCase      :		str		,    **__UpperCAmelCase      :		Dict	)     ->					Any:
							UpperCAmelCase_=  super().__call__(*__UpperCAmelCase		,    **__UpperCAmelCase	)
							UpperCAmelCase_=  self.cls(outputs[2]	)
							return outputs[:2] + (cls_out,)






class 						lowercase		(					snake_case__):






	"""simple docstring"""


	a__ :   Any						  =				FlaxBigBirdForNaturalQuestionsModule



def 			__a	(       lowerCAmelCase_  :   int     ,lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   str     ,lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   Optional[int]     ,lowerCAmelCase_  :   List[str]							) ->					List[Any]:






						'''simple docstring'''



						def cross_entropy(lowerCAmelCase_  :   int     ,lowerCAmelCase_  :   int     ,lowerCAmelCase_  :   Optional[int]=None							):
												UpperCAmelCase_=  logits.shape[-1]
												UpperCAmelCase_=  (labels[..., None] == jnp.arange(__A							)[None]).astype("""f4"""							)
												UpperCAmelCase_=  jax.nn.log_softmax(__A     ,axis=-1							)
												UpperCAmelCase_=  -jnp.sum(labels * logits     ,axis=-1							)
												if reduction is not None:
																		UpperCAmelCase_=  reduction(__A							)
												return loss

						UpperCAmelCase_=  partial(__A     ,reduction=jnp.mean							)
						UpperCAmelCase_=  cross_entropy(__A     ,__A							)
						UpperCAmelCase_=  cross_entropy(__A     ,__A							)
						UpperCAmelCase_=  cross_entropy(__A     ,__A							)
						return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 						lowercase		:






	"""simple docstring"""


	a__ :   str						  =				"google/bigbird-roberta-base"
	a__ :   int						  =				3000
	a__ :   int						  =				1_0500

	a__ :   int						  =				128
	a__ :   int						  =				3

	a__ :   int						  =				1
	a__ :   int						  =				5

	# tx_args
	a__ :   float						  =				3e-5
	a__ :   float						  =				0.0
	a__ :   int						  =				2_0000
	a__ :   float						  =				0.0095

	a__ :   str						  =				"bigbird-roberta-natural-questions"
	a__ :   str						  =				"training-expt"
	a__ :   str						  =				"data/nq-training.jsonl"
	a__ :   str						  =				"data/nq-validation.jsonl"




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any	)     ->					List[Any]:
							os.makedirs(self.base_dir		,    exist_ok=__UpperCAmelCase	)
							UpperCAmelCase_=  os.path.join(self.base_dir		,    self.save_dir	)
							UpperCAmelCase_=  self.batch_size_per_device * jax.device_count()






@dataclass
class 						lowercase		:






	"""simple docstring"""


	a__ :   int
	a__ :   int						  =				4096  # no dynamic padding on TPUs




	def __call__(      self      :		Optional[int]		,    __UpperCAmelCase      :		str	)     ->					Tuple:
							UpperCAmelCase_=  self.collate_fn(__UpperCAmelCase	)
							UpperCAmelCase_=  jax.tree_util.tree_map(__UpperCAmelCase		,    __UpperCAmelCase	)
							return batch




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		Optional[int]	)     ->					List[str]:
							UpperCAmelCase_,  UpperCAmelCase_=  self.fetch_inputs(features["""input_ids"""]	)
							UpperCAmelCase_=  {
							    """input_ids""": jnp.array(__UpperCAmelCase		,    dtype=jnp.intaa	),
							    """attention_mask""": jnp.array(__UpperCAmelCase		,    dtype=jnp.intaa	),
							    """start_labels""": jnp.array(features["""start_token"""]		,    dtype=jnp.intaa	),
							    """end_labels""": jnp.array(features["""end_token"""]		,    dtype=jnp.intaa	),
							    """pooled_labels""": jnp.array(features["""category"""]		,    dtype=jnp.intaa	),
							}
							return batch




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]		,    __UpperCAmelCase      :		str	)     ->					Tuple:
							UpperCAmelCase_=  [self._fetch_inputs(__UpperCAmelCase	) for ids in input_ids]
							return zip(*__UpperCAmelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		int	)     ->					Optional[Any]:
							UpperCAmelCase_=  [1 for _ in range(len(__UpperCAmelCase	)	)]
							while len(__UpperCAmelCase	) < self.max_length:
													input_ids.append(self.pad_id	)
													attention_mask.append(0	)
							return input_ids, attention_mask



def 			__a	(       lowerCAmelCase_  :   Any     ,lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   Any=None							) ->					Dict:






						'''simple docstring'''



						if seed is not None:
												UpperCAmelCase_=  dataset.shuffle(seed=__A							)
						for i in range(len(__A							) // batch_size							):
												UpperCAmelCase_=  dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(__A							)



@partial(jax.pmap     ,axis_name="""batch"""							)
def 			__a	(       lowerCAmelCase_  :   Optional[int]     ,lowerCAmelCase_  :   List[Any]     ,**lowerCAmelCase_  :   List[Any]							) ->					str:






						'''simple docstring'''



						def loss_fn(lowerCAmelCase_  :   List[Any]							):
												UpperCAmelCase_=  model_inputs.pop("""start_labels"""							)
												UpperCAmelCase_=  model_inputs.pop("""end_labels"""							)
												UpperCAmelCase_=  model_inputs.pop("""pooled_labels"""							)

												UpperCAmelCase_=  state.apply_fn(**__A     ,params=__A     ,dropout_rng=__A     ,train=__A							)
												UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  outputs

												return state.loss_fn(
												    __A     ,__A     ,__A     ,__A     ,__A     ,__A     ,)

						UpperCAmelCase_,  UpperCAmelCase_=  jax.random.split(__A							)
						UpperCAmelCase_=  jax.value_and_grad(__A							)
						UpperCAmelCase_,  UpperCAmelCase_=  grad_fn(state.params							)
						UpperCAmelCase_=  jax.lax.pmean({"""loss""": loss}     ,axis_name="""batch"""							)
						UpperCAmelCase_=  jax.lax.pmean(__A     ,"""batch"""							)

						UpperCAmelCase_=  state.apply_gradients(grads=__A							)
						return state, metrics, new_drp_rng



@partial(jax.pmap     ,axis_name="""batch"""							)
def 			__a	(       lowerCAmelCase_  :   Any     ,**lowerCAmelCase_  :   int							) ->					Any:






						'''simple docstring'''



						UpperCAmelCase_=  model_inputs.pop("""start_labels"""							)
						UpperCAmelCase_=  model_inputs.pop("""end_labels"""							)
						UpperCAmelCase_=  model_inputs.pop("""pooled_labels"""							)

						UpperCAmelCase_=  state.apply_fn(**__A     ,params=state.params     ,train=__A							)
						UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  outputs

						UpperCAmelCase_=  state.loss_fn(__A     ,__A     ,__A     ,__A     ,__A     ,__A							)
						UpperCAmelCase_=  jax.lax.pmean({"""loss""": loss}     ,axis_name="""batch"""							)
						return metrics






class 						lowercase		(					train_state.TrainState):






	"""simple docstring"""


	a__ :   Callable						  =				struct.field(pytree_node=snake_case__)






@dataclass
class 						lowercase		:






	"""simple docstring"""


	a__ :   Args
	a__ :   Callable
	a__ :   Callable
	a__ :   Callable
	a__ :   Callable
	a__ :   wandb
	a__ :   Callable						  =				None




	def     _SCREAMING_SNAKE_CASE						(      self      :		int		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Optional[int]		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		List[str]=None	)     ->					Tuple:
							UpperCAmelCase_=  model.params
							UpperCAmelCase_=  TrainState.create(
							    apply_fn=model.__call__		,    params=__UpperCAmelCase		,    tx=__UpperCAmelCase		,    loss_fn=__UpperCAmelCase		,    )
							if ckpt_dir is not None:
													UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  restore_checkpoint(__UpperCAmelCase		,    __UpperCAmelCase	)
													UpperCAmelCase_=  {
													    """lr""": args.lr,
													    """init_lr""": args.init_lr,
													    """warmup_steps""": args.warmup_steps,
													    """num_train_steps""": num_train_steps,
													    """weight_decay""": args.weight_decay,
													}
													UpperCAmelCase_,  UpperCAmelCase_=  build_tx(**__UpperCAmelCase	)
													UpperCAmelCase_=  train_state.TrainState(
													    step=__UpperCAmelCase		,    apply_fn=model.__call__		,    params=__UpperCAmelCase		,    tx=__UpperCAmelCase		,    opt_state=__UpperCAmelCase		,    )
													UpperCAmelCase_=  args
													UpperCAmelCase_=  data_collator
													UpperCAmelCase_=  lr
													UpperCAmelCase_=  params
							UpperCAmelCase_=  jax_utils.replicate(__UpperCAmelCase	)
							return state




	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Tuple		,    __UpperCAmelCase      :		Dict	)     ->					Dict:
							UpperCAmelCase_=  self.args
							UpperCAmelCase_=  len(__UpperCAmelCase	) // args.batch_size

							UpperCAmelCase_=  jax.random.PRNGKey(0	)
							UpperCAmelCase_=  jax.random.split(__UpperCAmelCase		,    jax.device_count()	)
							for epoch in range(args.max_epochs	):
													UpperCAmelCase_=  jnp.array(0		,    dtype=jnp.floataa	)
													UpperCAmelCase_=  get_batched_dataset(__UpperCAmelCase		,    args.batch_size		,    seed=__UpperCAmelCase	)
													UpperCAmelCase_=  0
													for batch in tqdm(__UpperCAmelCase		,    total=__UpperCAmelCase		,    desc=F"""Running EPOCH-{epoch}"""	):
																			UpperCAmelCase_=  self.data_collator(__UpperCAmelCase	)
																			UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_=  self.train_step_fn(__UpperCAmelCase		,    __UpperCAmelCase		,    **__UpperCAmelCase	)
																			running_loss += jax_utils.unreplicate(metrics["""loss"""]	)
																			i += 1
																			if i % args.logging_steps == 0:
																									UpperCAmelCase_=  jax_utils.unreplicate(state.step	)
																									UpperCAmelCase_=  running_loss.item() / i
																									UpperCAmelCase_=  self.scheduler_fn(state_step - 1	)

																									UpperCAmelCase_=  self.evaluate(__UpperCAmelCase		,    __UpperCAmelCase	)
																									UpperCAmelCase_=  {
																									    """step""": state_step.item(),
																									    """eval_loss""": eval_loss.item(),
																									    """tr_loss""": tr_loss,
																									    """lr""": lr.item(),
																									}
																									tqdm.write(str(__UpperCAmelCase	)	)
																									self.logger.log(__UpperCAmelCase		,    commit=__UpperCAmelCase	)

																			if i % args.save_steps == 0:
																									self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""		,    state=__UpperCAmelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		Tuple		,    __UpperCAmelCase      :		int	)     ->					List[Any]:
							UpperCAmelCase_=  get_batched_dataset(__UpperCAmelCase		,    self.args.batch_size	)
							UpperCAmelCase_=  len(__UpperCAmelCase	) // self.args.batch_size
							UpperCAmelCase_=  jnp.array(0		,    dtype=jnp.floataa	)
							UpperCAmelCase_=  0
							for batch in tqdm(__UpperCAmelCase		,    total=__UpperCAmelCase		,    desc="""Evaluating ... """	):
													UpperCAmelCase_=  self.data_collator(__UpperCAmelCase	)
													UpperCAmelCase_=  self.val_step_fn(__UpperCAmelCase		,    **__UpperCAmelCase	)
													running_loss += jax_utils.unreplicate(metrics["""loss"""]	)
													i += 1
							return running_loss / i




	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Optional[int]	)     ->					Dict:
							UpperCAmelCase_=  jax_utils.unreplicate(__UpperCAmelCase	)
							print(F"""SAVING CHECKPOINT IN {save_dir}"""		,    end=""" ... """	)
							self.model_save_fn(__UpperCAmelCase		,    params=state.params	)
							with open(os.path.join(__UpperCAmelCase		,    """opt_state.msgpack"""	)		,    """wb"""	) as f:
													f.write(to_bytes(state.opt_state	)	)
							joblib.dump(self.args		,    os.path.join(__UpperCAmelCase		,    """args.joblib"""	)	)
							joblib.dump(self.data_collator		,    os.path.join(__UpperCAmelCase		,    """data_collator.joblib"""	)	)
							with open(os.path.join(__UpperCAmelCase		,    """training_state.json"""	)		,    """w"""	) as f:
													json.dump({"""step""": state.step.item()}		,    __UpperCAmelCase	)
							print("""DONE"""	)



def 			__a	(       lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   str							) ->					Optional[Any]:






						'''simple docstring'''



						print(F"""RESTORING CHECKPOINT FROM {save_dir}"""     ,end=""" ... """							)
						with open(os.path.join(__A     ,"""flax_model.msgpack"""							)     ,"""rb"""							) as f:
												UpperCAmelCase_=  from_bytes(state.params     ,f.read()							)

						with open(os.path.join(__A     ,"""opt_state.msgpack"""							)     ,"""rb"""							) as f:
												UpperCAmelCase_=  from_bytes(state.opt_state     ,f.read()							)

						UpperCAmelCase_=  joblib.load(os.path.join(__A     ,"""args.joblib"""							)							)
						UpperCAmelCase_=  joblib.load(os.path.join(__A     ,"""data_collator.joblib"""							)							)

						with open(os.path.join(__A     ,"""training_state.json"""							)     ,"""r"""							) as f:
												UpperCAmelCase_=  json.load(__A							)
						UpperCAmelCase_=  training_state["""step"""]

						print("""DONE"""							)
						return params, opt_state, step, args, data_collator



def 			__a	(       lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   Any     ,lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   Union[str, Any]							) ->					Tuple:






						'''simple docstring'''



						UpperCAmelCase_=  num_train_steps - warmup_steps
						UpperCAmelCase_=  optax.linear_schedule(init_value=__A     ,end_value=__A     ,transition_steps=__A							)
						UpperCAmelCase_=  optax.linear_schedule(init_value=__A     ,end_value=1E-7     ,transition_steps=__A							)
						UpperCAmelCase_=  optax.join_schedules(schedules=[warmup_fn, decay_fn]     ,boundaries=[warmup_steps]							)
						return lr



def 			__a	(       lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   int     ,lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   int							) ->					List[Any]:






						'''simple docstring'''



						def weight_decay_mask(lowerCAmelCase_  :   Optional[int]							):
												UpperCAmelCase_=  traverse_util.flatten_dict(__A							)
												UpperCAmelCase_=  {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
												return traverse_util.unflatten_dict(__A							)

						UpperCAmelCase_=  scheduler_fn(__A     ,__A     ,__A     ,__A							)

						UpperCAmelCase_=  optax.adamw(learning_rate=__A     ,weight_decay=__A     ,mask=__A							)
						return tx, lr









'''simple docstring'''





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 __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    act_dim
      _a           =    state_dim
      _a           =    hidden_size
      _a           =    max_length
      _a           =    is_training



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

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



 def    a__    (self			)       ->	str:


      """simple docstring"""



      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    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  A					,  A					,  A					,  A					,  A			)

      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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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 __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			str		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a           =    DecisionTransformerModel.from_pretrained(A			)
           self.assertIsNotNone(A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a           =    model_class(A			)
           _a           =    inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a           =    [*signature.parameters.keys()]

           _a           =    [
               '''states''',
               '''actions''',
               '''rewards''',
               '''returns_to_go''',
               '''timesteps''',
               '''attention_mask''',
           ]

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






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    2  # number of steps of autoregressive prediction we will perform
      _a           =    10  # defined by the RL environment, may be normalized
      _a           =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
      _a           =    model.to(A			)
      _a           =    model.config
      torch.manual_seed(0			)
      _a           =    torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			)  # env.reset()

      _a           =    torch.tensor(
          [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]					,  device=A			)

      _a           =    torch.tensor(A					,  device=A					,  dtype=torch.floataa			).reshape(1					,  1					,  1			)
      _a           =    state
      _a           =    torch.zeros(1					,  0					,  config.act_dim					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.zeros(1					,  0					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.tensor(0					,  device=A					,  dtype=torch.long			).reshape(1					,  1			)

      for step in range(A			):
           _a           =    torch.cat([actions, torch.zeros(1					,  1					,  config.act_dim					,  device=A			)]					,  dim=1			)
           _a           =    torch.cat([rewards, torch.zeros(1					,  1					,  device=A			)]					,  dim=1			)

           _a           =    torch.ones(1					,  states.shape[1]			).to(dtype=torch.long					,  device=states.device			)

           with torch.no_grad():
                _a    ,       _a    ,       _a           =    model(
                    states=A					,  actions=A					,  rewards=A					,  returns_to_go=A					,  timesteps=A					,  attention_mask=A					,  return_dict=A					,  )

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

           _a           =    action_pred[0, -1]
           _a           =    torch.cat([states, state]					,  dim=1			)
           _a           =    returns_to_go[0, -1] - reward
           _a           =    torch.cat([returns_to_go, pred_return.reshape(1					,  1					,  1			)]					,  dim=1			)
           _a           =    torch.cat(
               [timesteps, torch.ones((1, 1)					,  device=A					,  dtype=torch.long			) * (step + 1)]					,  dim=1			)



