





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
      import tensorflow as tf

class 	UpperCamelCase__				(   enum.Enum     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    0
     __snake_case					:			Dict				=    1
     __snake_case					:			Union[str, Any]				=    2

@add_end_docstrings(lowerCAmelCase_     )
class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Any				=    "\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "






     def __init__(							self		:					Optional[Any]		,*lowerCamelCase__		:					Optional[Any]		,**lowerCamelCase__		:					Any )  ->	List[str]:




           '''simple docstring'''


           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )
           self.check_model_type(
               TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING )
           if "prefix" not in self._preprocess_params:
                 # This is very specific. The logic is quite complex and needs to be done
                 # as a "default".
                 # It also defines both some preprocess_kwargs and generate_kwargs
                 # which is why we cannot put them in their respective methods.
                 SCREAMING_SNAKE_CASE	=		None
                 if self.model.config.prefix is not None:
                       SCREAMING_SNAKE_CASE	=		self.model.config.prefix
                 if prefix is None and self.model.__class__.__name__ in [
                     "XLNetLMHeadModel",
                     "TransfoXLLMHeadModel",
                     "TFXLNetLMHeadModel",
                     "TFTransfoXLLMHeadModel",
                 ]:
                       # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                       SCREAMING_SNAKE_CASE	=		self.XL_PREFIX
                 if prefix is not None:
                       # Recalculate some generate_kwargs linked to prefix.
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self._sanitize_parameters(prefix=lowerCamelCase__		,**self._forward_params )
                       SCREAMING_SNAKE_CASE	=		{**self._preprocess_params, **preprocess_params}
                       SCREAMING_SNAKE_CASE	=		{**self._forward_params, **forward_params}






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[Any]=None		,lowerCamelCase__		:					Tuple=None		,lowerCamelCase__		:					Tuple=None		,lowerCamelCase__		:					Optional[int]=None		,lowerCamelCase__		:					Union[str, Any]=None		,lowerCamelCase__		:					Any=None		,lowerCamelCase__		:					List[Any]=None		,lowerCamelCase__		:					str=None		,**lowerCamelCase__		:					Union[str, Any]		,)  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{}
           if prefix is not None:
                 SCREAMING_SNAKE_CASE	=		prefix
           if prefix:
                 SCREAMING_SNAKE_CASE	=		self.tokenizer(
                     lowerCamelCase__		,padding=lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,return_tensors=self.framework )
                 SCREAMING_SNAKE_CASE	=		prefix_inputs["""input_ids"""].shape[-1]

           if handle_long_generation is not None:
                 if handle_long_generation not in {"hole"}:
                       raise ValueError(
                           F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
                           """ [None, 'hole']""" )
                 SCREAMING_SNAKE_CASE	=		handle_long_generation

           preprocess_params.update(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		generate_kwargs

           SCREAMING_SNAKE_CASE	=		{}
           if return_full_text is not None and return_type is None:
                 if return_text is not None:
                       raise ValueError("""`return_text` is mutually exclusive with `return_full_text`""" )
                 if return_tensors is not None:
                       raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`""" )
                 SCREAMING_SNAKE_CASE	=		ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
           if return_tensors is not None and return_type is None:
                 if return_text is not None:
                       raise ValueError("""`return_text` is mutually exclusive with `return_tensors`""" )
                 SCREAMING_SNAKE_CASE	=		ReturnType.TENSORS
           if return_type is not None:
                 SCREAMING_SNAKE_CASE	=		return_type
           if clean_up_tokenization_spaces is not None:
                 SCREAMING_SNAKE_CASE	=		clean_up_tokenization_spaces

           if stop_sequence is not None:
                 SCREAMING_SNAKE_CASE	=		self.tokenizer.encode(lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                 if len(lowerCamelCase__ ) > 1:
                       warnings.warn(
                           """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                           """ the stop sequence will be used as the stop sequence string in the interim.""" )
                 SCREAMING_SNAKE_CASE	=		stop_sequence_ids[0]

           return preprocess_params, forward_params, postprocess_params






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,*lowerCamelCase__		:					List[str]		,**lowerCamelCase__		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                 kwargs.update({"""add_space_before_punct_symbol""": True} )

           return super()._parse_and_tokenize(*lowerCamelCase__		,**lowerCamelCase__ )






     def __call__(							self		:					Any		,lowerCamelCase__		:					str		,**lowerCamelCase__		:					Any )  ->	Union[str, Any]:




           '''simple docstring'''


           return super().__call__(lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]=""		,lowerCamelCase__		:					Optional[Any]=None		,**lowerCamelCase__		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer(
               prefix + prompt_text		,padding=lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,return_tensors=self.framework )
           SCREAMING_SNAKE_CASE	=		prompt_text

           if handle_long_generation == "hole":
                 SCREAMING_SNAKE_CASE	=		inputs["""input_ids"""].shape[-1]
                 if "max_new_tokens" in generate_kwargs:
                       SCREAMING_SNAKE_CASE	=		generate_kwargs["""max_new_tokens"""]
                 else:
                       SCREAMING_SNAKE_CASE	=		generate_kwargs.get("""max_length"""		,self.model.config.max_length ) - cur_len
                       if new_tokens < 0:
                             raise ValueError("""We cannot infer how many new tokens are expected""" )
                 if cur_len + new_tokens > self.tokenizer.model_max_length:
                       SCREAMING_SNAKE_CASE	=		self.tokenizer.model_max_length - new_tokens
                       if keep_length <= 0:
                             raise ValueError(
                                 """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
                                 """ models max length""" )

                       SCREAMING_SNAKE_CASE	=		inputs["""input_ids"""][:, -keep_length:]
                       if "attention_mask" in inputs:
                             SCREAMING_SNAKE_CASE	=		inputs["""attention_mask"""][:, -keep_length:]

           return inputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					Union[str, Any]		,**lowerCamelCase__		:					Any )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		model_inputs["""input_ids"""]
           SCREAMING_SNAKE_CASE	=		model_inputs.get("""attention_mask"""		,lowerCamelCase__ )
           # Allow empty prompts
           if input_ids.shape[1] == 0:
                 SCREAMING_SNAKE_CASE	=		None
                 SCREAMING_SNAKE_CASE	=		None
                 SCREAMING_SNAKE_CASE	=		1
           else:
                 SCREAMING_SNAKE_CASE	=		input_ids.shape[0]
           SCREAMING_SNAKE_CASE	=		model_inputs.pop("""prompt_text""" )

           # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
           # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
           SCREAMING_SNAKE_CASE	=		generate_kwargs.pop("""prefix_length"""		,0 )
           if prefix_length > 0:
                 SCREAMING_SNAKE_CASE	=		"""max_new_tokens""" in generate_kwargs or (
                     """generation_config""" in generate_kwargs
                     and generate_kwargs["""generation_config"""].max_new_tokens is not None
                 )
                 if not has_max_new_tokens:
                       SCREAMING_SNAKE_CASE	=		generate_kwargs.get("""max_length""" ) or self.model.config.max_length
                       generate_kwargs["max_length"] += prefix_length
                 SCREAMING_SNAKE_CASE	=		"""min_new_tokens""" in generate_kwargs or (
                     """generation_config""" in generate_kwargs
                     and generate_kwargs["""generation_config"""].min_new_tokens is not None
                 )
                 if not has_min_new_tokens and "min_length" in generate_kwargs:
                       generate_kwargs["min_length"] += prefix_length

        # BS x SL
           SCREAMING_SNAKE_CASE	=		self.model.generate(input_ids=lowerCamelCase__		,attention_mask=lowerCamelCase__		,**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		generated_sequence.shape[0]
           if self.framework == "pt":
                 SCREAMING_SNAKE_CASE	=		generated_sequence.reshape(lowerCamelCase__		,out_b // in_b		,*generated_sequence.shape[1:] )
           elif self.framework == "tf":
                 SCREAMING_SNAKE_CASE	=		tf.reshape(lowerCamelCase__		,(in_b, out_b // in_b, *generated_sequence.shape[1:]) )
           return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Tuple=ReturnType.FULL_TEXT		,lowerCamelCase__		:					List[Any]=True )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		model_outputs["""generated_sequence"""][0]
           SCREAMING_SNAKE_CASE	=		model_outputs["""input_ids"""]
           SCREAMING_SNAKE_CASE	=		model_outputs["""prompt_text"""]
           SCREAMING_SNAKE_CASE	=		generated_sequence.numpy().tolist()
           SCREAMING_SNAKE_CASE	=		[]
           for sequence in generated_sequence:
                 if return_type == ReturnType.TENSORS:
                       SCREAMING_SNAKE_CASE	=		{"""generated_token_ids""": sequence}
                 elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                       # Decode text
                       SCREAMING_SNAKE_CASE	=		self.tokenizer.decode(
                           lowerCamelCase__		,skip_special_tokens=lowerCamelCase__		,clean_up_tokenization_spaces=lowerCamelCase__		,)

                       # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                       if input_ids is None:
                             SCREAMING_SNAKE_CASE	=		0
                       else:
                             SCREAMING_SNAKE_CASE	=		len(
                                 self.tokenizer.decode(
                                     input_ids[0]		,skip_special_tokens=lowerCamelCase__		,clean_up_tokenization_spaces=lowerCamelCase__		,) )

                       if return_type == ReturnType.FULL_TEXT:
                             SCREAMING_SNAKE_CASE	=		prompt_text + text[prompt_length:]
                       else:
                             SCREAMING_SNAKE_CASE	=		text[prompt_length:]

                       SCREAMING_SNAKE_CASE	=		{"""generated_text""": all_text}
                 records.append(lowerCamelCase__ )

           return records









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 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict=13		,lowerCamelCase__		:					Tuple=7		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					List[str]=17		,lowerCamelCase__		:					Union[str, Any]=23		,lowerCamelCase__		:					Tuple=11		,lowerCamelCase__		:					Any=True		,)  ->	List[Any]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, 1) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, 1) )
           SCREAMING_SNAKE_CASE	=		ids_tensor((self.batch_size, self.seq_length)		,vocab_size=1000 )
           SCREAMING_SNAKE_CASE	=		random_attention_mask((self.batch_size, self.seq_length) )

           SCREAMING_SNAKE_CASE	=		self.get_config()

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Union[str, Any]		,)  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModel(config=lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.eval()
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		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
           ),      
           )	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{
               """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 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    (DecisionTransformerModel,) if is_torch_available() else ()
     __snake_case					:			Dict				=    ()
     __snake_case					:			List[Any]				=    {"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					:			int				=    False

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			str				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			List[Any]				=    False
     __snake_case					:			Optional[int]				=    False
     __snake_case					:			List[Any]				=    False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,hidden_size=37 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE	=		[
                     """states""",
                     """actions""",
                     """rewards""",
                     """returns_to_go""",
                     """timesteps""",
                     """attention_mask""",
                 ]

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

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[int]:




           '''simple docstring'''


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

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

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

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

                 SCREAMING_SNAKE_CASE	=		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	=		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	=		(  # env.step(action)
                     torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,dtype=torch.floataa ),
                     1.0,
                     False,
                     {},
                 )

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


