






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

A__	:    str			 = random.Random()

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Union[str, Any]=1.0    ,						lowerCamelCase__     :   List[str]=None    ,						lowerCamelCase__     :   str=None  )    ->     Dict:
							if rng is None:
														lowerCamelCase_	:							Union[str, Any]									=global_rng

							lowerCamelCase_	:							int									=[]
							for batch_idx in range(shape[0]  ):
														values.append([]  )
														for _ in range(shape[1]  ):
																					values[-1].append(rng.random() * scale  )

							return values







@require_torch
@require_torchaudio
class        lowercase__      (							unittest.TestCase       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[Any]=7       ,	snake_case__					:  Union[str, Any]=400       ,	snake_case__					:  Union[str, Any]=2000       ,	snake_case__					:  Any=24       ,	snake_case__					:  Union[str, Any]=24       ,	snake_case__					:  Optional[Any]=0.0       ,	snake_case__					:  Optional[int]=1_6000       ,	snake_case__					:  str=True       ,	snake_case__					:  List[Any]=True       ,	):
										lowerCamelCase_	:							Tuple									=parent
										lowerCamelCase_	:							Tuple									=batch_size
										lowerCamelCase_	:							Tuple									=min_seq_length
										lowerCamelCase_	:							Any									=max_seq_length
										lowerCamelCase_	:							Union[str, Any]									=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowerCamelCase_	:							Union[str, Any]									=feature_size
										lowerCamelCase_	:							str									=num_mel_bins
										lowerCamelCase_	:							Tuple									=padding_value
										lowerCamelCase_	:							str									=sampling_rate
										lowerCamelCase_	:							str									=return_attention_mask
										lowerCamelCase_	:							int									=do_normalize


			def      UpperCAmelCase__						(				self					:  Dict ):
										return {
										    "feature_size": self.feature_size,
										    "num_mel_bins": self.num_mel_bins,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  int=False       ,	snake_case__					:  Union[str, Any]=False ):
										def _flatten(snake_case__					:  List[str] ):
																	return list(itertools.chain(*snake_case__ ) )

										if equal_length:
																	lowerCamelCase_	:							Dict									=[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
										else:
																	# make sure that inputs increase in size
																	lowerCamelCase_	:							int									=[
																	    floats_list((x, self.feature_size) )
																	    for x in range(self.min_seq_length       ,	self.max_seq_length       ,	self.seq_length_diff )
																	]
										if numpify:
																	lowerCamelCase_	:							Union[str, Any]									=[np.asarray(snake_case__ ) for x in speech_inputs]
										return speech_inputs







@require_torch
@require_torchaudio
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			SpeechaTextFeatureExtractor if is_speech_available() else None


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							str									=SpeechaTextFeatureExtractionTester(self )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Optional[Any] ):
										self.assertTrue(np.all(np.mean(snake_case__       ,	axis=0 ) < 1E-3 ) )
										self.assertTrue(np.all(np.abs(np.var(snake_case__       ,	axis=0 ) - 1 ) < 1E-3 ) )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										# Tests that all call wrap to encode_plus and batch_encode_plus
										lowerCamelCase_	:							int									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										# create three inputs of length 800, 1000, and 1200
										lowerCamelCase_	:							Tuple									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]
										lowerCamelCase_	:							Any									=[np.asarray(snake_case__ ) for speech_input in speech_inputs]

										# Test feature size
										lowerCamelCase_	:							Tuple									=feature_extractor(snake_case__       ,	padding=snake_case__       ,	return_tensors="np" ).input_features
										self.assertTrue(input_features.ndim == 3 )
										self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )

										# Test not batched input
										lowerCamelCase_	:							Union[str, Any]									=feature_extractor(speech_inputs[0]       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							List[str]									=feature_extractor(np_speech_inputs[0]       ,	return_tensors="np" ).input_features
										self.assertTrue(np.allclose(snake_case__       ,	snake_case__       ,	atol=1E-3 ) )

										# Test batched
										lowerCamelCase_	:							Tuple									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							List[str]									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										for enc_seq_a, enc_seq_a in zip(snake_case__       ,	snake_case__ ):
																	self.assertTrue(np.allclose(snake_case__       ,	snake_case__       ,	atol=1E-3 ) )

										# Test 2-D numpy arrays are batched.
										lowerCamelCase_	:							Tuple									=[floats_list((1, x) )[0] for x in (800, 800, 800)]
										lowerCamelCase_	:							Optional[Any]									=np.asarray(snake_case__ )
										lowerCamelCase_	:							Optional[int]									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							Optional[int]									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										for enc_seq_a, enc_seq_a in zip(snake_case__       ,	snake_case__ ):
																	self.assertTrue(np.allclose(snake_case__       ,	snake_case__       ,	atol=1E-3 ) )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Optional[int]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Any									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]

										lowerCamelCase_	:							List[Any]									=["longest", "max_length", "do_not_pad"]
										lowerCamelCase_	:							Optional[Any]									=[None, 16, None]
										for max_length, padding in zip(snake_case__       ,	snake_case__ ):
																	lowerCamelCase_	:							Optional[int]									=feature_extractor(
																	    snake_case__       ,	padding=snake_case__       ,	max_length=snake_case__       ,	return_attention_mask=snake_case__ )
																	lowerCamelCase_	:							Dict									=inputs.input_features
																	lowerCamelCase_	:							Any									=inputs.attention_mask
																	lowerCamelCase_	:							List[Any]									=[np.sum(snake_case__ ) for x in attention_mask]

																	self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
																	self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
																	self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Any									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Union[str, Any]									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]

										lowerCamelCase_	:							List[str]									=["longest", "max_length", "do_not_pad"]
										lowerCamelCase_	:							Union[str, Any]									=[None, 16, None]
										for max_length, padding in zip(snake_case__       ,	snake_case__ ):
																	lowerCamelCase_	:							str									=feature_extractor(
																	    snake_case__       ,	max_length=snake_case__       ,	padding=snake_case__       ,	return_tensors="np"       ,	return_attention_mask=snake_case__ )
																	lowerCamelCase_	:							List[Any]									=inputs.input_features
																	lowerCamelCase_	:							Dict									=inputs.attention_mask
																	lowerCamelCase_	:							List[Any]									=[np.sum(snake_case__ ) for x in attention_mask]

																	self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
																	self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
																	self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
																	self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
																	self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							List[str]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							int									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]
										lowerCamelCase_	:							str									=feature_extractor(
										    snake_case__       ,	padding="max_length"       ,	max_length=4       ,	truncation=snake_case__       ,	return_tensors="np"       ,	return_attention_mask=snake_case__       ,	)
										lowerCamelCase_	:							List[Any]									=inputs.input_features
										lowerCamelCase_	:							str									=inputs.attention_mask
										lowerCamelCase_	:							Tuple									=np.sum(attention_mask == 1       ,	axis=1 )

										self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
										self._check_zero_mean_unit_variance(input_features[1] )
										self._check_zero_mean_unit_variance(input_features[2] )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							List[Any]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Union[str, Any]									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]
										lowerCamelCase_	:							List[str]									=feature_extractor(
										    snake_case__       ,	padding="longest"       ,	max_length=4       ,	truncation=snake_case__       ,	return_tensors="np"       ,	return_attention_mask=snake_case__       ,	)
										lowerCamelCase_	:							List[str]									=inputs.input_features
										lowerCamelCase_	:							List[Any]									=inputs.attention_mask
										lowerCamelCase_	:							Optional[int]									=np.sum(attention_mask == 1       ,	axis=1 )

										self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
										self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
										self._check_zero_mean_unit_variance(input_features[2] )

										# make sure that if max_length < longest -> then pad to max_length
										self.assertEqual(input_features.shape       ,	(3, 4, 24) )

										lowerCamelCase_	:							List[Any]									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]
										lowerCamelCase_	:							Any									=feature_extractor(
										    snake_case__       ,	padding="longest"       ,	max_length=16       ,	truncation=snake_case__       ,	return_tensors="np"       ,	return_attention_mask=snake_case__       ,	)
										lowerCamelCase_	:							int									=inputs.input_features
										lowerCamelCase_	:							List[str]									=inputs.attention_mask
										lowerCamelCase_	:							Optional[Any]									=np.sum(attention_mask == 1       ,	axis=1 )

										self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
										self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
										self._check_zero_mean_unit_variance(input_features[2] )

										# make sure that if max_length < longest -> then pad to max_length
										self.assertEqual(input_features.shape       ,	(3, 6, 24) )


			def      UpperCAmelCase__						(				self					:  Dict ):
										import torch

										lowerCamelCase_	:							Optional[int]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Optional[int]									=np.random.rand(100       ,	32 ).astype(np.floataa )
										lowerCamelCase_	:							Tuple									=np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
																	lowerCamelCase_	:							Optional[int]									=feature_extractor.pad([{"input_features": inputs}]       ,	return_tensors="np" )
																	self.assertTrue(np_processed.input_features.dtype == np.floataa )
																	lowerCamelCase_	:							Tuple									=feature_extractor.pad([{"input_features": inputs}]       ,	return_tensors="pt" )
																	self.assertTrue(pt_processed.input_features.dtype == torch.floataa )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Optional[int] ):
										from datasets import load_dataset

										lowerCamelCase_	:							Dict									=load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,	"clean"       ,	split="validation" )
										# automatic decoding with librispeech
										lowerCamelCase_	:							Optional[Any]									=ds.sort("id" ).select(range(snake_case__ ) )[:num_samples]["audio"]

										return [x["array"] for x in speech_samples]


			def      UpperCAmelCase__						(				self					:  int ):
										# fmt: off
										lowerCamelCase_	:							Dict									=np.array([
										    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
										    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
										    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
										] )
										# fmt: on

										lowerCamelCase_	:							List[str]									=self._load_datasamples(1 )
										lowerCamelCase_	:							Optional[int]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Tuple									=feature_extractor(snake_case__       ,	return_tensors="pt" ).input_features
										self.assertEquals(input_features.shape       ,	(1, 584, 24) )
										self.assertTrue(np.allclose(input_features[0, 0, :30]       ,	snake_case__       ,	atol=1E-4 ) )











"""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        lowercase__      :


			def __init__(				self					:  List[Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Tuple=13       ,	snake_case__					:  str=7       ,	snake_case__					:  Union[str, Any]=6       ,	snake_case__					:  str=17       ,	snake_case__					:  Any=23       ,	snake_case__					:  int=11       ,	snake_case__					:  Tuple=True       ,	):
										lowerCamelCase_	:							str									=parent
										lowerCamelCase_	:							Union[str, Any]									=batch_size
										lowerCamelCase_	:							List[Any]									=seq_length
										lowerCamelCase_	:							Union[str, Any]									=act_dim
										lowerCamelCase_	:							Optional[Any]									=state_dim
										lowerCamelCase_	:							Optional[Any]									=hidden_size
										lowerCamelCase_	:							Tuple									=max_length
										lowerCamelCase_	:							List[Any]									=is_training


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
										lowerCamelCase_	:							List[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							List[Any]									=ids_tensor((self.batch_size, self.seq_length)       ,	vocab_size=1000 )
										lowerCamelCase_	:							Optional[int]									=random_attention_mask((self.batch_size, self.seq_length) )

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

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


			def      UpperCAmelCase__						(				self					:  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					:  Optional[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Dict       ,	snake_case__					:  int       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  List[str]       ,	snake_case__					:  List[str]       ,	):
										lowerCamelCase_	:							Tuple									=DecisionTransformerModel(config=snake_case__ )
										model.to(snake_case__ )
										model.eval()
										lowerCamelCase_	:							str									=model(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ )

										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					:  List[str] ):
										lowerCamelCase_	:							List[str]									=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						
										)	:							Optional[int]									=config_and_inputs
										lowerCamelCase_	:							Optional[int]									={
										    "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        lowercase__      (							snake_case__,       snake_case__,       snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			(DecisionTransformerModel,) if is_torch_available() else ()
			_UpperCAmelCase		:int             =			()
			_UpperCAmelCase		:int             =			{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

			# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
			_UpperCAmelCase		:Union[str, Any]             =			False

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


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Dict									=DecisionTransformerModelTester(self )
										lowerCamelCase_	:							str									=ConfigTester(self       ,	config_class=snake_case__       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Tuple									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_	:							str									=DecisionTransformerModel.from_pretrained(snake_case__ )
																	self.assertIsNotNone(snake_case__ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_							,						lowerCamelCase_	:							Dict									=self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowerCamelCase_	:							List[str]									=[
																	    "states",
																	    "actions",
																	    "rewards",
																	    "returns_to_go",
																	    "timesteps",
																	    "attention_mask",
																	]

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







@require_torch
class        lowercase__      (							unittest.TestCase       ):


			@slow
			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Optional[int]									=2  # number of steps of autoregressive prediction we will perform
										lowerCamelCase_	:							int									=10  # defined by the RL environment, may be normalized
										lowerCamelCase_	:							List[Any]									=DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
										lowerCamelCase_	:							Union[str, Any]									=model.to(snake_case__ )
										lowerCamelCase_	:							Any									=model.config
										torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=torch.randn(1       ,	1       ,	config.state_dim ).to(device=snake_case__       ,	dtype=torch.floataa )  # env.reset()

										lowerCamelCase_	:							Optional[Any]									=torch.tensor(
										    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]       ,	device=snake_case__ )

										lowerCamelCase_	:							int									=torch.tensor(snake_case__       ,	device=snake_case__       ,	dtype=torch.floataa ).reshape(1       ,	1       ,	1 )
										lowerCamelCase_	:							str									=state
										lowerCamelCase_	:							Optional[int]									=torch.zeros(1       ,	0       ,	config.act_dim       ,	device=snake_case__       ,	dtype=torch.floataa )
										lowerCamelCase_	:							int									=torch.zeros(1       ,	0       ,	device=snake_case__       ,	dtype=torch.floataa )
										lowerCamelCase_	:							Tuple									=torch.tensor(0       ,	device=snake_case__       ,	dtype=torch.long ).reshape(1       ,	1 )

										for step in range(snake_case__ ):
																	lowerCamelCase_	:							str									=torch.cat([actions, torch.zeros(1       ,	1       ,	config.act_dim       ,	device=snake_case__ )]       ,	dim=1 )
																	lowerCamelCase_	:							Union[str, Any]									=torch.cat([rewards, torch.zeros(1       ,	1       ,	device=snake_case__ )]       ,	dim=1 )

																	lowerCamelCase_	:							Optional[int]									=torch.ones(1       ,	states.shape[1] ).to(dtype=torch.long       ,	device=states.device )

																	with torch.no_grad():
																								lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=model(
																								    states=snake_case__       ,	actions=snake_case__       ,	rewards=snake_case__       ,	returns_to_go=snake_case__       ,	timesteps=snake_case__       ,	attention_mask=snake_case__       ,	return_dict=snake_case__       ,	)

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

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



