


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase       =							logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default='''cifar10'''     ,	metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The column name of the images in the files.'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the training data.'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the validation data.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=0.15     ,	metadata={'''help''': '''Percent to split off of train for validation.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }     ,	)
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }     ,	)

							def       lowerCamelCase_ (							self:	int							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      {}
									if self.train_dir is not None:
											lowercase__            =      self.train_dir
									if self.validation_dir is not None:
											lowercase__            =      self.validation_dir
									lowercase__            =      data_files if data_files else None


@dataclass
class        _a							:
							_lowercase	:			str        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''Override some existing default config settings when a model is trained from scratch. Example: '''
							            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}			)
							_lowercase	:			str        = field(
							    default='''main'''     ,	metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}     ,	)
							_lowercase	:			str        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''Name or path of preprocessor config.'''}			)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }     ,	)
							_lowercase	:			float        = field(
							    default=0.75     ,	metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}			)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}			)


@dataclass
class        _a							(			UpperCamelCase__			):
							_lowercase	:			float        = field(
							    default=1e-3     ,	metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}			)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      torch.stack([example['''pixel_values'''] for example in examples]     )
		return {"pixel_values": pixel_values}
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)     )
		if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('''run_mae'''						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,				datefmt='''%m/%d/%Y %H:%M:%S'''						,				handlers=[logging.StreamHandler(sys.stdout     )]						,				)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		lowercase__            =      training_args.get_process_log_level()
		logger.setLevel(SCREAMING_SNAKE_CASE     )
		transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE     )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
		    + f'distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'     )
		logger.info(f'Training/evaluation parameters {training_args}'     )

		# Detecting last checkpoint.
		lowercase__            =      None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				lowercase__            =      get_last_checkpoint(training_args.output_dir     )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
						raise ValueError(
						    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
						    '''Use --overwrite_output_dir to overcome.'''     )
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
						    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''     )

    # Initialize our dataset.
		lowercase__            =      load_dataset(
		    data_args.dataset_name						,				data_args.dataset_config_name						,				data_files=data_args.data_files						,				cache_dir=model_args.cache_dir						,				use_auth_token=True if model_args.use_auth_token else None						,				)

		# If we don't have a validation split, split off a percentage of train as validation.
		lowercase__            =      None if '''validation''' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split						,				SCREAMING_SNAKE_CASE     ) and data_args.train_val_split > 0.0:
				lowercase__            =      ds['''train'''].train_test_split(data_args.train_val_split     )
				lowercase__            =      split['''train''']
				lowercase__            =      split['''test''']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowercase__            =      {
		    '''cache_dir''': model_args.cache_dir,
		    '''revision''': model_args.model_revision,
		    '''use_auth_token''': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				lowercase__            =      ViTMAEConfig.from_pretrained(model_args.config_name						,				**SCREAMING_SNAKE_CASE     )
		elif model_args.model_name_or_path:
				lowercase__            =      ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,				**SCREAMING_SNAKE_CASE     )
		else:
				lowercase__            =      ViTMAEConfig()
				logger.warning('''You are instantiating a new config instance from scratch.'''     )
				if model_args.config_overrides is not None:
						logger.info(f'Overriding config: {model_args.config_overrides}'     )
						config.update_from_string(model_args.config_overrides     )
						logger.info(f'New config: {config}'     )

    # adapt config
		config.update(
		    {
		        '''mask_ratio''': model_args.mask_ratio,
		        '''norm_pix_loss''': model_args.norm_pix_loss,
		    }     )

		# create image processor
		if model_args.image_processor_name:
				lowercase__            =      ViTImageProcessor.from_pretrained(model_args.image_processor_name						,				**SCREAMING_SNAKE_CASE     )
		elif model_args.model_name_or_path:
				lowercase__            =      ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,				**SCREAMING_SNAKE_CASE     )
		else:
				lowercase__            =      ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				lowercase__            =      ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path						,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )						,				config=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir						,				revision=model_args.model_revision						,				use_auth_token=True if model_args.use_auth_token else None						,				)
		else:
				logger.info('''Training new model from scratch'''     )
				lowercase__            =      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE     )

		if training_args.do_train:
				lowercase__            =      ds['''train'''].column_names
		else:
				lowercase__            =      ds['''validation'''].column_names

		if data_args.image_column_name is not None:
				lowercase__            =      data_args.image_column_name
		elif "image" in column_names:
				lowercase__            =      '''image'''
		elif "img" in column_names:
				lowercase__            =      '''img'''
		else:
				lowercase__            =      column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				lowercase__            =      image_processor.size['''shortest_edge''']
		else:
				lowercase__            =      (image_processor.size['''height'''], image_processor.size['''width'''])
		lowercase__            =      Compose(
		    [
		        Lambda(lambda SCREAMING_SNAKE_CASE     : img.convert('''RGB'''     ) if img.mode != "RGB" else img     ),
		        RandomResizedCrop(SCREAMING_SNAKE_CASE						,				scale=(0.2, 1.0)						,				interpolation=InterpolationMode.BICUBIC     ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean						,				std=image_processor.image_std     ),
		    ]     )

		def preprocess_images(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [transforms(SCREAMING_SNAKE_CASE     ) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('''--do_train requires a train dataset'''     )
				if data_args.max_train_samples is not None:
						lowercase__            =      ds['''train'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
				# Set the training transforms
				ds["train"].set_transform(SCREAMING_SNAKE_CASE     )

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('''--do_eval requires a validation dataset'''     )
				if data_args.max_eval_samples is not None:
						lowercase__            =      (
						    ds['''validation'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
						)
				# Set the validation transforms
				ds["validation"].set_transform(SCREAMING_SNAKE_CASE     )

		# Compute absolute learning rate
		lowercase__            =      (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				lowercase__            =      training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		lowercase__            =      Trainer(
		    model=SCREAMING_SNAKE_CASE						,				args=SCREAMING_SNAKE_CASE						,				train_dataset=ds['''train'''] if training_args.do_train else None						,				eval_dataset=ds['''validation'''] if training_args.do_eval else None						,				tokenizer=SCREAMING_SNAKE_CASE						,				data_collator=SCREAMING_SNAKE_CASE						,				)

		# Training
		if training_args.do_train:
				lowercase__            =      None
				if training_args.resume_from_checkpoint is not None:
						lowercase__            =      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowercase__            =      last_checkpoint
				lowercase__            =      trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE     )
				trainer.save_model()
				trainer.log_metrics('''train'''						,				train_result.metrics     )
				trainer.save_metrics('''train'''						,				train_result.metrics     )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				lowercase__            =      trainer.evaluate()
				trainer.log_metrics('''eval'''						,				SCREAMING_SNAKE_CASE     )
				trainer.save_metrics('''eval'''						,				SCREAMING_SNAKE_CASE     )

		# Write model card and (optionally) push to hub
		lowercase__            =      {
		    '''tasks''': '''masked-auto-encoding''',
		    '''dataset''': data_args.dataset_name,
		    '''tags''': ['''masked-auto-encoding'''],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**SCREAMING_SNAKE_CASE     )
		else:
				trainer.create_model_card(**SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()






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							:

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





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







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

									lowercase__            =      self.get_config()

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

							def       lowerCamelCase_ (							self:	Dict							)			->   List[Any]:





									"""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       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int    ,						)			->   Dict:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModel(config=UpperCamelCase_							)
									model.to(UpperCamelCase_							)
									model.eval()
									lowercase__            =      model(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

									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       lowerCamelCase_ (							self:	List[Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		
									)            =      config_and_inputs
									lowercase__            =      {
									    '''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							(			UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = (DecisionTransformerModel,) if is_torch_available() else ()
							_lowercase	:			List[str]        = ()
							_lowercase	:			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
							_lowercase	:			Any        = False

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

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





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						hidden_size=37							)

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





									"""simple docstring"""







									self.config_tester.run_common_tests()

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





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

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





									"""simple docstring"""







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


							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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


@require_torch
class        _a							(			unittest.TestCase			):

							@slow
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







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

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

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

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

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

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

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

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


