


"""simple docstring"""
import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE					:       int							   =							16
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							32






def 						lowercase				(	_snake_case		: Optional[int]	)	->Optional[Any]:
	"""simple docstring"""





	return int(x / 2**20	)



class      _UpperCAmelCase					:






		'''simple docstring'''

		def __enter__(self   ):





			'''simple docstring'''

			gc.collect()
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
			__snake_case      :		List[Any]         =							torch.cuda.memory_allocated()
			__snake_case      :		str         =							psutil.Process()

			__snake_case      :		Dict         =							self.cpu_mem_used()
			__snake_case      :		Optional[int]         =							True
			__snake_case      :		Tuple         =							threading.Thread(target=self.peak_monitor_func   )
			__snake_case      :		Any         =							True
			peak_monitor_thread.start()
			return self



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.process.memory_info().rss



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							-1

			while True:
				__snake_case      :		Any         =							max(self.cpu_mem_used()							,			self.cpu_peak   )

				# can't sleep or will not catch the peak right (this comment is here on purpose)
				# time.sleep(0.001) # 1msec

				if not self.peak_monitoring:
					break



		def __exit__(self							,			*a_   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							False

			gc.collect()
			torch.cuda.empty_cache()
			__snake_case      :		int         =							torch.cuda.memory_allocated()
			__snake_case      :		Dict         =							torch.cuda.max_memory_allocated()
			__snake_case      :		Any         =							bamb(self.end - self.begin   )
			__snake_case      :		str         =							bamb(self.peak - self.begin   )

			__snake_case      :		int         =							self.cpu_mem_used()
			__snake_case      :		List[Any]         =							bamb(self.cpu_end - self.cpu_begin   )
			__snake_case      :		int         =							bamb(self.cpu_peak - self.cpu_begin   )
			# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			SCREAMING_SNAKE_CASE					:       List[str]							   =							mocked_dataloaders  # noqa: F811






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: str	)	->Tuple:
	"""simple docstring"""





	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,  _snake_case	) == "1":
		__snake_case      :		Tuple         =							2

	# New Code #
	# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
	__snake_case      :		str         =							FullyShardedDataParallelPlugin(
	    state_dict_config=FullStateDictConfig(offload_to_cpu=_snake_case	,  ranka_only=_snake_case	)	,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_snake_case	,  ranka_only=_snake_case	)	,  )

	# Initialize accelerator
	if args.with_tracking:
		__snake_case      :		Optional[int]         =							Accelerator(
		    cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with='''wandb'''	,  project_dir=args.logging_dir	,  fsdp_plugin=_snake_case	,  )
	else:
		__snake_case      :		List[Any]         =							Accelerator(fsdp_plugin=_snake_case	)
	accelerator.print(accelerator.distributed_type	)

	if hasattr(args.checkpointing_steps	,  '''isdigit'''	):
		if args.checkpointing_steps == "epoch":
			__snake_case      :		str         =							args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__snake_case      :		Tuple         =							int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
	else:
		__snake_case      :		List[Any]         =							None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case      :		List[Any]         =							config['''lr''']
	__snake_case      :		Dict         =							int(config['''num_epochs''']	)
	__snake_case      :		int         =							int(config['''seed''']	)
	__snake_case      :		List[str]         =							int(config['''batch_size''']	)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		__snake_case      :		List[Any]         =							vars(_snake_case	)
		accelerator.init_trackers('''fsdp_glue_no_trainer'''	,  _snake_case	)

	__snake_case      :		int         =							AutoTokenizer.from_pretrained(args.model_name_or_path	)
	__snake_case      :		Any         =							load_dataset('''glue'''	,  '''mrpc'''	)
	__snake_case      :		str         =							evaluate.load('''glue'''	,  '''mrpc'''	)

	def tokenize_function(_snake_case		: Optional[Any]	):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case      :		Optional[int]         =							tokenizer(examples['''sentence1''']	,  examples['''sentence2''']	,  truncation=_snake_case	,  max_length=_snake_case	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		__snake_case      :		int         =							datasets.map(
		    _snake_case	,  batched=_snake_case	,  remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,  )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	__snake_case      :		Optional[Any]         =							tokenized_datasets.rename_column('''label'''	,  '''labels'''	)

	# If the batch size is too big we use gradient accumulation
	__snake_case      :		int         =							1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__snake_case      :		str         =							batch_size // MAX_GPU_BATCH_SIZE
		__snake_case      :		Any         =							MAX_GPU_BATCH_SIZE

	def collate_fn(_snake_case		: int	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__snake_case      :		Union[str, Any]         =							128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__snake_case      :		Dict         =							16
		elif accelerator.mixed_precision != "no":
			__snake_case      :		str         =							8
		else:
			__snake_case      :		str         =							None

		return tokenizer.pad(
		    _snake_case	,  padding='''longest'''	,  max_length=_snake_case	,  pad_to_multiple_of=_snake_case	,  return_tensors='''pt'''	,  )

	# Instantiate dataloaders.
	__snake_case      :		Union[str, Any]         =							DataLoader(
	    tokenized_datasets['''train''']	,  shuffle=_snake_case	,  collate_fn=_snake_case	,  batch_size=_snake_case	)
	__snake_case      :		Any         =							DataLoader(
	    tokenized_datasets['''validation''']	,  shuffle=_snake_case	,  collate_fn=_snake_case	,  batch_size=_snake_case	)

	set_seed(_snake_case	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case      :		Optional[int]         =							AutoModelForSequenceClassification.from_pretrained(
	    args.model_name_or_path	,  return_dict=_snake_case	,  low_cpu_mem_usage=_snake_case	)

	# New Code #
	# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
	__snake_case      :		int         =							accelerator.prepare(_snake_case	)
	accelerator.print(_snake_case	)

	# Instantiate optimizer
	# New Code #
	# For FSDP feature, at present it doesn't support multiple parameter groups,
	# so we need to create a single parameter group for the whole model
	__snake_case      :		List[Any]         =							torch.optim.AdamW(params=model.parameters()	,  lr=_snake_case	,  weight_decay=2e-4	)

	# Instantiate scheduler
	__snake_case      :		Optional[Any]         =							get_linear_schedule_with_warmup(
	    optimizer=_snake_case	,  num_warmup_steps=10	,  num_training_steps=(len(_snake_case	) * num_epochs) // gradient_accumulation_steps	,  )

	# New Code #
	# For FSDP feature, prepare everything except the model as we have already prepared the model
	# before creating the optimizer
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	__snake_case      ,				__snake_case      ,				__snake_case      ,				__snake_case      :		List[Any]         =							accelerator.prepare(
	    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	)

	__snake_case      :		Optional[int]         =							0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
			accelerator.load_state(args.resume_from_checkpoint	)
			__snake_case      :		Dict         =							os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			__snake_case      :		str         =							[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			__snake_case      :		Optional[Any]         =							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		__snake_case      :		Union[str, Any]         =							os.path.splitext(_snake_case	)[0]

		if "epoch" in training_difference:
			num_epochs -= int(training_difference.replace('''epoch_'''	,  ''''''	)	)
			__snake_case      :		Optional[int]         =							None
		else:
			__snake_case      :		List[str]         =							int(training_difference.replace('''step_'''	,  ''''''	)	)
			num_epochs -= resume_step // len(_snake_case	)
			# If resuming by step, we also need to know exactly how far into the DataLoader we went
			__snake_case      :		int         =							(num_epochs * len(_snake_case	)) - resume_step

    # Now we train the model
	for epoch in range(_snake_case	):
		# New Code #
		# context manager to track the peak memory usage during the training epoch
		with TorchTracemalloc() as tracemalloc:
			model.train()
			if args.with_tracking:
				__snake_case      :		Union[str, Any]         =							0
			for step, batch in enumerate(_snake_case	):
				# We need to skip steps until we reach the resumed step
				if args.resume_from_checkpoint and epoch == 0:
					if resume_step is not None and step < resume_step:
						pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device	)
				__snake_case      :		Tuple         =							model(**_snake_case	)
				__snake_case      :		Dict         =							outputs.loss
				__snake_case      :		List[str]         =							loss / gradient_accumulation_steps
				# We keep track of the loss at each epoch
				if args.with_tracking:
					total_loss += loss.detach().float()
				accelerator.backward(_snake_case	)
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# accelerator.print(lr_scheduler.get_lr())

				overall_step += 1

				if isinstance(_snake_case	,  _snake_case	):
					__snake_case      :		List[Any]         =							f"""step_{overall_step}"""
					if overall_step % checkpointing_steps == 0:
						if args.output_dir is not None:
							__snake_case      :		str         =							os.path.join(args.output_dir	,  _snake_case	)
						accelerator.save_state(_snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin	)	)	)
		accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used	)	)
		accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked	)	)
		accelerator.print(
		    '''Total Peak Memory consumed during the train (max): {}'''.format(
		        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
		# Logging the peak memory usage of the GPU to the tracker
		if args.with_tracking:
			accelerator.log(
			    {
			        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
			    }	,  step=_snake_case	,  )

		# New Code #
		# context manager to track the peak memory usage during the evaluation
		with TorchTracemalloc() as tracemalloc:
			model.eval()
			for step, batch in enumerate(_snake_case	):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device	)
				with torch.no_grad():
					__snake_case      :		Dict         =							model(**_snake_case	)
				__snake_case      :		Optional[int]         =							outputs.logits.argmax(dim=-1	)
				__snake_case      ,				__snake_case      :		int         =							accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
				metric.add_batch(
				    predictions=_snake_case	,  references=_snake_case	,  )

			__snake_case      :		str         =							metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(f"""epoch {epoch}:"""	,  _snake_case	)
			if args.with_tracking:
				accelerator.log(
				    {
				        '''accuracy''': eval_metric['''accuracy'''],
				        '''f1''': eval_metric['''f1'''],
				        '''train_loss''': total_loss.item() / len(_snake_case	),
				    }	,  step=_snake_case	,  )

			if checkpointing_steps == "epoch":
				__snake_case      :		List[str]         =							f"""epoch_{epoch}"""
				if args.output_dir is not None:
					__snake_case      :		Tuple         =							os.path.join(args.output_dir	,  _snake_case	)
				accelerator.save_state(_snake_case	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin	)	)	)
		accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used	)	)
		accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked	)	)
		accelerator.print(
		    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
		# Logging the peak memory usage of the GPU to the tracker
		if args.with_tracking:
			accelerator.log(
			    {
			        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
			    }	,  step=_snake_case	,  )

	if args.with_tracking:
		accelerator.end_training()






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		str         =							argparse.ArgumentParser(description='''Simple example of training script.'''	)
	parser.add_argument(
	    '''--mixed_precision'''	,  type=_snake_case	,  default=_snake_case	,  choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,  help='''Whether to use mixed precision. Choose'''
	    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
	    '''and an Nvidia Ampere GPU.'''	,  )
	parser.add_argument('''--cpu'''	,  action='''store_true'''	,  help='''If passed, will train on the CPU.'''	)
	parser.add_argument(
	    '''--checkpointing_steps'''	,  type=_snake_case	,  default=_snake_case	,  help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,  )
	parser.add_argument(
	    '''--resume_from_checkpoint'''	,  type=_snake_case	,  default=_snake_case	,  help='''If the training should continue from a checkpoint folder.'''	,  )
	parser.add_argument(
	    '''--with_tracking'''	,  action='''store_true'''	,  help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,  )
	parser.add_argument(
	    '''--output_dir'''	,  type=_snake_case	,  default='''.'''	,  help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,  )
	parser.add_argument(
	    '''--logging_dir'''	,  type=_snake_case	,  default='''logs'''	,  help='''Location on where to store experiment tracking logs`'''	,  )
	parser.add_argument(
	    '''--model_name_or_path'''	,  type=_snake_case	,  help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,  required=_snake_case	,  )
	__snake_case      :		Union[str, Any]         =							parser.parse_args()
	__snake_case      :		List[str]         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			main()









"""simple docstring"""
import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.






def 						lowercase				(	_snake_case		: Tuple	,  _snake_case		: Tuple	,  _snake_case		: Optional[Any]	,  _snake_case		: str	,  _snake_case		: int	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Dict         =							states.reshape(1	,  -1	,  model.config.state_dim	)
	__snake_case      :		List[str]         =							actions.reshape(1	,  -1	,  model.config.act_dim	)
	__snake_case      :		Union[str, Any]         =							returns_to_go.reshape(1	,  -1	,  1	)
	__snake_case      :		Any         =							timesteps.reshape(1	,  -1	)

	if model.config.max_length is not None:
		__snake_case      :		Union[str, Any]         =							states[:, -model.config.max_length :]
		__snake_case      :		Any         =							actions[:, -model.config.max_length :]
		__snake_case      :		Tuple         =							returns_to_go[:, -model.config.max_length :]
		__snake_case      :		Union[str, Any]         =							timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		__snake_case      :		Dict         =							torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
		__snake_case      :		str         =							attention_mask.to(dtype=torch.long	,  device=states.device	).reshape(1	,  -1	)
		__snake_case      :		Dict         =							torch.cat(
		    [
		        torch.zeros(
		            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)	,  device=states.device	,  ),
		        states,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Tuple         =							torch.cat(
		    [
		        torch.zeros(
		            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)	,  device=actions.device	,  ),
		        actions,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		str         =							torch.cat(
		    [
		        torch.zeros(
		            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)	,  device=returns_to_go.device	,  ),
		        returns_to_go,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Union[str, Any]         =							torch.cat(
		    [
		        torch.zeros(
		            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])	,  device=timesteps.device	),
		        timesteps,
		    ]	,  dim=1	,  ).to(dtype=torch.long	)
	else:
		__snake_case      :		int         =							None

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							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	,  )

	return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE					:       List[Any]							   =							DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							model.to(device)
model.eval()

for ep in range(10):
			SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       str							   =							0, 0
			SCREAMING_SNAKE_CASE					:       int							   =							env.reset()
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			SCREAMING_SNAKE_CASE					:       str							   =							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.zeros(0, device=device, dtype=torch.floataa)

			SCREAMING_SNAKE_CASE					:       int							   =							torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
			for t in range(max_ep_len):
						env.render()
						# add padding
						SCREAMING_SNAKE_CASE					:       str							   =							torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([rewards, torch.zeros(1, device=device)])

						SCREAMING_SNAKE_CASE					:       Dict							   =							get_action(
						    model,
						    (states.to(dtype=torch.floataa) - state_mean) / state_std,
						    actions.to(dtype=torch.floataa),
						    rewards.to(dtype=torch.floataa),
						    target_return.to(dtype=torch.floataa),
						    timesteps.to(dtype=torch.long),
						)
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

						SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       List[str]							   =							env.step(action)

						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
						SCREAMING_SNAKE_CASE					:       int							   =							torch.cat([states, cur_state], dim=0)
						SCREAMING_SNAKE_CASE					:       List[str]							   =							reward

						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							target_return[0, -1] - (reward / scale)
						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

						episode_return += reward
						episode_length += 1

						if done:
									break





