

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
#
########################################################################


_UpperCamelCase     =    16
_UpperCamelCase     =    32
def 		UpperCamelCase_(    snake_case__:						Dict     ) ->				str:
 return int(x / 2**20     )



class 					lowercase		:



 '''simple docstring'''


 def __enter__(self				)				->      int:






  """simple docstring"""





  gc.collect()
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  UpperCAmelCase__           =       torch.cuda.memory_allocated()
  UpperCAmelCase__           =       psutil.Process()

  UpperCAmelCase__           =       self.cpu_mem_used()
  UpperCAmelCase__           =       True
  UpperCAmelCase__           =       threading.Thread(target=self.peak_monitor_func				)
  UpperCAmelCase__           =       True
  peak_monitor_thread.start()
  return self




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






  """simple docstring"""





  return self.process.memory_info().rss




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       -1

  while True:
   UpperCAmelCase__           =       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				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       False

  gc.collect()
  torch.cuda.empty_cache()
  UpperCAmelCase__           =       torch.cuda.memory_allocated()
  UpperCAmelCase__           =       torch.cuda.max_memory_allocated()
  UpperCAmelCase__           =       bamb(self.end - self.begin				)
  UpperCAmelCase__           =       bamb(self.peak - self.begin				)

  UpperCAmelCase__           =       self.cpu_mem_used()
  UpperCAmelCase__           =       bamb(self.cpu_end - self.cpu_begin				)
  UpperCAmelCase__           =       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

 _UpperCamelCase     =    mocked_dataloaders  # noqa: F811
def 		UpperCamelCase_(    snake_case__:						Any						,					snake_case__:						Any     ) ->				Dict:
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'						,					snake_case__     ) == "1":
  UpperCAmelCase__           =       2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 UpperCAmelCase__           =       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:
  UpperCAmelCase__           =       Accelerator(
      cpu=args.cpu						,					mixed_precision=args.mixed_precision						,					log_with='wandb'						,					project_dir=args.logging_dir						,					fsdp_plugin=snake_case__						,					)
 else:
  UpperCAmelCase__           =       Accelerator(fsdp_plugin=snake_case__     )
 accelerator.print(accelerator.distributed_type     )

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

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  UpperCAmelCase__           =       vars(snake_case__     )
  accelerator.init_trackers('fsdp_glue_no_trainer'						,					snake_case__     )

 UpperCAmelCase__           =       AutoTokenizer.from_pretrained(args.model_name_or_path     )
 UpperCAmelCase__           =       load_dataset('glue'						,					'mrpc'     )
 UpperCAmelCase__           =       evaluate.load('glue'						,					'mrpc'     )

 def tokenize_function(snake_case__:						Optional[int]     ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase__           =       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():
  UpperCAmelCase__           =       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
 UpperCAmelCase__           =       tokenized_datasets.rename_column('label'						,					'labels'     )

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

 def collate_fn(snake_case__:						Dict     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCAmelCase__           =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   UpperCAmelCase__           =       16
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase__           =       8
  else:
   UpperCAmelCase__           =       None

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

 # Instantiate dataloaders.
 UpperCAmelCase__           =       DataLoader(
     tokenized_datasets['train']						,					shuffle=snake_case__						,					collate_fn=snake_case__						,					batch_size=snake_case__     )
 UpperCAmelCase__           =       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)
 UpperCAmelCase__           =       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
 UpperCAmelCase__           =       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
 UpperCAmelCase__           =       torch.optim.AdamW(params=model.parameters()						,					lr=snake_case__						,					weight_decay=2e-4     )

 # Instantiate scheduler
 UpperCAmelCase__           =       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.
 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       accelerator.prepare(
     snake_case__						,					snake_case__						,					snake_case__						,					snake_case__     )

 UpperCAmelCase__           =       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     )
   UpperCAmelCase__           =       os.path.basename(args.resume_from_checkpoint     )
  else:
   # Get the most recent checkpoint
   UpperCAmelCase__           =       [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
   dirs.sort(key=os.path.getctime     )
   UpperCAmelCase__           =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  UpperCAmelCase__           =       os.path.splitext(snake_case__     )[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace('epoch_'						,					''     )     )
   UpperCAmelCase__           =       None
  else:
   UpperCAmelCase__           =       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
   UpperCAmelCase__           =       (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:
    UpperCAmelCase__           =       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     )
    UpperCAmelCase__           =       model(**snake_case__     )
    UpperCAmelCase__           =       outputs.loss
    UpperCAmelCase__           =       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__     ):
     UpperCAmelCase__           =       f"step_{overall_step}"
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       UpperCAmelCase__           =       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():
     UpperCAmelCase__           =       model(**snake_case__     )
    UpperCAmelCase__           =       outputs.logits.argmax(dim=-1     )
    UpperCAmelCase__       ,					UpperCAmelCase__           =       accelerator.gather_for_metrics((predictions, batch['labels'])     )
    metric.add_batch(
        predictions=snake_case__						,					references=snake_case__						,					)

   UpperCAmelCase__           =       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":
    UpperCAmelCase__           =       f"epoch_{epoch}"
    if args.output_dir is not None:
     UpperCAmelCase__           =       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 		UpperCamelCase_(    ) ->				List[str]:
 UpperCAmelCase__           =       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__						,					)
 UpperCAmelCase__           =       parser.parse_args()
 UpperCAmelCase__           =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(snake_case__						,					snake_case__     )


if __name__ == "__main__":
 main()






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 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

 UpperCAmelCase__           =       states.reshape(1						,					-1						,					model.config.state_dim     )
 UpperCAmelCase__           =       actions.reshape(1						,					-1						,					model.config.act_dim     )
 UpperCAmelCase__           =       returns_to_go.reshape(1						,					-1						,					1     )
 UpperCAmelCase__           =       timesteps.reshape(1						,					-1     )

 if model.config.max_length is not None:
  UpperCAmelCase__           =       states[:, -model.config.max_length :]
  UpperCAmelCase__           =       actions[:, -model.config.max_length :]
  UpperCAmelCase__           =       returns_to_go[:, -model.config.max_length :]
  UpperCAmelCase__           =       timesteps[:, -model.config.max_length :]

  # pad all tokens to sequence length
  UpperCAmelCase__           =       torch.cat(
      [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
  UpperCAmelCase__           =       attention_mask.to(dtype=torch.long						,					device=states.device     ).reshape(1						,					-1     )
  UpperCAmelCase__           =       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     )
  UpperCAmelCase__           =       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     )
  UpperCAmelCase__           =       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     )
  UpperCAmelCase__           =       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:
  UpperCAmelCase__           =       None

 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       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

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase     =    np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_UpperCamelCase     =    np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_UpperCamelCase     =    torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase     =    torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_UpperCamelCase     =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_UpperCamelCase     =    model.to(device)
model.eval()

for ep in range(10):
 _UpperCamelCase ,       _UpperCamelCase     =    0, 0
 _UpperCamelCase     =    env.reset()
 _UpperCamelCase     =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _UpperCamelCase     =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros(0, device=device, dtype=torch.floataa)

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

  _UpperCamelCase     =    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),
  )
  _UpperCamelCase     =    action
  _UpperCamelCase     =    action.detach().cpu().numpy()

  _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     =    env.step(action)

  _UpperCamelCase     =    torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _UpperCamelCase     =    torch.cat([states, cur_state], dim=0)
  _UpperCamelCase     =    reward

  _UpperCamelCase     =    target_return[0, -1] - (reward / scale)
  _UpperCamelCase     =    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _UpperCamelCase     =    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



