






"""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
#
########################################################################


a  :						List[Any]					       =							16
a  :						List[Any]					       =							32




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->List[str]:
 return int(x / 2**2_0			)


class 	_UpperCamelCase :



    '''simple docstring'''


    def __enter__(      self			):
     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 		A__      (      self			):
     return self.process.memory_info().rss


    def 		A__      (      self			):
     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   ,			*__lowercase			):
     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

 a  :						Union[str, Any]					       =							mocked_dataloaders  # noqa: F811




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Optional[int]:







 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,	lowerCAmelCase_			) == "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=lowerCAmelCase_       ,	ranka_only=lowerCAmelCase_			)       ,	optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase_       ,	ranka_only=lowerCAmelCase_			)       ,	)

 # 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=lowerCAmelCase_       ,	)
 else:
  UpperCAmelCase__ 						=				Accelerator(fsdp_plugin=lowerCAmelCase_			)
 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(lowerCAmelCase_			)
  accelerator.init_trackers("""fsdp_glue_no_trainer"""       ,	lowerCAmelCase_			)

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

 def tokenize_function(_SCREAMING_SNAKE_CASE			):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase__ 						=				tokenizer(examples["""sentence1"""]       ,	examples["""sentence2"""]       ,	truncation=lowerCAmelCase_       ,	max_length=lowerCAmelCase_			)
  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(
      lowerCAmelCase_       ,	batched=lowerCAmelCase_       ,	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(_SCREAMING_SNAKE_CASE			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCAmelCase__ 						=				1_2_8 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__ 						=				1_6
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase__ 						=				8
  else:
   UpperCAmelCase__ 						=				None

  return tokenizer.pad(
      lowerCAmelCase_       ,	padding="""longest"""       ,	max_length=lowerCAmelCase_       ,	pad_to_multiple_of=lowerCAmelCase_       ,	return_tensors="""pt"""       ,	)

 # Instantiate dataloaders.
 UpperCAmelCase__ 						=				DataLoader(
     tokenized_datasets["""train"""]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_			)
 UpperCAmelCase__ 						=				DataLoader(
     tokenized_datasets["""validation"""]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_			)

 set_seed(lowerCAmelCase_			)

 # 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=lowerCAmelCase_       ,	low_cpu_mem_usage=lowerCAmelCase_			)

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

 # 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=lowerCAmelCase_       ,	weight_decay=2E-4			)

 # Instantiate scheduler
 UpperCAmelCase__ 						=				get_linear_schedule_with_warmup(
     optimizer=lowerCAmelCase_       ,	num_warmup_steps=1_0       ,	num_training_steps=(len(lowerCAmelCase_			) * 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(
     lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_			)

 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(lowerCAmelCase_			)[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(lowerCAmelCase_			)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   UpperCAmelCase__ 						=				(num_epochs * len(lowerCAmelCase_			)) - resume_step

    # Now we train the model
 for epoch in range(lowerCAmelCase_			):
  # 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(lowerCAmelCase_			):
    # 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(**lowerCAmelCase_			)
    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(lowerCAmelCase_			)
    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(lowerCAmelCase_       ,	lowerCAmelCase_			):
     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       ,	lowerCAmelCase_			)
      accelerator.save_state(lowerCAmelCase_			)
        # 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=lowerCAmelCase_       ,	)

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

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

   if checkpointing_steps == "epoch":
    UpperCAmelCase__ 						=				F'''epoch_{epoch}'''
    if args.output_dir is not None:
     UpperCAmelCase__ 						=				os.path.join(args.output_dir       ,	lowerCAmelCase_			)
    accelerator.save_state(lowerCAmelCase_			)
        # 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=lowerCAmelCase_       ,	)

 if args.with_tracking:
  accelerator.end_training()




def       snake_case__		(   )    ->Optional[int]:
 UpperCAmelCase__ 						=				argparse.ArgumentParser(description="""Simple example of training script."""			)
 parser.add_argument(
     """--mixed_precision"""       ,	type=lowerCAmelCase_       ,	default=lowerCAmelCase_       ,	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=lowerCAmelCase_       ,	default=lowerCAmelCase_       ,	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=lowerCAmelCase_       ,	default=lowerCAmelCase_       ,	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=lowerCAmelCase_       ,	default="""."""       ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,	)
 parser.add_argument(
     """--logging_dir"""       ,	type=lowerCAmelCase_       ,	default="""logs"""       ,	help="""Location on where to store experiment tracking logs`"""       ,	)
 parser.add_argument(
     """--model_name_or_path"""       ,	type=lowerCAmelCase_       ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,	required=lowerCAmelCase_       ,	)
 UpperCAmelCase__ 						=				parser.parse_args()
 UpperCAmelCase__ 						=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
 training_function(lowerCAmelCase_       ,	lowerCAmelCase_			)


if __name__ == "__main__":
 main()






from __future__ import annotations

_lowerCamelCase							={1: 1}







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     if n in COLLATZ_SEQUENCE_LENGTHS:
          return COLLATZ_SEQUENCE_LENGTHS[n]
     SCREAMING_SNAKE_CASE 		=n // 2 if n % 2 == 0 else 3 * n + 1
     SCREAMING_SNAKE_CASE 		=collatz_sequence_length(lowerCAmelCase_     ) + 1
     SCREAMING_SNAKE_CASE 		=sequence_length
     return sequence_length







def      snake_case__							(  lowerCAmelCase_ = 1000000     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=max((collatz_sequence_length(lowerCAmelCase_     ), i) for i in range(1,       lowerCAmelCase_     )     )
     return result[1]


if __name__ == "__main__":
 print(solution(int(input().strip())))

