

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


lowercase_     =       1_6
lowercase_     =       3_2
def 					a				(      A__     :       Optional[int]					)   ->				Any:





      """simple docstring"""



      return int(x / 2**20					)





class 			__lowerCAmelCase					:




  def __enter__(    self )  ->			Tuple:
        '''simple docstring'''






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

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




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return self.process.memory_info().rss




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=-1

        while True:
              _lowercase     	=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,       *lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=False

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

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

  lowercase_     =       mocked_dataloaders  # noqa: F811
def 					a				(      A__     :       Optional[int]				,       A__     :       int					)   ->				Optional[int]:





      """simple docstring"""



      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,       A__					) == "1":
            _lowercase     	=2

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

      # Initialize accelerator
      if args.with_tracking:
            _lowercase     	=Accelerator(
                cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with='wandb'				,       project_dir=args.logging_dir				,       fsdp_plugin=A__				,       )
      else:
            _lowercase     	=Accelerator(fsdp_plugin=A__					)
      accelerator.print(accelerator.distributed_type					)

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

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

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

      def tokenize_function(A__     :       Any					):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase     	=tokenizer(examples['sentence1']				,       examples['sentence2']				,       truncation=A__				,       max_length=A__					)
            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():
            _lowercase     	=datasets.map(
                A__				,       batched=A__				,       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
      _lowercase     	=tokenized_datasets.rename_column('label'				,       'labels'					)

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

      def collate_fn(A__     :       List[Any]					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            _lowercase     	=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":
                  _lowercase     	=16
            elif accelerator.mixed_precision != "no":
                  _lowercase     	=8
            else:
                  _lowercase     	=None

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

      # Instantiate dataloaders.
      _lowercase     	=DataLoader(
          tokenized_datasets['train']				,       shuffle=A__				,       collate_fn=A__				,       batch_size=A__					)
      _lowercase     	=DataLoader(
          tokenized_datasets['validation']				,       shuffle=A__				,       collate_fn=A__				,       batch_size=A__					)

      set_seed(A__					)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowercase     	=AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path				,       return_dict=A__				,       low_cpu_mem_usage=A__					)

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

      # 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
      _lowercase     	=torch.optim.AdamW(params=model.parameters()				,       lr=A__				,       weight_decay=2e-4					)

      # Instantiate scheduler
      _lowercase     	=get_linear_schedule_with_warmup(
          optimizer=A__				,       num_warmup_steps=10				,       num_training_steps=(len(A__					) * 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.
      _lowercase	,	_lowercase	,	_lowercase	,	_lowercase     	=accelerator.prepare(
          A__				,       A__				,       A__				,       A__					)

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

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

    # Now we train the model
      for epoch in range(A__					):
            # New Code #
            # context manager to track the peak memory usage during the training epoch
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  if args.with_tracking:
                        _lowercase     	=0
                  for step, batch in enumerate(A__					):
                        # 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					)
                        _lowercase     	=model(**A__					)
                        _lowercase     	=outputs.loss
                        _lowercase     	=loss / gradient_accumulation_steps
                        # We keep track of the loss at each epoch
                        if args.with_tracking:
                              total_loss += loss.detach().float()
                        accelerator.backward(A__					)
                        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(A__				,       A__					):
                              _lowercase     	=F'''step_{overall_step}'''
                              if overall_step % checkpointing_steps == 0:
                                    if args.output_dir is not None:
                                          _lowercase     	=os.path.join(args.output_dir				,       A__					)
                                    accelerator.save_state(A__					)
        # 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=A__				,       )

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

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

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

      if args.with_tracking:
            accelerator.end_training()
def 					a				(      )   ->				List[str]:





      """simple docstring"""



      _lowercase     	=argparse.ArgumentParser(description='Simple example of training script.'					)
      parser.add_argument(
          '--mixed_precision'				,       type=A__				,       default=A__				,       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=A__				,       default=A__				,       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=A__				,       default=A__				,       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=A__				,       default='.'				,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'				,       )
      parser.add_argument(
          '--logging_dir'				,       type=A__				,       default='logs'				,       help='Location on where to store experiment tracking logs`'				,       )
      parser.add_argument(
          '--model_name_or_path'				,       type=A__				,       help='Path to pretrained model or model identifier from huggingface.co/models.'				,       required=A__				,       )
      _lowercase     	=parser.parse_args()
      _lowercase     	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(A__				,       A__					)


if __name__ == "__main__":
  main()





import os
import sys

from . import rsa_key_generator as rkg

lowercase_     =       1_2_8
lowercase_     =       2_5_6
def 					a				(      A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=message.encode('ascii'					)

      _lowercase     	=[]
      for block_start in range(0				,       len(A__					)				,       A__					):
            _lowercase     	=0
            for i in range(A__				,       min(block_start + block_size				,       len(A__					)					)					):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(A__					)
      return block_ints
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      for block_int in block_ints:
            _lowercase     	=[]
            for i in range(block_size - 1				,       -1				,       -1					):
                  if len(A__					) + i < message_length:
                        _lowercase     	=block_int // (BYTE_SIZE**i)
                        _lowercase     	=block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,       chr(A__					)					)
            message.extend(A__					)
      return "".join(A__					)
def 					a				(      A__     :       str				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key

      for block in get_blocks_from_text(A__				,       A__					):
            encrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return encrypted_blocks
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return get_text_from_blocks(A__				,       A__				,       A__					)
def 					a				(      A__     :       str					)   ->				tuple[int, int, int]:





      """simple docstring"""



      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split(','					)
      return (int(A__					), int(A__					), int(A__					))
def 					a				(      A__     :       str				,       A__     :       str				,       A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      if key_size < block_size * 8:
            sys.exit(
                'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                'requires the block size to be equal to or greater than the key size. '
                'Either decrease the block size or use different keys.'.format(
                    block_size * 8				,       A__					)					)

      _lowercase     	=[str(A__					) for i in encrypt_message(A__				,       (n, e)				,       A__					)]

      _lowercase     	=','.join(A__					)
      _lowercase     	=F'''{len(A__					)}_{block_size}_{encrypted_content}'''
      with open(A__				,       'w'					) as fo:
            fo.write(A__					)
      return encrypted_content
def 					a				(      A__     :       str				,       A__     :       str					)   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split('_'					)
      _lowercase     	=int(A__					)
      _lowercase     	=int(A__					)

      if key_size < block_size * 8:
            sys.exit(
                'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                'requires the block size to be equal to or greater than the key size. '
                'Did you specify the correct key file and encrypted file?'.format(
                    block_size * 8				,       A__					)					)

      _lowercase     	=[]
      for block in encrypted_message.split(','					):
            encrypted_blocks.append(int(A__					)					)

      return decrypt_message(A__				,       A__				,       (n, d)				,       A__					)
def 					a				(      )   ->				None:





      """simple docstring"""



      _lowercase     	='encrypted_file.txt'
      _lowercase     	=input(r'Encrypt\Decrypt [e\d]: '					)

      if response.lower().startswith('e'					):
            _lowercase     	='encrypt'
      elif response.lower().startswith('d'					):
            _lowercase     	='decrypt'

      if mode == "encrypt":
            if not os.path.exists('rsa_pubkey.txt'					):
                  rkg.make_key_files('rsa'				,       1024					)

            _lowercase     	=input('\nEnter message: '					)
            _lowercase     	='rsa_pubkey.txt'
            print(F'''Encrypting and writing to {filename}...'''					)
            _lowercase     	=encrypt_and_write_to_file(A__				,       A__				,       A__					)

            print('\nEncrypted text:'					)
            print(A__					)

      elif mode == "decrypt":
            _lowercase     	='rsa_privkey.txt'
            print(F'''Reading from {filename} and decrypting...'''					)
            _lowercase     	=read_from_file_and_decrypt(A__				,       A__					)
            print('writing decryption to rsa_decryption.txt...'					)
            with open('rsa_decryption.txt'				,       'w'					) as dec:
                  dec.write(A__					)

            print('\nDecryption:'					)
            print(A__					)


if __name__ == "__main__":
  main()


