


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


lowerCamelCase						 =	16
lowerCamelCase						 =	32




def      a_				(   SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





       return int(x / 2**20					)




class A  :






      def __enter__( self						:					str			) ->						List[Any]:
             """simple docstring"""




             gc.collect()
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
             _lowerCamelCase	:		Optional[int]          =torch.cuda.memory_allocated()
             _lowerCamelCase	:		int          =psutil.Process()

             _lowerCamelCase	:		Union[str, Any]          =self.cpu_mem_used()
             _lowerCamelCase	:		str          =True
             _lowerCamelCase	:		Tuple          =threading.Thread(target=self.peak_monitor_func			)
             _lowerCamelCase	:		Any          =True
             peak_monitor_thread.start()
             return self






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Tuple:
             """simple docstring"""




             return self.process.memory_info().rss






      def 	lowerCamelCase	( self						:					str			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		int          =-1

             while True:
                    _lowerCamelCase	:		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						:					int      ,       *lowercase_						:					Any			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		str          =False

             gc.collect()
             torch.cuda.empty_cache()
             _lowerCamelCase	:		List[Any]          =torch.cuda.memory_allocated()
             _lowerCamelCase	:		List[str]          =torch.cuda.max_memory_allocated()
             _lowerCamelCase	:		Optional[Any]          =bamb(self.end - self.begin			)
             _lowerCamelCase	:		Optional[Any]          =bamb(self.peak - self.begin			)

             _lowerCamelCase	:		Any          =self.cpu_mem_used()
             _lowerCamelCase	:		List[Any]          =bamb(self.cpu_end - self.cpu_begin			)
             _lowerCamelCase	:		List[str]          =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

 lowerCamelCase						 =	mocked_dataloaders  # noqa: F811




def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       if os.environ.get('TESTING_MOCKED_DATALOADERS'		,      _A					) == "1":
              _lowerCamelCase	:		Any          =2

       # New Code #
       # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
       _lowerCamelCase	:		List[str]          =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:
              _lowerCamelCase	:		Optional[int]          =Accelerator(
                  cpu=args.cpu		,      mixed_precision=args.mixed_precision		,      log_with='wandb'		,      project_dir=args.logging_dir		,      fsdp_plugin=_A		,      )
       else:
              _lowerCamelCase	:		str          =Accelerator(fsdp_plugin=_A					)
       accelerator.print(accelerator.distributed_type					)

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

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

       _lowerCamelCase	:		List[Any]          =AutoTokenizer.from_pretrained(args.model_name_or_path					)
       _lowerCamelCase	:		Optional[Any]          =load_dataset('glue'		,      'mrpc'					)
       _lowerCamelCase	:		List[Any]          =evaluate.load('glue'		,      'mrpc'					)

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

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

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

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

       # Instantiate dataloaders.
       _lowerCamelCase	:		Optional[int]          =DataLoader(
           tokenized_datasets['train']		,      shuffle=_A		,      collate_fn=_A		,      batch_size=_A					)
       _lowerCamelCase	:		Dict          =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)
       _lowerCamelCase	:		Optional[int]          =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
       _lowerCamelCase	:		Tuple          =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
       _lowerCamelCase	:		List[str]          =torch.optim.AdamW(params=model.parameters()		,      lr=_A		,      weight_decay=2e-4					)

       # Instantiate scheduler
       _lowerCamelCase	:		Optional[int]          =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.
       _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Any          =accelerator.prepare(
           _A		,      _A		,      _A		,      _A					)

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

              if "epoch" in training_difference:
                     num_epochs -= int(training_difference.replace('epoch_'		,      ''					)					)
                     _lowerCamelCase	:		List[str]          =None
              else:
                     _lowerCamelCase	:		str          =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
                     _lowerCamelCase	:		List[Any]          =(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:
                            _lowerCamelCase	:		Union[str, Any]          =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					)
                            _lowerCamelCase	:		List[Any]          =model(**_A					)
                            _lowerCamelCase	:		Optional[int]          =outputs.loss
                            _lowerCamelCase	:		Optional[int]          =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					):
                                   _lowerCamelCase	:		Dict          =F'''step_{overall_step}'''
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 _lowerCamelCase	:		List[str]          =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():
                                   _lowerCamelCase	:		Tuple          =model(**_A					)
                            _lowerCamelCase	:		Any          =outputs.logits.argmax(dim=-1					)
                            _lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =accelerator.gather_for_metrics((predictions, batch['labels'])					)
                            metric.add_batch(
                                predictions=_A		,      references=_A		,      )

                     _lowerCamelCase	:		Union[str, Any]          =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":
                            _lowerCamelCase	:		List[Any]          =F'''epoch_{epoch}'''
                            if args.output_dir is not None:
                                   _lowerCamelCase	:		Optional[int]          =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_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =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		,      )
       _lowerCamelCase	:		Dict          =parser.parse_args()
       _lowerCamelCase	:		int          ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(_A		,      _A					)


if __name__ == "__main__":
 main()






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase						 =	logging.getLogger()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =argparse.ArgumentParser()
       parser.add_argument('-f'					)
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()
       return args.f




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Any          =logging.StreamHandler(sys.stdout			)
             logger.addHandler(lowercase_			)

             _lowerCamelCase	:		Dict          =self.get_auto_remove_tmp_dir()
             _lowerCamelCase	:		int          =F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

             with patch.object(lowercase_      ,       'argv'      ,       lowercase_			):
                    _lowerCamelCase	:		Dict          =run_glue_with_pabee.main()
                    for value in result.values():
                           self.assertGreaterEqual(lowercase_      ,       0.75			)


