






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      				=							1_6
__UpperCAmelCase      				=							3_2





def 		A_			(		lowercase_	)							->Dict:







 """simple docstring"""

 return int(x / 2**2_0	)
class 					a_:



     """simple docstring"""



     def __enter__(						self			:					Optional[int])    ->					Union[str, Any]:

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

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





     def   __UpperCamelCase		(						self			:					List[Any])    ->					Union[str, Any]:

      """simple docstring"""
      return self.process.memory_info().rss





     def   __UpperCamelCase		(						self			:					Dict)    ->					List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						-1

      while True:
       SCREAMING_SNAKE_CASE							  =						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			:					Optional[int]       ,       *lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						False

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

      SCREAMING_SNAKE_CASE							  =						self.cpu_mem_used()
      SCREAMING_SNAKE_CASE							  =						bamb(self.cpu_end - self.cpu_begin)
      SCREAMING_SNAKE_CASE							  =						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 		A_			(		lowercase_					,       lowercase_	)							->Any:







 """simple docstring"""

 if os.environ.get('TESTING_MOCKED_DATALOADERS'					,       lowercase_	) == "1":
  SCREAMING_SNAKE_CASE							  =						2

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

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

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

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

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

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

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

 def collate_fn(lowercase_	):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE							  =						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":
   SCREAMING_SNAKE_CASE							  =						1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE							  =						8
  else:
   SCREAMING_SNAKE_CASE							  =						None

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

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

 set_seed(lowercase_	)

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

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

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

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE							  =						get_linear_schedule_with_warmup(
     optimizer=lowercase_					,       num_warmup_steps=1_0					,       num_training_steps=(len(lowercase_	) * 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.
 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						accelerator.prepare(
     lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)

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

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

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

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

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

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

 if args.with_tracking:
  accelerator.end_training()





def 		A_			(		)							->Optional[int]:







 """simple docstring"""

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


if __name__ == "__main__":
      main()












import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def 		A_			(		lowercase_					,       lowercase_ = False	)							->Union[str, Any]:







 """simple docstring"""

 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(lowercase_	)
 np.random.seed(lowercase_	)
 torch.manual_seed(lowercase_	)
 if is_xpu_available():
  torch.xpu.manual_seed_all(lowercase_	)
 elif is_npu_available():
  torch.npu.manual_seed_all(lowercase_	)
 else:
  torch.cuda.manual_seed_all(lowercase_	)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(lowercase_	)





def 		A_			(		lowercase_ = None					,       lowercase_ = None	)							->Optional[Any]:







 """simple docstring"""

 if rng_type == RNGType.TORCH:
  SCREAMING_SNAKE_CASE							  =						torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  SCREAMING_SNAKE_CASE							  =						torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  SCREAMING_SNAKE_CASE							  =						torch.tensor(xm.get_rng_state()	)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  SCREAMING_SNAKE_CASE							  =						torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  SCREAMING_SNAKE_CASE							  =						torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  SCREAMING_SNAKE_CASE							  =						generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 SCREAMING_SNAKE_CASE							  =						AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  SCREAMING_SNAKE_CASE							  =						rng_state.to(xm.xla_device()	)
  xm.collective_broadcast([rng_state]	)
  xm.mark_step()
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  SCREAMING_SNAKE_CASE							  =						rng_state.to(state.device	)
  torch.distributed.broadcast(lowercase_					,       0	)
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(lowercase_					,       0	)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(lowercase_	)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(lowercase_	)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()	)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = None	)							->str:







 """simple docstring"""

 for rng_type in rng_types:
  synchronize_rng_state(RNGType(lowercase_	)					,       generator=lowercase_	)




