






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# 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)
#
# 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:     Any   					=					16
__a:     List[Any]   					=					32




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = 1_6 )				->						Dict:
       _UpperCAmelCase							=    AutoTokenizer.from_pretrained("""bert-base-cased""" )
       _UpperCAmelCase							=    load_dataset("""glue"""   ,      """mrpc""" )

       def tokenize_function(__snake_case ):
              # 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""" )

       def collate_fn(__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(
                  __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   ,      drop_last=__snake_case )
       _UpperCAmelCase							=    DataLoader(
           tokenized_datasets["""validation"""]   ,      shuffle=__snake_case   ,      collate_fn=__snake_case   ,      batch_size=__snake_case   ,      drop_last=(accelerator.mixed_precision == """fp8""")   ,      )

       return train_dataloader, eval_dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       # Initialize accelerator
       _UpperCAmelCase							=    Accelerator(cpu=args.cpu   ,      mixed_precision=args.mixed_precision )
       # 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"""] )

       _UpperCAmelCase							=    evaluate.load("""glue"""   ,      """mrpc""" )

       # 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

       set_seed(__snake_case )
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataloaders(__snake_case   ,      __snake_case )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase							=    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,      return_dict=__snake_case )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       _UpperCAmelCase							=    model.to(accelerator.device )
       # Instantiate optimizer
       _UpperCAmelCase							=    AdamW(params=model.parameters()   ,      lr=__snake_case )

       # Instantiate scheduler
       _UpperCAmelCase							=    get_linear_schedule_with_warmup(
           optimizer=__snake_case   ,      num_warmup_steps=1_0_0   ,      num_training_steps=(len(__snake_case ) * num_epochs) // gradient_accumulation_steps   ,      )

       # Prepare everything
       # 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	,			_UpperCAmelCase							=    accelerator.prepare(
           __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

       # Now we train the model
       for epoch in range(__snake_case ):
              model.train()
              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 )
                     _UpperCAmelCase							=    model(**__snake_case )
                     _UpperCAmelCase							=    outputs.loss
                     _UpperCAmelCase							=    loss / gradient_accumulation_steps
                     accelerator.backward(__snake_case )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              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 )




def       _SCREAMING_SNAKE_CASE (							)				->						Optional[int]:
       _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.""" )
       _UpperCAmelCase							=    parser.parse_args()
       _UpperCAmelCase							=    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
       training_function(__snake_case   ,      __snake_case )


if __name__ == "__main__":
  main()







import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


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


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    max(self.process.memory_info().rss       ,				self.cpu_memory_peak							)

                # can't sleep or will not catch the peak right (this comment is here on purpose)
                if not self.peak_monitoring:
                       break


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

       gc.collect()
       torch.cuda.empty_cache()

       # CPU mem
       _UpperCAmelCase							=    psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    torch.cuda.memory_allocated(__snake_case )
       torch.cuda.reset_peak_memory_stats()

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

       gc.collect()
       torch.cuda.empty_cache()

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       print(f"""{description}:""" )
       print(f"""- Time: {measures["time"]:.2f}s""" )
       for i in range(torch.cuda.device_count() ):
              print(f"""- GPU {i} allocated: {measures[str(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    measures[f"""{i}-peak"""]
              print(f"""- GPU {i} peak: {peak:.2f}MiB""" )
       print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
       print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )