






'''simple docstring'''
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__ =			16
UpperCamelCase__ =			32




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = 16							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     AutoTokenizer.from_pretrained("bert-base-cased"							)
   lowercase_	:    List[str]           =     load_dataset("glue"						,		"mrpc"							)

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

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

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

   # Instantiate dataloaders.
   lowercase_	:    List[Any]           =     DataLoader(
       tokenized_datasets["train"]						,		shuffle=_lowerCamelCase						,		collate_fn=_lowerCamelCase						,		batch_size=_lowerCamelCase							)
   lowercase_	:    int           =     DataLoader(
       tokenized_datasets["validation"]						,		shuffle=_lowerCamelCase						,		collate_fn=_lowerCamelCase						,		batch_size=_lowerCamelCase							)

   return train_dataloader, eval_dataloader


# 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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   if os.environ.get("TESTING_MOCKED_DATALOADERS"						,		_lowerCamelCase							) == "1":
      lowercase_	:    List[Any]           =     2
   # Initialize accelerator
   lowercase_	:    int           =     Accelerator(cpu=args.cpu						,		mixed_precision=args.mixed_precision							)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_	:    Dict           =     config["""lr"""]
   lowercase_	:    Any           =     int(config["num_epochs"]							)
   lowercase_	:    Optional[int]           =     int(config["seed"]							)
   lowercase_	:    List[Any]           =     int(config["batch_size"]							)

   lowercase_	:    Optional[Any]           =     evaluate.load("glue"						,		"mrpc"							)

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=_lowerCamelCase							)
   def inner_training_loop(_UpperCamelCase							):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(_lowerCamelCase							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase_	:    str           =     AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,		return_dict=_lowerCamelCase							)

      # 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).
      lowercase_	:    str           =     model.to(accelerator.device							)

      # Instantiate optimizer
      lowercase_	:    Optional[Any]           =     AdamW(params=model.parameters()						,		lr=_lowerCamelCase							)
      lowercase_	:    List[Any]           =     get_dataloaders(_lowerCamelCase						,		_lowerCamelCase							)

      # Instantiate scheduler
      lowercase_	:    Dict           =     get_linear_schedule_with_warmup(
          optimizer=_lowerCamelCase						,		num_warmup_steps=100						,		num_training_steps=(len(_lowerCamelCase							) * num_epochs)						,		)

      # 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.
      lowercase_	:    Optional[Any]           =     accelerator.prepare(
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase							)

      # Now we train the model
      for epoch in range(_lowerCamelCase							):
         model.train()
         for step, batch in enumerate(_lowerCamelCase							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            lowercase_	:    Any           =     model(**_lowerCamelCase							)
            lowercase_	:    Union[str, Any]           =     outputs.loss
            accelerator.backward(_lowerCamelCase							)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         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():
               lowercase_	:    Optional[int]           =     model(**_lowerCamelCase							)
            lowercase_	:    Tuple           =     outputs.logits.argmax(dim=-1							)
            lowercase_	:    Any           =     accelerator.gather_for_metrics((predictions, batch["labels"])							)
            metric.add_batch(
                predictions=_lowerCamelCase						,		references=_lowerCamelCase						,		)

         lowercase_	:    int           =     metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""						,		_lowerCamelCase							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




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


if __name__ == "__main__":
      main()












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   if number < 0:
      raise ValueError("number must not be negative"							)
   return number & (number - 1) == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()




