
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

_A	  =					32




def 				lowerCamelCase__				(						a__  :  Accelerator	,      a__  :  int = 16     )		->	Dict:
       UpperCamelCase_							       =       AutoTokenizer.from_pretrained("""bert-base-cased"""     )
       UpperCamelCase_							       =       load_dataset("""glue"""	,      """mrpc"""     )

       def tokenize_function(a__  :  Tuple     ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_							       =       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():
              UpperCamelCase_							       =       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
       UpperCamelCase_							       =       tokenized_datasets.rename_column("""label"""	,      """labels"""     )

       def collate_fn(a__  :  List[str]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase_							       =       16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_							       =       8
              else:
                     UpperCamelCase_							       =       None

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

       # Instantiate dataloaders.
       UpperCamelCase_							       =       DataLoader(
           tokenized_datasets["""train"""]	,      shuffle=a__	,      collate_fn=a__	,      batch_size=a__     )
       UpperCamelCase_							       =       DataLoader(
           tokenized_datasets["""validation"""]	,      shuffle=a__	,      collate_fn=a__	,      batch_size=a__     )

       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

      _A	  =					mocked_dataloaders  # noqa: F811




def 				lowerCamelCase__				(						a__  :  List[Any]	,      a__  :  List[str]     )		->	List[Any]:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,      a__     ) == "1":
              UpperCamelCase_							       =       2
       # 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"""     )

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(a__     )     )
       def inner_training_loop(a__  :  Union[str, Any]     ):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              UpperCamelCase_							       =       observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              UpperCamelCase_							       =       num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(a__     )

              # 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=a__     )

              # 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=a__     )
              UpperCamelCase_       ,				UpperCamelCase_							       =       get_dataloaders(a__	,      a__     )

              # Instantiate scheduler
              UpperCamelCase_							       =       get_linear_schedule_with_warmup(
                  optimizer=a__	,      num_warmup_steps=100	,      num_training_steps=(len(a__     ) * 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.
              UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       accelerator.prepare(
                  a__	,      a__	,      a__	,      a__	,      a__     )

              # Now we train the model
              for epoch in range(a__     ):
                     model.train()
                     for step, batch in enumerate(a__     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(a__     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   UpperCamelCase_							       =       model(**a__     )
                                   UpperCamelCase_							       =       outputs.loss
                                   accelerator.backward(a__     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     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():
                                   UpperCamelCase_							       =       model(**a__     )
                            UpperCamelCase_							       =       outputs.logits.argmax(dim=-1     )
                            UpperCamelCase_       ,				UpperCamelCase_							       =       accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                            metric.add_batch(
                                predictions=a__	,      references=a__	,      )

                     UpperCamelCase_							       =       metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f'''epoch {epoch}:'''	,      a__     )

    # 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 				lowerCamelCase__				(						)		->	Optional[int]:
       UpperCamelCase_							       =       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."""     )
       UpperCamelCase_							       =       parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       UpperCamelCase_							       =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
       training_function(a__	,      a__     )


if __name__ == "__main__":
      main()





def 				lowerCamelCase__				(						a__  :  float	,      a__  :  float	,      a__  :  float     )		->	float:
       if mass < 0:
              raise ValueError("""The mass of the body cannot be negative"""     )
       if radius <= 0:
              raise ValueError("""The radius is always a positive non zero integer"""     )
       return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



