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


_snake_case      = 16
_snake_case      = 32

def  __lowerCamelCase					( _lowercase						,     _lowercase = 16			)       ->      Any:
     UpperCamelCase						    =					AutoTokenizer.from_pretrained('bert-base-cased'			)
     UpperCamelCase						    =					load_dataset('glue'						,     'mrpc'			)

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

     def collate_fn(_lowercase			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCamelCase						    =					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":
               UpperCamelCase						    =					16
          elif accelerator.mixed_precision != "no":
               UpperCamelCase						    =					8
          else:
               UpperCamelCase						    =					None

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

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

     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

 _snake_case      = mocked_dataloaders  # noqa: F811

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Dict:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'						,     _lowercase			) == "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 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=_lowercase			)
     def inner_training_loop(_lowercase			):
          # 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(_lowercase			)

          # 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=_lowercase			)

          # 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=_lowercase			)
          UpperCamelCase    ,		UpperCamelCase						    =					get_dataloaders(_lowercase						,     _lowercase			)

          # Instantiate scheduler
          UpperCamelCase						    =					get_linear_schedule_with_warmup(
              optimizer=_lowercase						,     num_warmup_steps=100						,     num_training_steps=(len(_lowercase			) * 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(
              _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)

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

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

               UpperCamelCase						    =					metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'epoch {epoch}:'						,     _lowercase			)

    # 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					( )       ->      str:
     UpperCamelCase						    =					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.'			)
     UpperCamelCase						    =					parser.parse_args()
     UpperCamelCase						    =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase						,     _lowercase			)


if __name__ == "__main__":
 main()




def  __lowerCamelCase					( _lowercase			)       ->      list:
     UpperCamelCase						    =					len(_lowercase			)
     for _ in range(_lowercase			):
          for i in range(_ % 2						,     arr_size - 1						,     2			):
               if arr[i + 1] < arr[i]:
                    UpperCamelCase    ,		UpperCamelCase						    =					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _snake_case      = list(range(10, 0, -1))
 print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")


