




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

SCREAMING_SNAKE_CASE__ :   Optional[int]						=     32

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE = 16					)			->				Tuple:
       lowerCamelCase     :   Union[str, Any]    				=      AutoTokenizer.from_pretrained("bert-base-cased"					)
       lowerCamelCase     :   int    				=      load_dataset("glue"				,"mrpc"					)

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

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

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

       # Instantiate dataloaders.
       lowerCamelCase     :   Tuple    				=      DataLoader(
           tokenized_datasets["train"]				,shuffle=_SCREAMING_SNAKE_CASE				,collate_fn=_SCREAMING_SNAKE_CASE				,batch_size=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Dict    				=      DataLoader(
           tokenized_datasets["validation"]				,shuffle=_SCREAMING_SNAKE_CASE				,collate_fn=_SCREAMING_SNAKE_CASE				,batch_size=_SCREAMING_SNAKE_CASE					)

       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

       SCREAMING_SNAKE_CASE__ :   int						=     mocked_dataloaders  # noqa: F811

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Union[str, Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"				,_SCREAMING_SNAKE_CASE					) == "1":
              lowerCamelCase     :   Any    				=      2
       # Initialize accelerator
       lowerCamelCase     :   str    				=      Accelerator(cpu=args.cpu				,mixed_precision=args.mixed_precision					)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCamelCase     :   Optional[Any]    				=      config["lr"]
       lowerCamelCase     :   Optional[int]    				=      int(config["num_epochs"]					)
       lowerCamelCase     :   Any    				=      int(config["seed"]					)
       lowerCamelCase     :   List[Any]    				=      int(config["batch_size"]					)

       lowerCamelCase     :   Optional[int]    				=      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(_SCREAMING_SNAKE_CASE					)					)
       def inner_training_loop(_SCREAMING_SNAKE_CASE					):
              # 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
              lowerCamelCase     :   str    				=      observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase     :   int    				=      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(_SCREAMING_SNAKE_CASE					)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowerCamelCase     :   Tuple    				=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,return_dict=_SCREAMING_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).
              lowerCamelCase     :   Dict    				=      model.to(accelerator.device					)

              # Instantiate optimizer
              lowerCamelCase     :   str    				=      AdamW(params=model.parameters()				,lr=_SCREAMING_SNAKE_CASE					)
              lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      get_dataloaders(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

              # Instantiate scheduler
              lowerCamelCase     :   List[Any]    				=      get_linear_schedule_with_warmup(
                  optimizer=_SCREAMING_SNAKE_CASE				,num_warmup_steps=100				,num_training_steps=(len(_SCREAMING_SNAKE_CASE					) * 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.
              lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase     :   List[Any]    				=      accelerator.prepare(
                  _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

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

                     model.eval()
                     for step, batch in enumerate(_SCREAMING_SNAKE_CASE					):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device					)
                            with torch.no_grad():
                                   lowerCamelCase     :   Union[str, Any]    				=      model(**_SCREAMING_SNAKE_CASE					)
                            lowerCamelCase     :   int    				=      outputs.logits.argmax(dim=-1					)
                            lowerCamelCase				,   lowerCamelCase     :   Dict    				=      accelerator.gather_for_metrics((predictions, batch["labels"])					)
                            metric.add_batch(
                                predictions=_SCREAMING_SNAKE_CASE				,references=_SCREAMING_SNAKE_CASE				,)

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

    # 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   A   (   )			->				List[Any]:
       lowerCamelCase     :   List[Any]    				=      argparse.ArgumentParser(description="Simple example of training script."					)
       parser.add_argument(
           "--mixed_precision"				,type=_SCREAMING_SNAKE_CASE				,default=_SCREAMING_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."					)
       lowerCamelCase     :   Union[str, Any]    				=      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
       lowerCamelCase     :   Dict    				=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
       training_function(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
       main()












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






