





import argparse
import os

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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowercase      :       str									=  1_6
lowercase      :       List[Any]									=  3_2



def A_	(		A__		,      A__ = 16				)  ->		List[str]:
       a__						:	int =				AutoTokenizer.from_pretrained('bert-base-cased'				)
       a__						:	Optional[int] =				load_dataset('glue'		,      'mrpc'				)

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

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

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

       # Instantiate dataloaders.
       a__						:	int =				DataLoader(
           tokenized_datasets['train']		,      shuffle=A__		,      collate_fn=A__		,      batch_size=A__				)
       a__						:	Dict =				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

    lowercase      :       Union[str, Any]									=  mocked_dataloaders  # noqa: F811



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

       a__						:	Tuple =				evaluate.load('glue'		,      'mrpc'				)

       # If the batch size is too big we use gradient accumulation
       a__						:	Optional[int] =				1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              a__						:	Union[str, Any] =				batch_size // MAX_GPU_BATCH_SIZE
              a__						:	Union[str, Any] =				MAX_GPU_BATCH_SIZE

       set_seed(A__				)
       a__ ,							a__						:	Dict =				get_dataloaders(A__		,      A__				)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__						:	int =				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).
       a__						:	Union[str, Any] =				model.to(accelerator.device				)

       # Instantiate optimizer
       a__						:	Any =				AdamW(params=model.parameters()		,      lr=A__				)

       # Instantiate scheduler
       a__						:	Optional[int] =				get_linear_schedule_with_warmup(
           optimizer=A__		,      num_warmup_steps=100		,      num_training_steps=(len(A__				) * 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.
       a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Dict =				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__				):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device				)
                     a__						:	Tuple =				model(**A__				)
                     a__						:	List[str] =				outputs.loss
                     a__						:	int =				loss / gradient_accumulation_steps
                     accelerator.backward(A__				)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              a__						:	Any =				0
              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():
                            a__						:	List[Any] =				model(**A__				)
                     a__						:	List[Any] =				outputs.logits.argmax(dim=-1				)
                     a__ ,							a__						:	Any =				accelerator.gather((predictions, batch['labels'])				)
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(A__				) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   a__						:	Tuple =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
                                   a__						:	Tuple =				references[: len(eval_dataloader.dataset				) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=A__		,      references=A__		,      )

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



def A_	(		)  ->		List[str]:
       a__						:	Optional[int] =				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.'				)
       a__						:	Optional[Any] =				parser.parse_args()
       a__						:	str =				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(A__		,      A__				)


if __name__ == "__main__":
    main()









def A_	(		A__		,      A__				)  ->		str:
       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'				)

       a__						:	List[str] =				str(bin(A__				)				)[2:]  # remove the leading "0b"
       a__						:	Optional[int] =				str(bin(A__				)				)[2:]  # remove the leading "0b"

       a__						:	List[str] =				max(len(A__				)		,      len(A__				)				)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'				)				)
           for char_a, char_b in zip(a_binary.zfill(A__				)		,      b_binary.zfill(A__				)				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


