'''simple docstring'''


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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__	:     Tuple      =    16
snake_case__	:     Dict      =    32


def    _lowerCamelCase       ( lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				str  ):



     """simple docstring"""





     if args.with_tracking:
          UpperCAmelCase_				:  Optional[int]  = Accelerator(
              cpu=args.cpu      ,	mixed_precision=args.mixed_precision      ,	log_with='all'      ,	project_dir=args.project_dir  )
     else:
          UpperCAmelCase_				:  List[Any]  = Accelerator(cpu=args.cpu      ,	mixed_precision=args.mixed_precision  )

     if hasattr(args.checkpointing_steps      ,	'isdigit'  ):
          if args.checkpointing_steps == "epoch":
               UpperCAmelCase_				:  Union[str, Any]  = args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               UpperCAmelCase_				:  Any  = int(args.checkpointing_steps  )
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
     else:
          UpperCAmelCase_				:  Optional[int]  = None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_				:  Union[str, Any]  = config['lr']
     UpperCAmelCase_				:  str  = int(config['num_epochs']  )
     UpperCAmelCase_				:  Optional[int]  = int(config['seed']  )
     UpperCAmelCase_				:  List[str]  = int(config['batch_size']  )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          UpperCAmelCase_				:  Optional[int]  = os.path.split(lowercase_  )[-1].split('.'  )[0]
          accelerator.init_trackers(lowercase_      ,	lowercase_  )

     UpperCAmelCase_				:  str  = AutoTokenizer.from_pretrained('bert-base-cased'  )
     UpperCAmelCase_				:  Optional[int]  = load_dataset('glue'      ,	'mrpc'  )
     UpperCAmelCase_				:  str  = evaluate.load('glue'      ,	'mrpc'  )

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

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

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

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

     # Instantiate dataloaders.
     UpperCAmelCase_				:  Optional[int]  = DataLoader(
         tokenized_datasets['train']      ,	shuffle=lowercase_      ,	collate_fn=lowercase_      ,	batch_size=lowercase_  )
     UpperCAmelCase_				:  Any  = DataLoader(
         tokenized_datasets['validation']      ,	shuffle=lowercase_      ,	collate_fn=lowercase_      ,	batch_size=lowercase_  )

     set_seed(lowercase_  )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_				:  Tuple  = 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_				:  Any  = model.to(accelerator.device  )

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

     # Instantiate scheduler
     UpperCAmelCase_				:  int  = get_linear_schedule_with_warmup(
         optimizer=lowercase_      ,	num_warmup_steps=100      ,	num_training_steps=(len(lowercase_  ) * 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.
     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  str  = accelerator.prepare(
         lowercase_      ,	lowercase_      ,	lowercase_      ,	lowercase_      ,	lowercase_  )

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_				:  List[Any]  = 0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase_				:  int  = 0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''  )
               accelerator.load_state(args.resume_from_checkpoint  )
               UpperCAmelCase_				:  Any  = os.path.basename(args.resume_from_checkpoint  )
          else:
               # Get the most recent checkpoint
               UpperCAmelCase_				:  List[str]  = [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
               dirs.sort(key=os.path.getctime  )
               UpperCAmelCase_				:  Optional[int]  = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          UpperCAmelCase_				:  Any  = os.path.splitext(lowercase_  )[0]

          if "epoch" in training_difference:
               UpperCAmelCase_				:  int  = int(training_difference.replace('epoch_'      ,	''  )  ) + 1
               UpperCAmelCase_				:  Union[str, Any]  = None
          else:
               UpperCAmelCase_				:  int  = int(training_difference.replace('step_'      ,	''  )  )
               UpperCAmelCase_				:  Tuple  = resume_step // len(lowercase_  )
               resume_step -= starting_epoch * len(lowercase_  )

          # Now we train the model
     for epoch in range(lowercase_      ,	lowercase_  ):
          model.train()
          if args.with_tracking:
               UpperCAmelCase_				:  Any  = 0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               UpperCAmelCase_				:  Optional[Any]  = accelerator.skip_first_batches(lowercase_      ,	lowercase_  )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               UpperCAmelCase_				:  Optional[Any]  = train_dataloader
          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_				:  Union[str, Any]  = model(**lowercase_  )
               UpperCAmelCase_				:  Union[str, Any]  = outputs.loss
               UpperCAmelCase_				:  List[Any]  = loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(lowercase_  )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(lowercase_      ,	lowercase_  ):
                    UpperCAmelCase_				:  List[str]  = F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              UpperCAmelCase_				:  Dict  = os.path.join(args.output_dir      ,	lowercase_  )
                         accelerator.save_state(lowercase_  )

          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_				:  Tuple  = model(**lowercase_  )
               UpperCAmelCase_				:  Dict  = outputs.logits.argmax(dim=-1  )
               UpperCAmelCase_  ,     UpperCAmelCase_				:  Tuple  = accelerator.gather_for_metrics((predictions, batch['labels'])  )
               metric.add_batch(
                   predictions=lowercase_      ,	references=lowercase_      ,	)

          UpperCAmelCase_				:  int  = metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''      ,	lowercase_  )
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(lowercase_  ),
                       'epoch': epoch,
                   }      ,	step=lowercase_      ,	)

          if checkpointing_steps == "epoch":
               UpperCAmelCase_				:  str  = F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    UpperCAmelCase_				:  List[str]  = os.path.join(args.output_dir      ,	lowercase_  )
               accelerator.save_state(lowercase_  )

     if args.with_tracking:
          accelerator.end_training()


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = 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.'  )
     parser.add_argument(
         '--checkpointing_steps'      ,	type=lowercase_      ,	default=lowercase_      ,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'      ,	)
     parser.add_argument(
         '--resume_from_checkpoint'      ,	type=lowercase_      ,	default=lowercase_      ,	help='If the training should continue from a checkpoint folder.'      ,	)
     parser.add_argument(
         '--with_tracking'      ,	action='store_true'      ,	help='Whether to load in all available experiment trackers from the environment and use them for logging.'      ,	)
     parser.add_argument(
         '--output_dir'      ,	type=lowercase_      ,	default='.'      ,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,	)
     parser.add_argument(
         '--project_dir'      ,	type=lowercase_      ,	default='logs'      ,	help='Location on where to store experiment tracking logs` and relevent project information'      ,	)
     UpperCAmelCase_				:  Union[str, Any]  = parser.parse_args()
     UpperCAmelCase_				:  str  = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowercase_      ,	lowercase_  )


if __name__ == "__main__":
 main()









def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Union[str, Any]:





  '''simple docstring'''





  if len(str(lowercase_				)				) == 1 or len(str(lowercase_				)				) == 1:
    return a * b

  snake_case_					= max(len(str(lowercase_				)				)     ,   len(str(lowercase_				)				)				)
  snake_case_					= ma // 2

  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)
  snake_case_		,							snake_case_					= divmod(lowercase_     ,   10**ma				)

  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)
  snake_case_					= karatsuba((aa + aa)     ,   (ba + ba)				)
  snake_case_					= karatsuba(lowercase_     ,   lowercase_				)

  return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)



def   UpperCamelCase(   )      ->							Dict:





  '''simple docstring'''





  print(karatsuba(15463     ,   23489				)				)


if __name__ == "__main__":
    main()