


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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
#
########################################################################


__lowerCAmelCase			=							16
__lowerCAmelCase			=							32




def        snake_case_					(  snake_case      ,							snake_case = 16					)  ->    List[str]:
       lowercase__:       Union[str, Any]        =       AutoTokenizer.from_pretrained('bert-base-cased'					)
       lowercase__:       Optional[int]        =       load_dataset('glue'      ,							'mrpc'					)

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

       def collate_fn(snake_case					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__:       Tuple        =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowercase__:       str        =       16
              elif accelerator.mixed_precision != "no":
                     lowercase__:       Dict        =       8
              else:
                     lowercase__:       int        =       None

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

       # Instantiate dataloaders.
       lowercase__:       Optional[int]        =       DataLoader(
           tokenized_datasets['train']      ,							shuffle=snake_case      ,							collate_fn=snake_case      ,							batch_size=snake_case					)
       lowercase__:       Dict        =       DataLoader(
           tokenized_datasets['validation']      ,							shuffle=snake_case      ,							collate_fn=snake_case      ,							batch_size=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

 __lowerCAmelCase			=							mocked_dataloaders  # noqa: F811




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,							snake_case					) == "1":
              lowercase__:       Any        =       2
       # New Code #
       lowercase__:       str        =       int(args.gradient_accumulation_steps					)
       lowercase__:       str        =       int(args.local_sgd_steps					)
       # Initialize accelerator
       lowercase__:       List[Any]        =       Accelerator(
           cpu=args.cpu      ,							mixed_precision=args.mixed_precision      ,							gradient_accumulation_steps=snake_case					)
       if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
              raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'					)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__:       List[str]        =       config['lr']
       lowercase__:       str        =       int(config['num_epochs']					)
       lowercase__:       Any        =       int(config['seed']					)
       lowercase__:       int        =       int(config['batch_size']					)

       lowercase__:       Any        =       evaluate.load('glue'      ,							'mrpc'					)

       set_seed(snake_case					)
       lowercase__      ,						lowercase__:       Tuple        =       get_dataloaders(snake_case      ,							snake_case					)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__:       Tuple        =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,							return_dict=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).
       lowercase__:       List[str]        =       model.to(accelerator.device					)

       # Instantiate optimizer
       lowercase__:       int        =       AdamW(params=model.parameters()      ,							lr=snake_case					)

       # Instantiate scheduler
       lowercase__:       Optional[int]        =       get_linear_schedule_with_warmup(
           optimizer=snake_case      ,							num_warmup_steps=1_00      ,							num_training_steps=(len(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.
       lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Tuple        =       accelerator.prepare(
           snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)

       # Now we train the model
       for epoch in range(snake_case					):
              model.train()
              with LocalSGD(
                  accelerator=snake_case      ,							model=snake_case      ,							local_sgd_steps=snake_case      ,							enabled=local_sgd_steps is not None					) as local_sgd:
                     for step, batch in enumerate(snake_case					):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device					)
                            # New code #
                            # We use the new `accumulate` context manager to perform gradient accumulation
                            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                            with accelerator.accumulate(snake_case					):
                                   lowercase__:       List[str]        =       model(**snake_case					)
                                   lowercase__:       Union[str, Any]        =       output.loss
                                   accelerator.backward(snake_case					)
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

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

              lowercase__:       Optional[Any]        =       metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'epoch {epoch}:'      ,							snake_case					)




def        snake_case_					(  )  ->    List[Any]:
       lowercase__:       Any        =       argparse.ArgumentParser(description='Simple example of training script.'					)
       parser.add_argument(
           '--mixed_precision'      ,							type=snake_case      ,							default=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.'      ,							)
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps'      ,							type=snake_case      ,							default=1      ,							help='The number of minibatches to be ran before gradients are accumulated.'      ,							)
       parser.add_argument(
           '--local_sgd_steps'      ,							type=snake_case      ,							default=8      ,							help='Number of local SGD steps or None to disable local SGD'					)
       parser.add_argument('--cpu'      ,							action='store_true'      ,							help='If passed, will train on the CPU.'					)
       lowercase__:       Dict        =       parser.parse_args()
       lowercase__:       Dict        =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(snake_case      ,							snake_case					)


if __name__ == "__main__":
 main()






from math import pow




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							)  ->    tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       lowercase__:       int        =       int(pow(snake_case      ,							snake_case					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              lowercase__      ,						lowercase__:       Any        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              lowercase__      ,						lowercase__:       Optional[int]        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
       return current_sum, solutions_count




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  'Invalid input\n'
                  'needed_sum must be between 1 and 1000, power between 2 and 10.'					)

       return backtrack(snake_case      ,							snake_case      ,							1      ,							0      ,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
 import doctest

 doctest.testmod()


