'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	bool:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       len(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       len(a_		)
     SCREAMING_SNAKE_CASE					:			Any    	=       [[False for _ in range(m + 1		)] for _ in range(n + 1		)]
     SCREAMING_SNAKE_CASE					:			Dict    	=       True
     for i in range(a_		):
          for j in range(m + 1		):
               if dp[i][j]:
                    if j < m and a[i].upper() == b[j]:
                         SCREAMING_SNAKE_CASE					:			Any    	=       True
                    if a[i].islower():
                         SCREAMING_SNAKE_CASE					:			Any    	=       True
     return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




'''simple docstring'''





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


_lowerCAmelCase  :Tuple  				=    16
_lowerCAmelCase  :List[Any]  				=    32


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_ = 16		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       AutoTokenizer.from_pretrained('bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       DatasetDict(
         {
             'train': dataset['train'].select(a_		),
             'validation': dataset['train'].select(a_		),
             'test': dataset['validation'],
         }		)

     def tokenize_function(a_		):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE					:			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():
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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
     SCREAMING_SNAKE_CASE					:			Dict    	=       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.
          SCREAMING_SNAKE_CASE					:			Optional[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":
               SCREAMING_SNAKE_CASE					:			Any    	=       16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE					:			Any    	=       8
          else:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       None

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

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       DataLoader(
         tokenized_datasets['train']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       DataLoader(
         tokenized_datasets['validation']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     SCREAMING_SNAKE_CASE					:			Any    	=       DataLoader(
         tokenized_datasets['test']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     return train_dataloader, eval_dataloader, test_dataloader


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     # Download the dataset
     SCREAMING_SNAKE_CASE					:			Any    	=       load_dataset('glue'       ,      'mrpc'		)
     # Create our splits
     SCREAMING_SNAKE_CASE					:			Dict    	=       StratifiedKFold(n_splits=int(args.num_folds		)		)
     # Initialize accelerator
     SCREAMING_SNAKE_CASE					:			Tuple    	=       Accelerator(cpu=args.cpu       ,      mixed_precision=args.mixed_precision		)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE					:			Dict    	=       config['lr']
     SCREAMING_SNAKE_CASE					:			Tuple    	=       int(config['num_epochs']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['seed']		)
     SCREAMING_SNAKE_CASE					:			Any    	=       int(config['batch_size']		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       evaluate.load('glue'       ,      'mrpc'		)

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

     set_seed(a_		)

     # New Code #
     # Create our folds:
     SCREAMING_SNAKE_CASE					:			int    	=       kfold.split(np.zeros(datasets['train'].num_rows		)       ,      datasets['train']['label']		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       []
     # Iterate over them
     for i, (train_idxs, valid_idxs) in enumerate(a_		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_fold_dataloaders(
              a_       ,      a_       ,      a_       ,      a_       ,      )
          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          SCREAMING_SNAKE_CASE					:			str    	=       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).
          SCREAMING_SNAKE_CASE					:			Dict    	=       model.to(accelerator.device		)

          # Instantiate optimizer
          SCREAMING_SNAKE_CASE					:			int    	=       AdamW(params=model.parameters()       ,      lr=a_		)

          # Instantiate scheduler
          SCREAMING_SNAKE_CASE					:			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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       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		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.loss
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       loss / gradient_accumulation_steps
                    accelerator.backward(a_		)
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

               model.eval()
               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():
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Any    	=       outputs.logits.argmax(dim=-1		)
                    SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
                    metric.add_batch(
                        predictions=a_       ,      references=a_       ,      )

               SCREAMING_SNAKE_CASE					:			int    	=       metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f"""epoch {epoch}:"""       ,      a_		)

          # New Code #
          # We also run predictions on the test set at the very end
          SCREAMING_SNAKE_CASE					:			Any    	=       []
          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():
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(**a_		)
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       outputs.logits
               SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
               fold_predictions.append(predictions.cpu()		)
               if i == 0:
                    # We need all of the test predictions
                    test_references.append(references.cpu()		)
        # Use accelerator.print to print only on the main process.
          test_predictions.append(torch.cat(a_       ,      dim=0		)		)
          # We now need to release all our memory and get rid of the current model, optimizer, etc
          accelerator.free_memory()
     # New Code #
     # Finally we check the accuracy of our folded results:
     SCREAMING_SNAKE_CASE					:			int    	=       torch.cat(a_       ,      dim=0		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.stack(a_       ,      dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       metric.compute(predictions=a_       ,      references=a_		)
     accelerator.print('Average test metrics from all folds:'       ,      a_		)


def     __lowerCAmelCase						(       )      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			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.'		)
     # New Code #
     parser.add_argument('--num_folds'       ,      type=a_       ,      default=3       ,      help='The number of splits to perform across the dataset'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       parser.parse_args()
     SCREAMING_SNAKE_CASE					:			Dict    	=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a_       ,      a_		)


if __name__ == "__main__":
 main()



