





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


lowercase				:	Any      						=  1_6
lowercase				:	Optional[Any]      						=  3_2



def A_     (		A__		,				A__		,				A__		,				A__		,				A__ = 16    )  ->		Union[str, Any]:
       a__						:     int			=    AutoTokenizer.from_pretrained('bert-base-cased'    )
       a__						:     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)
              a__						:     str			=    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__						:     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
       a__						:     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__						:     List[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__						:     Union[str, Any]			=    16
              elif accelerator.mixed_precision != "no":
                     a__						:     List[Any]			=    8
              else:
                     a__						:     str			=    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__						:     Any			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       a__						:     Optional[Any]			=    DataLoader(
           tokenized_datasets['test']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader, test_dataloader



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

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

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

       set_seed(A__    )

       # New Code #
       # Create our folds:
       a__						:     int			=    kfold.split(np.zeros(datasets['train'].num_rows    )		,				datasets['train']['label']    )
       a__						:     Union[str, Any]			=    []
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(A__    ):
              a__			,							a__			,							a__						:     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)
              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__						:     List[str]			=    model.to(accelerator.device    )

              # Instantiate optimizer
              a__						:     List[str]			=    AdamW(params=model.parameters()		,				lr=A__    )

              # Instantiate scheduler
              a__						:     List[Any]			=    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__						:     List[str]			=    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__						:     int			=    model(**A__    )
                            a__						:     Optional[Any]			=    outputs.loss
                            a__						:     Union[str, 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():
                                   a__						:     Dict			=    model(**A__    )
                            a__						:     Optional[Any]			=    outputs.logits.argmax(dim=-1    )
                            a__			,							a__						:     List[Any]			=    accelerator.gather_for_metrics((predictions, batch['labels'])    )
                            metric.add_batch(
                                predictions=A__		,				references=A__		,				)

                     a__						:     Optional[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
              a__						:     Dict			=    []
              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__						:     Dict			=    outputs.logits
                     a__			,							a__						:     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:
       a__						:     Optional[Any]			=    torch.cat(A__		,				dim=0    )
       a__						:     List[str]			=    torch.stack(A__		,				dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
       a__						:     List[Any]			=    metric.compute(predictions=A__		,				references=A__    )
       accelerator.print('Average test metrics from all folds:'		,				A__    )



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


if __name__ == "__main__":
    main()









import enum
import shutil
import sys


lowercase						, lowercase				:	List[Any]      						=  shutil.get_terminal_size()

lowercase				:	Union[str, Any]      						=  {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}





class 			A__   (							enum.Enum     ):




   """simple docstring"""



   __A       :    List[str]	  =  0
   __A       :    str	  =  1



def A_     (		A__		,				A__=""    )  ->		int:
       sys.stdout.write(str(A__    ) + end    )
       sys.stdout.flush()



def A_     (		A__		,				A__		,				A__=""    )  ->		int:
       forceWrite(F'\u001b[{color}m{content}\u001b[0m'		,				A__    )



def A_     (		)  ->		Any:
       forceWrite('\r'    )



def A_     (		A__		,				A__    )  ->		List[str]:
       forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'    )



def A_     (		)  ->		Any:
       forceWrite(' ' * TERMINAL_WIDTH    )
       reset_cursor()



def A_     (		)  ->		Any:
       reset_cursor()
       forceWrite('-' * TERMINAL_WIDTH    )


