





'''simple docstring'''

from ..utils import DummyObject, requires_backends






class    UpperCAmelCase	(       metaclass=UpperCamelCase__				):
 __lowercase   		=				["""note_seq"""]
 def __init__(				self      :Optional[Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :List[str]							)->							int:
     requires_backends(self						,       ["note_seq"]							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :str						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							Optional[int]:
     requires_backends(cls						,       ["note_seq"]							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       *lowercase_      :Tuple						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     requires_backends(cls						,       ["note_seq"]							)










'''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							:	Optional[int]			=16
__lowerCAmelCase							:	Tuple			=32





def 						UpperCamelCase				(							_lowerCamelCase					:					Accelerator						,       _lowerCamelCase					:					DatasetDict						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					int = 16       ):
    A__				=			AutoTokenizer.from_pretrained("bert-base-cased"       )
    A__				=			DatasetDict(
        {
            "train": dataset["train"].select(_lowerCamelCase       ),
            "validation": dataset["train"].select(_lowerCamelCase       ),
            "test": dataset["validation"],
        }       )

    def tokenize_function(_lowerCamelCase					:					Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        A__				=			tokenizer(examples["sentence1"]						,       examples["sentence2"]						,       truncation=_lowerCamelCase						,       max_length=_lowerCamelCase       )
        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__				=			datasets.map(
            _lowerCamelCase						,       batched=_lowerCamelCase						,       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__				=			tokenized_datasets.rename_column("label"						,       "labels"       )

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

        return tokenizer.pad(
            _lowerCamelCase						,       padding="longest"						,       max_length=_lowerCamelCase						,       pad_to_multiple_of=_lowerCamelCase						,       return_tensors="pt"						,       )

    # Instantiate dataloaders.
    A__				=			DataLoader(
        tokenized_datasets["train"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )
    A__				=			DataLoader(
        tokenized_datasets["validation"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )

    A__				=			DataLoader(
        tokenized_datasets["test"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )

    return train_dataloader, eval_dataloader, test_dataloader





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

    A__				=			evaluate.load("glue"						,       "mrpc"       )

    # If the batch size is too big we use gradient accumulation
    A__				=			1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        A__				=			batch_size // MAX_GPU_BATCH_SIZE
        A__				=			MAX_GPU_BATCH_SIZE

    set_seed(_lowerCamelCase       )

    # New Code #
    # Create our folds:
    A__				=			kfold.split(np.zeros(datasets["train"].num_rows       )						,       datasets["train"]["label"]       )
    A__				=			[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(_lowerCamelCase       ):
        A__,	A__,	A__				=			get_fold_dataloaders(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        A__				=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,       return_dict=_lowerCamelCase       )

        # 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__				=			model.to(accelerator.device       )

        # Instantiate optimizer
        A__				=			AdamW(params=model.parameters()						,       lr=_lowerCamelCase       )

        # Instantiate scheduler
        A__				=			get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase						,       num_warmup_steps=1_00						,       num_training_steps=(len(_lowerCamelCase       ) * 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__				=			accelerator.prepare(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

        # Now we train the model
        for epoch in range(_lowerCamelCase       ):
            model.train()
            for step, batch in enumerate(_lowerCamelCase       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                A__				=			model(**_lowerCamelCase       )
                A__				=			outputs.loss
                A__				=			loss / gradient_accumulation_steps
                accelerator.backward(_lowerCamelCase       )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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

        # New Code #
        # We also run predictions on the test set at the very end
        A__				=			[]
        for step, batch in enumerate(_lowerCamelCase       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                A__				=			model(**_lowerCamelCase       )
            A__				=			outputs.logits
            A__,	A__				=			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(_lowerCamelCase						,       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__				=			torch.cat(_lowerCamelCase						,       dim=0       )
    A__				=			torch.stack(_lowerCamelCase						,       dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    A__				=			metric.compute(predictions=_lowerCamelCase						,       references=_lowerCamelCase       )
    accelerator.print("Average test metrics from all folds:"						,       _lowerCamelCase       )





def 						UpperCamelCase				(							):
    A__				=			argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision"						,       type=_lowerCamelCase						,       default=_lowerCamelCase						,       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=_lowerCamelCase						,       default=3						,       help="The number of splits to perform across the dataset"       )
    A__				=			parser.parse_args()
    A__				=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase						,       _lowerCamelCase       )


if __name__ == "__main__":
  main()



