





'''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
#
########################################################################


_SCREAMING_SNAKE_CASE	 =   16
_SCREAMING_SNAKE_CASE	 =   32


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					List[Any]		,       SCREAMING_SNAKE_CASE_  :					Tuple = 16    ):



    '''simple docstring'''





    _lowerCAmelCase				=      AutoTokenizer.from_pretrained("bert-base-cased"    )
    _lowerCAmelCase				=      DatasetDict(
        {
            "train": dataset["train"].select(__UpperCamelCase    ),
            "validation": dataset["train"].select(__UpperCamelCase    ),
            "test": dataset["validation"],
        }    )

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

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

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

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

    _lowerCAmelCase				=      DataLoader(
        tokenized_datasets["test"]		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=__UpperCamelCase    )

    return train_dataloader, eval_dataloader, test_dataloader


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





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

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

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

    set_seed(__UpperCamelCase    )

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

        # 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).
        _lowerCAmelCase				=      model.to(accelerator.device    )

        # Instantiate optimizer
        _lowerCAmelCase				=      AdamW(params=model.parameters()		,       lr=__UpperCamelCase    )

        # Instantiate scheduler
        _lowerCAmelCase				=      get_linear_schedule_with_warmup(
            optimizer=__UpperCamelCase		,       num_warmup_steps=100		,       num_training_steps=(len(__UpperCamelCase    ) * 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.
        _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      accelerator.prepare(
            __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase    )

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

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

            _lowerCAmelCase				=      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''		,       __UpperCamelCase    )

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


def 	__a():



    '''simple docstring'''





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


if __name__ == "__main__":
       main()









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(SCREAMING_SNAKE_CASE_    )    )


def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if index == len(SCREAMING_SNAKE_CASE_    ):
        return True

    # Recursive Step
    for i in range(SCREAMING_SNAKE_CASE_    ):
        if valid_coloring(graph[index]		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
            # Color current vertex
            _lowerCAmelCase				=      i
            # Validate coloring
            if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       index + 1    ):
                return True
            # Backtrack
            _lowerCAmelCase				=      -1
    return False





def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [-1] * len(SCREAMING_SNAKE_CASE_    )

    if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       0    ):
        return colored_vertices

    return []


