





'''simple docstring'''

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase							:	List[str]			=logging.get_logger(__name__)






@dataclass
class    UpperCAmelCase	:
 __lowercase   		=				field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()				)}				)
 __lowercase   		=				field(
     metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
 __lowercase   		=				field(
     default=128			,       metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }			,       )
 __lowercase   		=				field(
     default=UpperCamelCase__			,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     A__				=			self.task_name.lower()






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""train"""
 __lowercase   		=				"""dev"""
 __lowercase   		=				"""test"""






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				42
 __lowercase   		=				42
 __lowercase   		=				42
 def __init__(				self      :List[str]						,       lowercase_      :GlueDataTrainingArguments						,       lowercase_      :PreTrainedTokenizerBase						,       lowercase_      :Optional[int] = None						,       lowercase_      :Union[str, Split] = Split.train						,       lowercase_      :Optional[str] = None						,       )->							Tuple:
     warnings.warn(
         "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
         "library. You can have a look at this example script for pointers: "
         "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"						,       lowercase_						,       )
     A__				=			args
     A__				=			glue_processors[args.task_name]()
     A__				=			glue_output_modes[args.task_name]
     if isinstance(lowercase_						,       lowercase_							):
         try:
             A__				=			Split[mode]
         except KeyError:
             raise KeyError("mode is not a valid split name"							)
        # Load data features from cache or dataset file
     A__				=			os.path.join(
         cache_dir if cache_dir is not None else args.data_dir						,       F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"						,       )
     A__				=			self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         A__,	A__				=			label_list[2], label_list[1]
     A__				=			label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     A__				=			cached_features_file + ".lock"
     with FileLock(lowercase_							):
         if os.path.exists(lowercase_							) and not args.overwrite_cache:
             A__				=			time.time()
             A__				=			torch.load(lowercase_							)
             logger.info(
                 F"Loading features from cached file {cached_features_file} [took %.3f s]"						,       time.time() - start							)
         else:
             logger.info(F"Creating features from dataset file at {args.data_dir}"							)

             if mode == Split.dev:
                 A__				=			self.processor.get_dev_examples(args.data_dir							)
             elif mode == Split.test:
                 A__				=			self.processor.get_test_examples(args.data_dir							)
             else:
                 A__				=			self.processor.get_train_examples(args.data_dir							)
             if limit_length is not None:
                 A__				=			examples[:limit_length]
             A__				=			glue_convert_examples_to_features(
                 lowercase_						,       lowercase_						,       max_length=args.max_seq_length						,       label_list=lowercase_						,       output_mode=self.output_mode						,       )
             A__				=			time.time()
             torch.save(self.features						,       lowercase_							)
             # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
             logger.info(
                 F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"							)
 def __len__(				self      :Optional[int]							)->							int:
     return len(self.features							)
 def __getitem__(				self      :Any						,       lowercase_      :int							)->							InputFeatures:
     return self.features[i]



 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     return self.label_list










'''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()



