


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


A      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A ,						__A = 1_6			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     AutoTokenizer.from_pretrained("bert-base-cased"			)
       UpperCAmelCase__										=     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)
              UpperCAmelCase__										=     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():
              UpperCAmelCase__										=     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
       UpperCAmelCase__										=     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.
              UpperCAmelCase__										=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase__										=     1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__										=     8
              else:
                     UpperCAmelCase__										=     None

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

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

       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["test"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)

       return train_dataloader, eval_dataloader, test_dataloader





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[int]:



       '''simple docstring'''





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

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

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

       set_seed(__A			)

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

              # Instantiate optimizer
              UpperCAmelCase__										=     AdamW(params=model.parameters() ,						lr=__A			)

              # Instantiate scheduler
              UpperCAmelCase__										=     get_linear_schedule_with_warmup(
                  optimizer=__A ,						num_warmup_steps=1_0_0 ,						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.
              UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     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			)
                            UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     outputs.loss
                            UpperCAmelCase__										=     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():
                                   UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     outputs.logits.argmax(dim=-1			)
                            UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.gather_for_metrics((predictions, batch["labels"])			)
                            metric.add_batch(
                                predictions=__A ,						references=__A ,						)

                     UpperCAmelCase__										=     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
              UpperCAmelCase__										=     []
              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():
                            UpperCAmelCase__										=     model(**__A			)
                     UpperCAmelCase__										=     outputs.logits
                     UpperCAmelCase__						,			UpperCAmelCase__										=     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:
       UpperCAmelCase__										=     torch.cat(__A ,						dim=0			)
       UpperCAmelCase__										=     torch.stack(__A ,						dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
       UpperCAmelCase__										=     metric.compute(predictions=__A ,						references=__A			)
       accelerator.print("Average test metrics from all folds:" ,						__A			)





def __UpperCAmelCase       (		)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     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"			)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()









from .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





