






"""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
#
########################################################################


UpperCamelCase_	:				Tuple										= 16
UpperCamelCase_	:				int										= 32



def     __lowercase					(    a :				Accelerator       ,		a :				DatasetDict       ,		a :				List[int]       ,		a :				List[int]       ,		a :				int = 16 )					->					int:
      __snake_case      :    str  			=AutoTokenizer.from_pretrained('''bert-base-cased''' )
      __snake_case      :    Optional[Any]  			=DatasetDict(
          {
              '''train''': dataset['''train'''].select(a ),
              '''validation''': dataset['''train'''].select(a ),
              '''test''': dataset['''validation'''],
          } )

      def tokenize_function(a :				int ):
            # max_length=None => use the model max length (it's actually the default)
            __snake_case      :    int  			=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():
            __snake_case      :    Union[str, 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
      __snake_case      :    Tuple  			=tokenized_datasets.rename_column('''label'''       ,		'''labels''' )

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

            return tokenizer.pad(
                a       ,		padding='''longest'''       ,		max_length=a       ,		pad_to_multiple_of=a       ,		return_tensors='''pt'''       ,		)

      # Instantiate dataloaders.
      __snake_case      :    Tuple  			=DataLoader(
          tokenized_datasets['''train''']       ,		shuffle=a       ,		collate_fn=a       ,		batch_size=a )
      __snake_case      :    Tuple  			=DataLoader(
          tokenized_datasets['''validation''']       ,		shuffle=a       ,		collate_fn=a       ,		batch_size=a )

      __snake_case      :    List[str]  			=DataLoader(
          tokenized_datasets['''test''']       ,		shuffle=a       ,		collate_fn=a       ,		batch_size=a )

      return train_dataloader, eval_dataloader, test_dataloader



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

      __snake_case      :    int  			=evaluate.load('''glue'''       ,		'''mrpc''' )

      # If the batch size is too big we use gradient accumulation
      __snake_case      :    List[Any]  			=1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            __snake_case      :    Union[str, Any]  			=batch_size // MAX_GPU_BATCH_SIZE
            __snake_case      :    List[str]  			=MAX_GPU_BATCH_SIZE

      set_seed(a )

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

            # Instantiate optimizer
            __snake_case      :    List[Any]  			=AdamW(params=model.parameters()       ,		lr=a )

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

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



def     __lowercase					(    )					->					Optional[Any]:
      __snake_case      :    Optional[Any]  			=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''' )
      __snake_case      :    Dict  			=parser.parse_args()
      __snake_case      :    Dict  			={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(a       ,		a )


if __name__ == "__main__":
    main()










"""simple docstring"""





from __future__ import annotations



def     __lowercase					(    a :				int       ,		a :				int )					->					list[str]:
      if partitions <= 0:
            raise ValueError('''partitions must be a positive number!''' )
      if partitions > number_of_bytes:
            raise ValueError('''partitions can not > number_of_bytes!''' )
      __snake_case      :    List[str]  			=number_of_bytes // partitions
      __snake_case      :    str  			=[]
      for i in range(a ):
            __snake_case      :    Optional[Any]  			=i * bytes_per_partition + 1
            __snake_case      :    Any  			=(
                number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
            )
            allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
      return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()


