




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     :		str								=       16
_lowerCamelCase     :		Tuple								=       32







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_ = 16						)       ->	Tuple:



       """simple docstring"""

       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(lowercase_						):
              # 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(lowercase_						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__							=   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":
                     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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       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__							=   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=100			,    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__							=   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__							=   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__							=   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 SCREAMING_SNAKE_CASE    (	)       ->	int:



       """simple docstring"""

       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()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCamelCase     :		Union[str, Any]								=       {
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Union[str, Any]								=       [
      """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SwiftFormerForImageClassification""",
      """SwiftFormerModel""",
      """SwiftFormerPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_swiftformer import (
      SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SwiftFormerConfig,
      SwiftFormerOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swiftformer import (
        SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwiftFormerForImageClassification,
        SwiftFormerModel,
        SwiftFormerPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		str								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


