






'''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__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase = 16					)->				Optional[int]:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      DatasetDict(
          {
              """train""": dataset["""train"""].select(__UpperCamelCase					),
              """validation""": dataset["""train"""].select(__UpperCamelCase					),
              """test""": dataset["""validation"""],
          }					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      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():
            UpperCamelCase       						=      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
      UpperCamelCase       						=      tokenized_datasets.rename_column("""label"""	,		"""labels"""					)

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

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

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

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

      return train_dataloader, eval_dataloader, test_dataloader







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Any:
      # New Code #
      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(__UpperCamelCase					)

      # 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(__UpperCamelCase					):
            UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      get_fold_dataloaders(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		)
            # 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=__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).
            UpperCamelCase       						=      model.to(accelerator.device					)

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

            # Instantiate scheduler
            UpperCamelCase       						=      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.
            UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      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					)
                        UpperCamelCase       						=      model(**__UpperCamelCase					)
                        UpperCamelCase       						=      outputs.loss
                        UpperCamelCase       						=      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():
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                        UpperCamelCase       						=      outputs.logits.argmax(dim=-1					)
                        UpperCamelCase						,UpperCamelCase       						=      accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
                        metric.add_batch(
                            predictions=__UpperCamelCase	,		references=__UpperCamelCase	,		)

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







def 					lowercase__				(   )->				List[str]:
      UpperCamelCase       						=      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"""					)
      UpperCamelCase       						=      parser.parse_args()
      UpperCamelCase       						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__											=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         '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

     SCREAMING_SNAKE_CASE__											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
