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


lowercase_         =       16
lowercase_         =       32




def       A_      (       lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase = 16   )  ->				Dict:


     """simple docstring"""







     UpperCAmelCase_    :     List[str]            =	AutoTokenizer.from_pretrained("""bert-base-cased"""   )
     UpperCAmelCase_    :     Dict            =	DatasetDict(
         {
             """train""": dataset["""train"""].select(lowercase   ),
             """validation""": dataset["""train"""].select(lowercase   ),
             """test""": dataset["""validation"""],
         }   )

     def tokenize_function(lowercase   ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_    :     Any            =	tokenizer(examples["""sentence1"""]    ,			examples["""sentence2"""]    ,			truncation=lowercase    ,			max_length=lowercase   )
          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_    :     int            =	datasets.map(
              lowercase    ,			batched=lowercase    ,			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_    :     int            =	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.
          UpperCAmelCase_    :     Any            =	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_    :     Optional[int]            =	16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase_    :     Any            =	8
          else:
               UpperCAmelCase_    :     Any            =	None

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

     # Instantiate dataloaders.
     UpperCAmelCase_    :     List[Any]            =	DataLoader(
         tokenized_datasets["""train"""]    ,			shuffle=lowercase    ,			collate_fn=lowercase    ,			batch_size=lowercase   )
     UpperCAmelCase_    :     Optional[Any]            =	DataLoader(
         tokenized_datasets["""validation"""]    ,			shuffle=lowercase    ,			collate_fn=lowercase    ,			batch_size=lowercase   )

     UpperCAmelCase_    :     Optional[int]            =	DataLoader(
         tokenized_datasets["""test"""]    ,			shuffle=lowercase    ,			collate_fn=lowercase    ,			batch_size=lowercase   )

     return train_dataloader, eval_dataloader, test_dataloader




def       A_      (       lowercase    ,			lowercase   )  ->				Tuple:


     """simple docstring"""







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

     UpperCAmelCase_    :     int            =	evaluate.load("""glue"""    ,			"""mrpc"""   )

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

     set_seed(lowercase   )

     # New Code #
     # Create our folds:
     UpperCAmelCase_    :     Tuple            =	kfold.split(np.zeros(datasets["""train"""].num_rows   )    ,			datasets["""train"""]["""label"""]   )
     UpperCAmelCase_    :     Optional[int]            =	[]
     # Iterate over them
     for i, (train_idxs, valid_idxs) in enumerate(lowercase   ):
          UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Optional[Any]            =	get_fold_dataloaders(
              lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)
          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          UpperCAmelCase_    :     int            =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,			return_dict=lowercase   )

          # 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_    :     Union[str, Any]            =	model.to(accelerator.device   )

          # Instantiate optimizer
          UpperCAmelCase_    :     Union[str, Any]            =	AdamW(params=model.parameters()    ,			lr=lowercase   )

          # Instantiate scheduler
          UpperCAmelCase_    :     Union[str, Any]            =	get_linear_schedule_with_warmup(
              optimizer=lowercase    ,			num_warmup_steps=100    ,			num_training_steps=(len(lowercase   ) * 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_    :     Optional[Any]            =	accelerator.prepare(
              lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase   )

          # Now we train the model
          for epoch in range(lowercase   ):
               model.train()
               for step, batch in enumerate(lowercase   ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device   )
                    UpperCAmelCase_    :     Dict            =	model(**lowercase   )
                    UpperCAmelCase_    :     Tuple            =	outputs.loss
                    UpperCAmelCase_    :     Optional[int]            =	loss / gradient_accumulation_steps
                    accelerator.backward(lowercase   )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(lowercase   ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device   )
                    with torch.no_grad():
                         UpperCAmelCase_    :     List[Any]            =	model(**lowercase   )
                    UpperCAmelCase_    :     Optional[int]            =	outputs.logits.argmax(dim=-1   )
                    UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
                    metric.add_batch(
                        predictions=lowercase    ,			references=lowercase    ,			)

               UpperCAmelCase_    :     Dict            =	metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f'''epoch {epoch}:'''    ,			lowercase   )

          # New Code #
          # We also run predictions on the test set at the very end
          UpperCAmelCase_    :     Union[str, Any]            =	[]
          for step, batch in enumerate(lowercase   ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device   )
               with torch.no_grad():
                    UpperCAmelCase_    :     Union[str, Any]            =	model(**lowercase   )
               UpperCAmelCase_    :     List[str]            =	outputs.logits
               UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	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(lowercase    ,			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_    :     Union[str, Any]            =	torch.cat(lowercase    ,			dim=0   )
     UpperCAmelCase_    :     Union[str, Any]            =	torch.stack(lowercase    ,			dim=0   ).sum(dim=0   ).div(int(args.num_folds   )   ).argmax(dim=-1   )
     UpperCAmelCase_    :     Any            =	metric.compute(predictions=lowercase    ,			references=lowercase   )
     accelerator.print("""Average test metrics from all folds:"""    ,			lowercase   )




def       A_      (       )  ->				Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_    :     str            =	argparse.ArgumentParser(description="""Simple example of training script."""   )
     parser.add_argument(
         """--mixed_precision"""    ,			type=lowercase    ,			default=lowercase    ,			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=lowercase    ,			default=3    ,			help="""The number of splits to perform across the dataset"""   )
     UpperCAmelCase_    :     Dict            =	parser.parse_args()
     UpperCAmelCase_    :     Optional[Any]            =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(lowercase    ,			lowercase   )


if __name__ == "__main__":
       main()


"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
