


'''simple docstring'''



import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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__		:					Any												=  16
A__		:					Tuple												=  32



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Accelerator      ,    UpperCAmelCase_    :				int = 16							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Any								=		AutoTokenizer.from_pretrained('bert-base-cased'							)
     __lowerCamelCase   :	Any								=		load_dataset('glue'      ,    'mrpc'							)

     def tokenize_function(UpperCAmelCase_    :				Optional[Any]							):
          # max_length=None => use the model max length (it's actually the default)
          __lowerCamelCase   :	List[Any]								=		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():
          __lowerCamelCase   :	str								=		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
     __lowerCamelCase   :	Dict								=		tokenized_datasets.rename_column('label'      ,    'labels'							)

     def collate_fn(UpperCAmelCase_    :				str							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowerCamelCase   :	Any								=		1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __lowerCamelCase   :	Tuple								=		16
          elif accelerator.mixed_precision != "no":
               __lowerCamelCase   :	str								=		8
          else:
               __lowerCamelCase   :	Optional[Any]								=		None

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

     # Instantiate dataloaders.
     __lowerCamelCase   :	List[str]								=		DataLoader(
         tokenized_datasets['train']      ,    shuffle=UpperCAmelCase_      ,    collate_fn=UpperCAmelCase_      ,    batch_size=UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		DataLoader(
         tokenized_datasets['validation']      ,    shuffle=UpperCAmelCase_      ,    collate_fn=UpperCAmelCase_      ,    batch_size=UpperCAmelCase_							)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A__		:					Optional[int]												=  mocked_dataloaders  # noqa: F811



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Any:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,    UpperCAmelCase_							) == "1":
          __lowerCamelCase   :	int								=		2
     # Initialize accelerator
     __lowerCamelCase   :	Dict								=		Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCamelCase   :	int								=		config['lr']
     __lowerCamelCase   :	Any								=		int(config['num_epochs']							)
     __lowerCamelCase   :	Optional[int]								=		int(config['seed']							)
     __lowerCamelCase   :	Optional[int]								=		int(config['batch_size']							)

     __lowerCamelCase   :	str								=		evaluate.load('glue'      ,    'mrpc'							)

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=UpperCAmelCase_							)
     def inner_training_loop(UpperCAmelCase_    :				Any							):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(UpperCAmelCase_							)

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          __lowerCamelCase   :	Any								=		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).
          __lowerCamelCase   :	Optional[Any]								=		model.to(accelerator.device							)

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

          # Instantiate scheduler
          __lowerCamelCase   :	str								=		get_linear_schedule_with_warmup(
              optimizer=UpperCAmelCase_      ,    num_warmup_steps=1_00      ,    num_training_steps=(len(UpperCAmelCase_							) * num_epochs)      ,    )

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

               __lowerCamelCase   :	List[Any]								=		metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'epoch {epoch}:'      ,    UpperCAmelCase_							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()



def 			UpperCAmelCase__					(	)     ->		List[Any]:
     __lowerCamelCase   :	List[Any]								=		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.'							)
     __lowerCamelCase   :	Any								=		parser.parse_args()
     __lowerCamelCase   :	Dict								=		{'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_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






