
'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

__UpperCAmelCase												=   32



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Accelerator   ,     snake_case_				:	int = 16							)       ->	List[Any]:
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		AutoTokenizer.from_pretrained('bert-base-cased'							)
 SCREAMING_SNAKE_CASE  :  List[str]								=		load_dataset('glue'   ,     'mrpc'							)

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

 def collate_fn(snake_case_				:	Dict							):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE  :  int								=		16
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE  :  Tuple								=		8
  else:
   SCREAMING_SNAKE_CASE  :  str								=		None

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

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		DataLoader(
     tokenized_datasets['train']   ,     shuffle=_lowerCamelCase   ,     collate_fn=_lowerCamelCase   ,     batch_size=_lowerCamelCase							)
 SCREAMING_SNAKE_CASE  :  Dict								=		DataLoader(
     tokenized_datasets['validation']   ,     shuffle=_lowerCamelCase   ,     collate_fn=_lowerCamelCase   ,     batch_size=_lowerCamelCase							)

 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

   __UpperCAmelCase												=   mocked_dataloaders  # noqa: F811



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

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		evaluate.load('glue'   ,     'mrpc'							)

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(_lowerCamelCase							)							)
 def inner_training_loop(snake_case_				:	Optional[Any]							):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  SCREAMING_SNAKE_CASE  :  Tuple								=		observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  SCREAMING_SNAKE_CASE  :  Any								=		num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(_lowerCamelCase							)

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

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE  :  Tuple								=		AdamW(params=model.parameters()   ,     lr=_lowerCamelCase							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		get_dataloaders(_lowerCamelCase   ,     _lowerCamelCase							)

  # Instantiate scheduler
  SCREAMING_SNAKE_CASE  :  str								=		get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase   ,     num_warmup_steps=100   ,     num_training_steps=(len(_lowerCamelCase							) * 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.
  SCREAMING_SNAKE_CASE  :  Tuple								=		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							):
    # And perform gradient accumulation
    with accelerator.accumulate(_lowerCamelCase							):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device							)
     SCREAMING_SNAKE_CASE  :  str								=		model(**_lowerCamelCase							)
     SCREAMING_SNAKE_CASE  :  Tuple								=		outputs.loss
     accelerator.backward(_lowerCamelCase							)
     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():
     SCREAMING_SNAKE_CASE  :  Any								=		model(**_lowerCamelCase							)
    SCREAMING_SNAKE_CASE  :  Tuple								=		outputs.logits.argmax(dim=-1							)
    SCREAMING_SNAKE_CASE  :  int								=		accelerator.gather_for_metrics((predictions, batch['labels'])							)
    metric.add_batch(
        predictions=_lowerCamelCase   ,     references=_lowerCamelCase   ,     )

   SCREAMING_SNAKE_CASE  :  str								=		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"""epoch {epoch}:"""   ,     _lowerCamelCase							)

    # 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        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  List[str]								=		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.'							)
 SCREAMING_SNAKE_CASE  :  str								=		parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 SCREAMING_SNAKE_CASE  :  str								=		{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
 training_function(_lowerCamelCase   ,     _lowerCamelCase							)


if __name__ == "__main__":
   main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

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



