

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
#
########################################################################

lowercase_     =       3_2
def 					a				(      A__     :       Tuple				,       A__     :       Any = 16					)   ->				Optional[int]:





      """simple docstring"""



      _lowercase     	=AutoTokenizer.from_pretrained('bert-base-cased'					)
      _lowercase     	=load_dataset('glue'				,       'mrpc'					)

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

      def collate_fn(A__     :       List[str]					):
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  _lowercase     	=16
            elif accelerator.mixed_precision != "no":
                  _lowercase     	=8
            else:
                  _lowercase     	=None

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

      # Instantiate dataloaders.
      _lowercase     	=DataLoader(
          tokenized_datasets['train']				,       shuffle=lowercase_				,       collate_fn=lowercase_				,       batch_size=lowercase_					)
      _lowercase     	=DataLoader(
          tokenized_datasets['validation']				,       shuffle=lowercase_				,       collate_fn=lowercase_				,       batch_size=lowercase_					)

      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

  lowercase_     =       mocked_dataloaders  # noqa: F811
def 					a				(      A__     :       Dict				,       A__     :       List[Any]					)   ->				Dict:





      """simple docstring"""



      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,       lowercase_					) == "1":
            _lowercase     	=2
      # Initialize accelerator
      _lowercase     	=Accelerator(cpu=args.cpu				,       mixed_precision=args.mixed_precision					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _lowercase     	=config['lr']
      _lowercase     	=int(config['num_epochs']					)
      _lowercase     	=int(config['seed']					)
      _lowercase     	=int(config['batch_size']					)

      _lowercase     	=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(lowercase_					)					)
      def inner_training_loop(A__     :       Union[str, 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
            _lowercase     	=observed_batch_size // batch_size

            # And then set it in the Accelerator directly:
            _lowercase     	=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(lowercase_					)

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            _lowercase     	=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).
            _lowercase     	=model.to(accelerator.device					)

            # Instantiate optimizer
            _lowercase     	=AdamW(params=model.parameters()				,       lr=lowercase_					)
            _lowercase	,	_lowercase     	=get_dataloaders(lowercase_				,       lowercase_					)

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

                  _lowercase     	=metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F'''epoch {epoch}:'''				,       lowercase_					)

                   # 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 					a				(      )   ->				Tuple:





      """simple docstring"""



      _lowercase     	=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.'					)
      _lowercase     	=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
      _lowercase     	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
      training_function(lowercase_				,       lowercase_					)


if __name__ == "__main__":
  main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_     =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       ['OneFormerImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       [
      'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OneFormerForUniversalSegmentation',
      'OneFormerModel',
      'OneFormerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
  from .processing_oneformer import OneFormerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_oneformer import OneFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_oneformer import (
        ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        OneFormerForUniversalSegmentation,
        OneFormerModel,
        OneFormerPreTrainedModel,
    )


else:
  import sys

  lowercase_     =       _LazyModule(__name__, globals()['__file__'], _import_structure)


