

'''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
#
########################################################################

lowerCamelCase				:   Tuple      				=  3_2


def  _lowerCAmelCase      (		_UpperCamelCase		:  Accelerator							,				_UpperCamelCase		:  int = 16       )   ->    int:
     """simple docstring"""




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

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

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

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

     # Instantiate dataloaders.
     _SCREAMING_SNAKE_CASE												=DataLoader(
         tokenized_datasets['train']							,				shuffle=_UpperCamelCase							,				collate_fn=_UpperCamelCase							,				batch_size=_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=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

     lowerCamelCase				:   Tuple      				=  mocked_dataloaders  # noqa: F811


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     if os.environ.get('TESTING_MOCKED_DATALOADERS'							,				_UpperCamelCase       ) == "1":
          _SCREAMING_SNAKE_CASE												=2
     # Initialize accelerator
     _SCREAMING_SNAKE_CASE												=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												=config['lr']
     _SCREAMING_SNAKE_CASE												=int(config['num_epochs']       )
     _SCREAMING_SNAKE_CASE												=int(config['seed']       )
     _SCREAMING_SNAKE_CASE												=int(config['batch_size']       )

     _SCREAMING_SNAKE_CASE												=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(_UpperCamelCase       )       )
     def inner_training_loop(_UpperCamelCase		:  List[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												=observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          _SCREAMING_SNAKE_CASE												=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(_UpperCamelCase       )

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

          # Instantiate optimizer
          _SCREAMING_SNAKE_CASE												=AdamW(params=model.parameters()							,				lr=_UpperCamelCase       )
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=get_dataloaders(_UpperCamelCase							,				_UpperCamelCase       )

          # Instantiate scheduler
          _SCREAMING_SNAKE_CASE												=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.
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=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       ):
                    # And perform gradient accumulation
                    with accelerator.accumulate(_UpperCamelCase       ):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device       )
                         _SCREAMING_SNAKE_CASE												=model(**_UpperCamelCase       )
                         _SCREAMING_SNAKE_CASE												=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():
                         _SCREAMING_SNAKE_CASE												=model(**_UpperCamelCase       )
                    _SCREAMING_SNAKE_CASE												=outputs.logits.argmax(dim=-1       )
                    _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=accelerator.gather_for_metrics((predictions, batch['labels'])       )
                    metric.add_batch(
                        predictions=_UpperCamelCase							,				references=_UpperCamelCase							,				)

               _SCREAMING_SNAKE_CASE												=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  _lowerCAmelCase      (		)   ->    Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=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.'       )
     _SCREAMING_SNAKE_CASE												=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												={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
     training_function(_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     main()



'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
