




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   fname.split(os.path.sep      )[-1]
  return re.search(R"""^(.*)_\d+\.jpg$"""   ,     _UpperCAmelCase      ).groups()[0]





class     snake_case  (	lowerCamelCase__							):


   '''simple docstring'''







   def __init__(					self     :	Optional[Any]						,    lowerCamelCase_     :	List[Any]						,    lowerCamelCase_     :	Optional[Any]=None						,    lowerCamelCase_     :	Union[str, Any]=None			)					->Any:
     '''simple docstring'''







     UpperCAmelCase__    						=   file_names
     UpperCAmelCase__    						=   image_transform
     UpperCAmelCase__    						=   label_to_id




   def __len__(					self     :	Any			)					->Dict:
     '''simple docstring'''







     return len(self.file_names			)







   def __getitem__(					self     :	int						,    lowerCamelCase_     :	str			)					->Dict:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.file_names[idx]
     UpperCAmelCase__    						=   PIL.Image.open(SCREAMING_SNAKE_CASE_			)
     UpperCAmelCase__    						=   raw_image.convert("""RGB"""			)
     if self.image_transform is not None:
       UpperCAmelCase__    						=   self.image_transform(SCREAMING_SNAKE_CASE_			)
     UpperCAmelCase__    						=   extract_label(SCREAMING_SNAKE_CASE_			)
     if self.label_to_id is not None:
       UpperCAmelCase__    						=   self.label_to_id[label]
     return {"image": image, "label": label}



def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  # Initialize accelerator
  UpperCAmelCase__    						=   Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision      )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase__    						=   config["""lr"""]
  UpperCAmelCase__    						=   int(config["""num_epochs"""]      )
  UpperCAmelCase__    						=   int(config["""seed"""]      )
  UpperCAmelCase__    						=   int(config["""batch_size"""]      )
  UpperCAmelCase__    						=   config["""image_size"""]
  if not isinstance(_UpperCAmelCase   ,     (list, tuple)      ):
    UpperCAmelCase__    						=   (image_size, image_size)

  # Grab all the image filenames
  UpperCAmelCase__    						=   [os.path.join(args.data_dir   ,     _UpperCAmelCase      ) for fname in os.listdir(args.data_dir      ) if fname.endswith(""".jpg"""      )]

  # Build the label correspondences
  UpperCAmelCase__    						=   [extract_label(_UpperCAmelCase      ) for fname in file_names]
  UpperCAmelCase__    						=   list(set(_UpperCAmelCase      )      )
  id_to_label.sort()
  UpperCAmelCase__    						=   {lbl: i for i, lbl in enumerate(_UpperCAmelCase      )}

  # Set the seed before splitting the data.
  np.random.seed(_UpperCAmelCase      )
  torch.manual_seed(_UpperCAmelCase      )
  torch.cuda.manual_seed_all(_UpperCAmelCase      )

  # Split our filenames between train and validation
  UpperCAmelCase__    						=   np.random.permutation(len(_UpperCAmelCase      )      )
  UpperCAmelCase__    						=   int(0.8 * len(_UpperCAmelCase      )      )
  UpperCAmelCase__    						=   random_perm[:cut]
  UpperCAmelCase__    						=   random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  UpperCAmelCase__    						=   Compose([RandomResizedCrop(_UpperCAmelCase   ,     scale=(0.5, 1.0)      ), ToTensor()]      )
  UpperCAmelCase__    						=   PetsDataset(
      [file_names[i] for i in train_split]   ,     image_transform=_UpperCAmelCase   ,     label_to_id=_UpperCAmelCase      )

  # For evaluation, we use a deterministic Resize
  UpperCAmelCase__    						=   Compose([Resize(_UpperCAmelCase      ), ToTensor()]      )
  UpperCAmelCase__    						=   PetsDataset([file_names[i] for i in eval_split]   ,     image_transform=_UpperCAmelCase   ,     label_to_id=_UpperCAmelCase      )

  # Instantiate dataloaders.
  UpperCAmelCase__    						=   DataLoader(_UpperCAmelCase   ,     shuffle=_UpperCAmelCase   ,     batch_size=_UpperCAmelCase   ,     num_workers=4      )
  UpperCAmelCase__    						=   DataLoader(_UpperCAmelCase   ,     shuffle=_UpperCAmelCase   ,     batch_size=_UpperCAmelCase   ,     num_workers=4      )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase__    						=   create_model("""resnet50d"""   ,     pretrained=_UpperCAmelCase   ,     num_classes=len(_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).
  UpperCAmelCase__    						=   model.to(accelerator.device      )

  # Freezing the base model
  for param in model.parameters():
    UpperCAmelCase__    						=   False
  for param in model.get_classifier().parameters():
    UpperCAmelCase__    						=   True

  # We normalize the batches of images to be a bit faster.
  UpperCAmelCase__    						=   torch.tensor(model.default_cfg["""mean"""]      )[None, :, None, None].to(accelerator.device      )
  UpperCAmelCase__    						=   torch.tensor(model.default_cfg["""std"""]      )[None, :, None, None].to(accelerator.device      )

  # Instantiate optimizer
  UpperCAmelCase__    						=   torch.optim.Adam(params=model.parameters()   ,     lr=lr / 25      )

  # Instantiate learning rate scheduler
  UpperCAmelCase__    						=   OneCycleLR(optimizer=_UpperCAmelCase   ,     max_lr=_UpperCAmelCase   ,     epochs=_UpperCAmelCase   ,     steps_per_epoch=len(_UpperCAmelCase      )      )

  # 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__    						=   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`.
      UpperCAmelCase__    						=   {k: v.to(accelerator.device      ) for k, v in batch.items()}
      UpperCAmelCase__    						=   (batch["""image"""] - mean) / std
      UpperCAmelCase__    						=   model(_UpperCAmelCase      )
      UpperCAmelCase__    						=   torch.nn.functional.cross_entropy(_UpperCAmelCase   ,     batch["""label"""]      )
      accelerator.backward(_UpperCAmelCase      )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

    model.eval()
    UpperCAmelCase__    						=   0
    UpperCAmelCase__    						=   0
    for _, batch in enumerate(_UpperCAmelCase      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase__    						=   {k: v.to(accelerator.device      ) for k, v in batch.items()}
      UpperCAmelCase__    						=   (batch["""image"""] - mean) / std
      with torch.no_grad():
        UpperCAmelCase__    						=   model(_UpperCAmelCase      )
      UpperCAmelCase__    						=   outputs.argmax(dim=-1      )
      UpperCAmelCase__						,					UpperCAmelCase__    						=   accelerator.gather_for_metrics((predictions, batch["""label"""])      )
      UpperCAmelCase__    						=   predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    UpperCAmelCase__    						=   accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''      )


def 						lowercase		(     ):
  UpperCAmelCase__    						=   argparse.ArgumentParser(description="""Simple example of training script."""      )
  parser.add_argument("""--data_dir"""   ,     required=_UpperCAmelCase   ,     help="""The data folder on disk."""      )
  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(
      """--checkpointing_steps"""   ,     type=_UpperCAmelCase   ,     default=_UpperCAmelCase   ,     help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""   ,     )
  parser.add_argument("""--cpu"""   ,     action="""store_true"""   ,     help="""If passed, will train on the CPU."""      )
  UpperCAmelCase__    						=   parser.parse_args()
  UpperCAmelCase__    						=   {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  training_function(_UpperCAmelCase   ,     _UpperCAmelCase      )


if __name__ == "__main__":
     main()










from typing import TYPE_CHECKING

from ...utils import _LazyModule


snake_case__ :						List[str]     =			{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
   from .tokenization_phobert import PhobertTokenizer

else:
   import sys

   snake_case__ :						Tuple     =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




