



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 		SCREAMING_SNAKE_CASE     (     snake_case__      )   ->	Union[str, Any]:
    __UpperCAmelCase			 =fname.split(os.path.sep      )[-1]
    return re.search(r'''^(.*)_\d+\.jpg$'''				,   lowercase__      ).groups()[0]



class        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase ):





   def __init__(self    ,  UpperCAmelCase    ,  UpperCAmelCase=None    ,  UpperCAmelCase=None):
       '''simple docstring'''







       __UpperCAmelCase			 =file_names
       __UpperCAmelCase			 =image_transform
       __UpperCAmelCase			 =label_to_id





   def __len__(self):
       '''simple docstring'''







       return len(self.file_names)






   def __getitem__(self    ,  UpperCAmelCase):
       '''simple docstring'''







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







def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__      )   ->	Dict:
    # Initialize accelerator
    if args.with_tracking:
        __UpperCAmelCase			 =Accelerator(
            cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with='''all'''				,   project_dir=args.project_dir      )
    else:
        __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(lowercase__				,   (list, tuple)      ):
        __UpperCAmelCase			 =(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps				,   '''isdigit'''      ):
        if args.checkpointing_steps == "epoch":
            __UpperCAmelCase			 =args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCAmelCase			 =int(args.checkpointing_steps      )
        else:
            raise ValueError(
                f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
    else:
        __UpperCAmelCase			 =None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __UpperCAmelCase			 =os.path.split(lowercase__      )[-1].split('''.'''      )[0]
        accelerator.init_trackers(lowercase__				,   lowercase__      )

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

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

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

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

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

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

    # Instantiate dataloaders.
    __UpperCAmelCase			 =DataLoader(lowercase__				,   shuffle=lowercase__				,   batch_size=lowercase__				,   num_workers=4      )
    __UpperCAmelCase			 =DataLoader(lowercase__				,   shuffle=lowercase__				,   batch_size=lowercase__				,   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=lowercase__				,   num_classes=len(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).
    __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=lowercase__				,   max_lr=lowercase__				,   epochs=lowercase__				,   steps_per_epoch=len(lowercase__      )      )

    # 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			 =accelerator.prepare(
        lowercase__				,   lowercase__				,   lowercase__				,   lowercase__				,   lowercase__      )
    # We need to keep track of how many total steps we have iterated over
    __UpperCAmelCase			 =0
    # We also need to keep track of the starting epoch so files are named properly
    __UpperCAmelCase			 =0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""      )
            accelerator.load_state(args.resume_from_checkpoint      )
            __UpperCAmelCase			 =os.path.basename(args.resume_from_checkpoint      )
        else:
            # Get the most recent checkpoint
            __UpperCAmelCase			 =[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
            dirs.sort(key=os.path.getctime      )
            __UpperCAmelCase			 =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCAmelCase			 =os.path.splitext(lowercase__      )[0]

        if "epoch" in training_difference:
            __UpperCAmelCase			 =int(training_difference.replace('''epoch_'''				,   ''''''      )      ) + 1
            __UpperCAmelCase			 =None
        else:
            __UpperCAmelCase			 =int(training_difference.replace('''step_'''				,   ''''''      )      )
            __UpperCAmelCase			 =resume_step // len(lowercase__      )
            resume_step -= starting_epoch * len(lowercase__      )

        # Now we train the model
    for epoch in range(lowercase__				,   lowercase__      ):
        model.train()
        if args.with_tracking:
            __UpperCAmelCase			 =0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __UpperCAmelCase			 =accelerator.skip_first_batches(lowercase__				,   lowercase__      )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCAmelCase			 =train_dataloader
        for batch in active_dataloader:
            # 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(lowercase__      )
            __UpperCAmelCase			 =torch.nn.functional.cross_entropy(lowercase__				,   batch['''label''']      )
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowercase__      )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(lowercase__				,   lowercase__      ):
                __UpperCAmelCase			 =f"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCAmelCase			 =os.path.join(args.output_dir				,   lowercase__      )
                    accelerator.save_state(lowercase__      )
        model.eval()
        __UpperCAmelCase			 =0
        __UpperCAmelCase			 =0
        for step, batch in enumerate(lowercase__      ):
            # 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(lowercase__      )
            __UpperCAmelCase			 =outputs.argmax(dim=-1      )
            __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}"""      )
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': 100 * eval_metric,
                    '''train_loss''': total_loss.item() / len(lowercase__      ),
                    '''epoch''': epoch,
                }				,   step=lowercase__				,   )
        if checkpointing_steps == "epoch":
            __UpperCAmelCase			 =f"""epoch_{epoch}"""
            if args.output_dir is not None:
                __UpperCAmelCase			 =os.path.join(args.output_dir				,   lowercase__      )
            accelerator.save_state(lowercase__      )

    if args.with_tracking:
        accelerator.end_training()




def 		SCREAMING_SNAKE_CASE     (     )   ->	Any:
    __UpperCAmelCase			 =argparse.ArgumentParser(description='''Simple example of training script.'''      )
    parser.add_argument('''--data_dir'''				,   required=lowercase__				,   help='''The data folder on disk.'''      )
    parser.add_argument('''--fp16'''				,   action='''store_true'''				,   help='''If passed, will use FP16 training.'''      )
    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.'''      )
    parser.add_argument(
        '''--checkpointing_steps'''				,   type=lowercase__				,   default=lowercase__				,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,   )
    parser.add_argument(
        '''--output_dir'''				,   type=lowercase__				,   default='''.'''				,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,   )
    parser.add_argument(
        '''--resume_from_checkpoint'''				,   type=lowercase__				,   default=lowercase__				,   help='''If the training should continue from a checkpoint folder.'''				,   )
    parser.add_argument(
        '''--with_tracking'''				,   action='''store_true'''				,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,   )
    parser.add_argument(
        '''--project_dir'''				,   type=lowercase__				,   default='''logs'''				,   help='''Location on where to store experiment tracking logs` and relevent project information'''				,   )
    __UpperCAmelCase			 =parser.parse_args()
    __UpperCAmelCase			 ={"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
    training_function(lowercase__				,   lowercase__      )


if __name__ == "__main__":
       main()





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""GPTSw3Tokenizer"""]


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



