


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 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[int] 						=						fname.split(os.path.sep					)[-1]
    return re.search(R'^(.*)_\d+\.jpg$'				,					lowerCamelCase_					).groups()[0]






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						file_names
           _lowercase			:				List[str] 						=						image_transform
           _lowercase			:				List[Any] 						=						label_to_id
       def __len__(   self)			->  str:




           """simple docstring"""

           return len(self.file_names)




       def __getitem__(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.file_names[idx]
           _lowercase			:				Tuple 						=						PIL.Image.open(lowerCamelCase)
           _lowercase			:				Any 						=						raw_image.convert('RGB')
           if self.image_transform is not None:
               _lowercase			:				Dict 						=						self.image_transform(lowerCamelCase)
           _lowercase			:				Dict 						=						extract_label(lowerCamelCase)
           if self.label_to_id is not None:
               _lowercase			:				List[Any] 						=						self.label_to_id[label]
           return {"image": image, "label": label}



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    # Initialize accelerator
    if args.with_tracking:
        _lowercase			:				int 						=						Accelerator(
            cpu=args.cpu				,					mixed_precision=args.mixed_precision				,					log_with='all'				,					project_dir=args.project_dir					)
    else:
        _lowercase			:				Tuple 						=						Accelerator(cpu=args.cpu				,					mixed_precision=args.mixed_precision					)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase			:				List[str] 						=						config['lr']
    _lowercase			:				Optional[int] 						=						int(config['num_epochs']					)
    _lowercase			:				Union[str, Any] 						=						int(config['seed']					)
    _lowercase			:				Dict 						=						int(config['batch_size']					)
    _lowercase			:				List[str] 						=						config['image_size']
    if not isinstance(lowerCamelCase_				,					(list, tuple)					):
        _lowercase			:				List[str] 						=						(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":
            _lowercase			:				Optional[Any] 						=						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _lowercase			:				int 						=						int(args.checkpointing_steps					)
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
    else:
        _lowercase			:				Union[str, Any] 						=						None

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

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

    # Build the label correspondences
    _lowercase			:				List[str] 						=						[extract_label(lowerCamelCase_					) for fname in file_names]
    _lowercase			:				Dict 						=						list(set(lowerCamelCase_					)					)
    id_to_label.sort()
    _lowercase			:				Optional[int] 						=						{lbl: i for i, lbl in enumerate(lowerCamelCase_					)}

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

    # Split our filenames between train and validation
    _lowercase			:				Optional[Any] 						=						np.random.permutation(len(lowerCamelCase_					)					)
    _lowercase			:				str 						=						int(0.8 * len(lowerCamelCase_					)					)
    _lowercase			:				Any 						=						random_perm[:cut]
    _lowercase			:				Optional[int] 						=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    _lowercase			:				Optional[int] 						=						Compose([RandomResizedCrop(lowerCamelCase_				,					scale=(0.5, 1.0)					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset(
        [file_names[i] for i in train_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # For evaluation, we use a deterministic Resize
    _lowercase			:				Union[str, Any] 						=						Compose([Resize(lowerCamelCase_					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset([file_names[i] for i in eval_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # Instantiate dataloaders.
    _lowercase			:				List[str] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)
    _lowercase			:				List[Any] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase			:				List[str] 						=						create_model('resnet50d'				,					pretrained=lowerCamelCase_				,					num_classes=len(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).
    _lowercase			:				Union[str, Any] 						=						model.to(accelerator.device					)

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

    # We normalize the batches of images to be a bit faster.
    _lowercase			:				int 						=						torch.tensor(model.default_cfg['mean']					)[None, :, None, None].to(accelerator.device					)
    _lowercase			:				Union[str, Any] 						=						torch.tensor(model.default_cfg['std']					)[None, :, None, None].to(accelerator.device					)

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

    # Instantiate learning rate scheduler
    _lowercase			:				List[Any] 						=						OneCycleLR(optimizer=lowerCamelCase_				,					max_lr=lowerCamelCase_				,					epochs=lowerCamelCase_				,					steps_per_epoch=len(lowerCamelCase_					)					)

    # 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			:				Union[str, Any] 						=						accelerator.prepare(
        lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    # We need to keep track of how many total steps we have iterated over
    _lowercase			:				Optional[int] 						=						0
    # We also need to keep track of the starting epoch so files are named properly
    _lowercase			:				List[Any] 						=						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					)
            _lowercase			:				Optional[Any] 						=						os.path.basename(args.resume_from_checkpoint					)
        else:
            # Get the most recent checkpoint
            _lowercase			:				List[str] 						=						[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
            dirs.sort(key=os.path.getctime					)
            _lowercase			:				str 						=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _lowercase			:				Union[str, Any] 						=						os.path.splitext(lowerCamelCase_					)[0]

        if "epoch" in training_difference:
            _lowercase			:				Any 						=						int(training_difference.replace('epoch_'				,					''					)					) + 1
            _lowercase			:				Optional[Any] 						=						None
        else:
            _lowercase			:				Dict 						=						int(training_difference.replace('step_'				,					''					)					)
            _lowercase			:				Any 						=						resume_step // len(lowerCamelCase_					)
            resume_step -= starting_epoch * len(lowerCamelCase_					)

    # Now we train the model
    for epoch in range(lowerCamelCase_				,					lowerCamelCase_					):
        model.train()
        if args.with_tracking:
            _lowercase			:				Tuple 						=						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
            _lowercase			:				Union[str, Any] 						=						accelerator.skip_first_batches(lowerCamelCase_				,					lowerCamelCase_					)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            _lowercase			:				Any 						=						train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Dict 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            _lowercase			:				Optional[int] 						=						model(lowerCamelCase_					)
            _lowercase			:				Union[str, Any] 						=						torch.nn.functional.cross_entropy(lowerCamelCase_				,					batch['label']					)
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowerCamelCase_					)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(lowerCamelCase_				,					lowerCamelCase_					):
                _lowercase			:				Tuple 						=						F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _lowercase			:				Tuple 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
                    accelerator.save_state(lowerCamelCase_					)
        model.eval()
        _lowercase			:				List[str] 						=						0
        _lowercase			:				List[Any] 						=						0
        for step, batch in enumerate(lowerCamelCase_					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Union[str, Any] 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            with torch.no_grad():
                _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
            _lowercase			:				Any 						=						outputs.argmax(dim=-1					)
            _lowercase						,							_lowercase			:				Optional[int] 						=						accelerator.gather_for_metrics((predictions, batch['label'])					)
            _lowercase			:				int 						=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _lowercase			:				Union[str, Any] 						=						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(lowerCamelCase_					),
                    'epoch': epoch,
                }				,					step=lowerCamelCase_				,					)
        if checkpointing_steps == "epoch":
            _lowercase			:				Any 						=						F'''epoch_{epoch}'''
            if args.output_dir is not None:
                _lowercase			:				Dict 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
            accelerator.save_state(lowerCamelCase_					)

    if args.with_tracking:
        accelerator.end_training()



def 							UpperCamelCase_( )       ->		Optional[Any]:
    _lowercase			:				Tuple 						=						argparse.ArgumentParser(description='Simple example of training script.'					)
    parser.add_argument('--data_dir'				,					required=lowerCamelCase_				,					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=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.'					)
    parser.add_argument(
        '--checkpointing_steps'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					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=lowerCamelCase_				,					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=lowerCamelCase_				,					default=lowerCamelCase_				,					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=lowerCamelCase_				,					default='logs'				,					help='Location on where to store experiment tracking logs` and relevent project information'				,					)
    _lowercase			:				str 						=						parser.parse_args()
    _lowercase			:				List[str] 						=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
    training_function(lowerCamelCase_				,					lowerCamelCase_					)


if __name__ == "__main__":
 main()




"""simple docstring"""


import os
from datetime import datetime as dt

from github import Github


A_    							=					[
    """good first issue""",
    """feature request""",
    """wip""",
]






def  lowercase						(	):
			lowerCamelCase_					=  Github(os.environ['''GITHUB_TOKEN''']						)
			lowerCamelCase_					=  g.get_repo('''huggingface/accelerate'''						)
			lowerCamelCase_					=  repo.get_issues(state='''open'''						)

			for issue in open_issues:
						lowerCamelCase_					=  sorted([comment for comment in issue.get_comments()]	,key=lambda lowerCAmelCase__						: i.created_at	,reverse=lowerCAmelCase__						)
						lowerCamelCase_					=  comments[0] if len(lowerCAmelCase__						) > 0 else None
						lowerCamelCase_					=  dt.utcnow()
						lowerCamelCase_					=  (current_time - issue.updated_at).days
						lowerCamelCase_					=  (current_time - issue.created_at).days
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and days_since_updated > 7
						    and days_since_creation >= 30
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
						):
									# Close issue since it has been 7 days of inactivity since bot mention.
									issue.edit(state='''closed'''						)
						elif (
						    days_since_updated > 23
						    and days_since_creation >= 30
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
						):
									# Add stale comment
									issue.create_comment(
									    '''This issue has been automatically marked as stale because it has not had '''
									    '''recent activity. If you think this still needs to be addressed '''
									    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
									    '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
									    '''are likely to be ignored.'''						)


if __name__ == "__main__":
						main()



