



'''simple docstring'''

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

class 							a__( _a     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							List[str]:
					snake_case__	     =file_names
					snake_case__	     =image_transform
					snake_case__	     =label_to_id






			def __len__(     self						)							->							str:
					return len(self.file_names						)






			def __getitem__(     self					,   _UpperCAmelCase						)							->							List[Any]:
					snake_case__	     =self.file_names[idx]
					snake_case__	     =PIL.Image.open(A_						)
					snake_case__	     =raw_image.convert('RGB'						)
					if self.image_transform is not None:
							snake_case__	     =self.image_transform(A_						)
					snake_case__	     =extract_label(A_						)
					if self.label_to_id is not None:
							snake_case__	     =self.label_to_id[label]
					return {"image": image, "label": label}






def 				a  (     UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    List[Any]			)       ->	Tuple:


		if args.with_tracking:
				snake_case__	     =Accelerator(
				    cpu=args.cpu      ,					mixed_precision=args.mixed_precision      ,					log_with='all'      ,					project_dir=args.project_dir			)
		else:
				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
		snake_case__	     =config['lr']
		snake_case__	     =int(config['num_epochs']			)
		snake_case__	     =int(config['seed']			)
		snake_case__	     =int(config['batch_size']			)
		snake_case__	     =config['image_size']
		if not isinstance(_lowercase      ,					(list, tuple)			):
				snake_case__	     =(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":
						snake_case__	     =args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						snake_case__	     =int(args.checkpointing_steps			)
				else:
						raise ValueError(
						    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
		else:
				snake_case__	     =None

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

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

		# Build the label correspondences
		snake_case__	     =[extract_label(_lowercase			) for fname in file_names]
		snake_case__	     =list(set(_lowercase			)			)
		id_to_label.sort()
		snake_case__	     ={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
		snake_case__	     =np.random.permutation(len(_lowercase			)			)
		snake_case__	     =int(0.8 * len(_lowercase			)			)
		snake_case__	     =random_perm[:cut]
		snake_case__	     =random_perm[cut:]

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

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

		# Instantiate dataloaders.
		snake_case__	     =DataLoader(_lowercase      ,					shuffle=_lowercase      ,					batch_size=_lowercase      ,					num_workers=4			)
		snake_case__	     =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)
		snake_case__	     =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).
		snake_case__	     =model.to(accelerator.device			)

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

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

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

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

				if "epoch" in training_difference:
						snake_case__	     =int(training_difference.replace('epoch_'      ,					''			)			) + 1
						snake_case__	     =None
				else:
						snake_case__	     =int(training_difference.replace('step_'      ,					''			)			)
						snake_case__	     =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:
						snake_case__	     =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
						snake_case__	     =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
						snake_case__	     =train_dataloader
				for batch in active_dataloader:
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case__	     ={k: v.to(accelerator.device			) for k, v in batch.items()}
						snake_case__	     =(batch['image'] - mean) / std
						snake_case__	     =model(_lowercase			)
						snake_case__	     =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			):
								snake_case__	     =f"""step_{overall_step}"""
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												snake_case__	     =os.path.join(args.output_dir      ,					_lowercase			)
										accelerator.save_state(_lowercase			)
				model.eval()
				snake_case__	     =0
				snake_case__	     =0
				for step, batch in enumerate(_lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case__	     ={k: v.to(accelerator.device			) for k, v in batch.items()}
						snake_case__	     =(batch['image'] - mean) / std
						with torch.no_grad():
								snake_case__	     =model(_lowercase			)
						snake_case__	     =outputs.argmax(dim=-1			)
						snake_case__     ,	snake_case__	     =accelerator.gather_for_metrics((predictions, batch['label'])			)
						snake_case__	     =predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				snake_case__	     =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":
						snake_case__	     =f"""epoch_{epoch}"""
						if args.output_dir is not None:
								snake_case__	     =os.path.join(args.output_dir      ,					_lowercase			)
						accelerator.save_state(_lowercase			)

		if args.with_tracking:
				accelerator.end_training()






def 				a  (     )       ->	Dict:
		snake_case__	     =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'      ,					)
		snake_case__	     =parser.parse_args()
		snake_case__	     ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
		training_function(_lowercase      ,					_lowercase			)


if __name__ == "__main__":
			main()








__snake_case   							=						{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__snake_case   							=						{value: key for key, value in encode_dict.items()}






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			''
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception('encode() accepts only letters of the alphabet and spaces' )
     return encoded






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     if set(_lowercase ) - {"A", "B", " "} != set():
          raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
     __UpperCamelCase =			''
     for word in coded.split():
          while len(_lowercase ) != 0:
               decoded += decode_dict[word[:5]]
               __UpperCamelCase =			word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


