






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__ (__lowercase							:Optional[Any]   )		-> List[str]:
				_A	:				List[str]   =      fname.split(os.path.sep   )[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''					,  __lowercase   ).groups()[0]






class 						UpperCAmelCase__ (							__snake_case							):







		def __init__(					self       ,A__       ,A__=None       ,A__=None		):
						_A	:				Optional[int]   =      file_names
						_A	:				Any   =      image_transform
						_A	:				Optional[int]   =      label_to_id







		def __len__(					self		):
						return len(self.file_names		)







		def __getitem__(					self       ,A__		):
						_A	:				Any   =      self.file_names[idx]
						_A	:				str   =      PIL.Image.open(A__		)
						_A	:				Optional[int]   =      raw_image.convert('''RGB'''		)
						if self.image_transform is not None:
										_A	:				List[Any]   =      self.image_transform(A__		)
						_A	:				Optional[Any]   =      extract_label(A__		)
						if self.label_to_id is not None:
										_A	:				Optional[int]   =      self.label_to_id[label]
						return {"image": image, "label": label}



def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Dict:
				# Initialize accelerator
				if args.with_tracking:
								_A	:				str   =      Accelerator(
								    cpu=args.cpu					,  mixed_precision=args.mixed_precision					,  log_with='''all'''					,  project_dir=args.project_dir   )
				else:
								_A	:				Union[str, Any]   =      Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision   )

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

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

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

				# Build the label correspondences
				_A	:				int   =      [extract_label(__lowercase   ) for fname in file_names]
				_A	:				Any   =      list(set(__lowercase   )   )
				id_to_label.sort()
				_A	:				Dict   =      {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
				_A	:				List[str]   =      np.random.permutation(len(__lowercase   )   )
				_A	:				List[Any]   =      int(0.8 * len(__lowercase   )   )
				_A	:				Dict   =      random_perm[:cut]
				_A	:				str   =      random_perm[cut:]

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

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

				# Instantiate dataloaders.
				_A	:				Optional[int]   =      DataLoader(__lowercase					,  shuffle=__lowercase					,  batch_size=__lowercase					,  num_workers=4   )
				_A	:				Optional[int]   =      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)
				_A	:				int   =      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).
				_A	:				Dict   =      model.to(accelerator.device   )

				# Freezing the base model
				for param in model.parameters():
								_A	:				Any   =      False
				for param in model.get_classifier().parameters():
								_A	:				Optional[int]   =      True

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

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

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

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

								_A	:				List[str]   =      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":
												_A	:				Optional[int]   =      f"""epoch_{epoch}"""
												if args.output_dir is not None:
																_A	:				Optional[int]   =      os.path.join(args.output_dir					,  __lowercase   )
												accelerator.save_state(__lowercase   )

				if args.with_tracking:
								accelerator.end_training()



def  a__ ()		-> Dict:
				_A	:				Dict   =      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'''					,  )
				_A	:				Optional[Any]   =      parser.parse_args()
				_A	:				Any   =      {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(__lowercase					,  __lowercase   )


if __name__ == "__main__":
	main()











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



