

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  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       fname.split(os.path.sep)[-1]
	return re.search(r"^(.*)_\d+\.jpg$"							,			SCREAMING_SNAKE_CASE__).groups()[0]






class _UpperCamelCase							(							_UpperCAmelCase   ):
		'''simple docstring'''







		def __init__(			self							: Tuple      ,      a							: Any      ,      a							: str=None      ,      a							: Union[str, Any]=None	)							->       Dict:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       file_names
			SCREAMING_SNAKE_CASE   :      Tuple				     =       image_transform
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       label_to_id
		def __len__(			self							: Any	)							->       Dict:

			"""simple docstring"""

			return len(self.file_names	)






		def __getitem__(			self							: str      ,      a							: Union[str, Any]	)							->       List[str]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.file_names[idx]
			SCREAMING_SNAKE_CASE   :      Dict				     =       PIL.Image.open(lowercase__	)
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       raw_image.convert("RGB"	)
			if self.image_transform is not None:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_transform(lowercase__	)
			SCREAMING_SNAKE_CASE   :      Dict				     =       extract_label(lowercase__	)
			if self.label_to_id is not None:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.label_to_id[label]
			return {"image": image, "label": label}

def  lowerCamelCase__ (					_a							,			_a):





	if args.with_tracking:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator(
		    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			log_with="all"							,			project_dir=args.project_dir)
	else:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       Accelerator(cpu=args.cpu							,			mixed_precision=args.mixed_precision)

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

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

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

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

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

	# Split our filenames between train and validation
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.random.permutation(len(SCREAMING_SNAKE_CASE__))
	SCREAMING_SNAKE_CASE   :      Dict				     =       int(0.8 * len(SCREAMING_SNAKE_CASE__))
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       random_perm[:cut]
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       random_perm[cut:]

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

	# For evaluation, we use a deterministic Resize
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Compose([Resize(SCREAMING_SNAKE_CASE__), ToTensor()])
	SCREAMING_SNAKE_CASE   :      str				     =       PetsDataset([file_names[i] for i in eval_split]							,			image_transform=SCREAMING_SNAKE_CASE__							,			label_to_id=SCREAMING_SNAKE_CASE__)

	# Instantiate dataloaders.
	SCREAMING_SNAKE_CASE   :      int				     =       DataLoader(SCREAMING_SNAKE_CASE__							,			shuffle=SCREAMING_SNAKE_CASE__							,			batch_size=SCREAMING_SNAKE_CASE__							,			num_workers=4)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       DataLoader(SCREAMING_SNAKE_CASE__							,			shuffle=SCREAMING_SNAKE_CASE__							,			batch_size=SCREAMING_SNAKE_CASE__							,			num_workers=4)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       create_model("resnet50d"							,			pretrained=SCREAMING_SNAKE_CASE__							,			num_classes=len(SCREAMING_SNAKE_CASE__))

	# 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).
	SCREAMING_SNAKE_CASE   :      Tuple				     =       model.to(accelerator.device)

	# Freezing the base model
	for param in model.parameters():
		SCREAMING_SNAKE_CASE   :      Any				     =       False
	for param in model.get_classifier().parameters():
		SCREAMING_SNAKE_CASE   :      List[str]				     =       True

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

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

	# Instantiate learning rate scheduler
	SCREAMING_SNAKE_CASE   :      Dict				     =       OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__							,			max_lr=SCREAMING_SNAKE_CASE__							,			epochs=SCREAMING_SNAKE_CASE__							,			steps_per_epoch=len(SCREAMING_SNAKE_CASE__))

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

		if "epoch" in training_difference:
			SCREAMING_SNAKE_CASE   :      Tuple				     =       int(training_difference.replace("epoch_"							,			"")) + 1
			SCREAMING_SNAKE_CASE   :      str				     =       None
		else:
			SCREAMING_SNAKE_CASE   :      Tuple				     =       int(training_difference.replace("step_"							,			""))
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       resume_step // len(SCREAMING_SNAKE_CASE__)
			resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__)

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

		SCREAMING_SNAKE_CASE   :      Tuple				     =       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(SCREAMING_SNAKE_CASE__),
			        "epoch": epoch,
			    }							,			step=SCREAMING_SNAKE_CASE__							,			)
		if checkpointing_steps == "epoch":
			SCREAMING_SNAKE_CASE   :      Dict				     =       f"epoch_{epoch}"
			if args.output_dir is not None:
				SCREAMING_SNAKE_CASE   :      Dict				     =       os.path.join(args.output_dir							,			SCREAMING_SNAKE_CASE__)
			accelerator.save_state(SCREAMING_SNAKE_CASE__)

	if args.with_tracking:
		accelerator.end_training()

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument("--data_dir"							,			required=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			default=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			default=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			default=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			default="logs"							,			help="Location on where to store experiment tracking logs` and relevent project information"							,			)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       parser.parse_args()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
	training_function(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
			main()


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)