


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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       fname.split(os.path.sep)[-1]
	return re.search(R"^(.*)_\d+\.jpg$"      ,					lowerCAmelCase_).groups()[0]





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_=None				,							a_=None     ):
							lowerCamelCase_			:							List[str]      =       file_names
							lowerCamelCase_			:							Optional[int]      =       image_transform
							lowerCamelCase_			:							Any      =       label_to_id




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




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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							int      =       Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision)

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase_			:							Tuple      =       config["lr"]
	lowerCamelCase_			:							Optional[Any]      =       int(config["num_epochs"])
	lowerCamelCase_			:							List[str]      =       int(config["seed"])
	lowerCamelCase_			:							Dict      =       int(config["batch_size"])
	lowerCamelCase_			:							Any      =       config["image_size"]
	if not isinstance(lowerCAmelCase_      ,					(list, tuple)):
		lowerCamelCase_			:							Optional[Any]      =       (image_size, image_size)

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

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

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

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

	# Instantiate dataloaders.
	lowerCamelCase_			:							Dict      =       DataLoader(lowerCAmelCase_      ,					shuffle=lowerCAmelCase_      ,					batch_size=lowerCAmelCase_      ,					num_workers=4)
	lowerCamelCase_			:							Optional[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)
	lowerCamelCase_			:							Tuple      =       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).
	lowerCamelCase_			:							Any      =       model.to(accelerator.device)

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

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

	# Instantiate optimizer
	lowerCamelCase_			:							Optional[int]      =       torch.optim.Adam(params=model.parameters()      ,					lr=lr / 25)

	# Instantiate learning rate scheduler
	lowerCamelCase_			:							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.
	lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							Dict      =       accelerator.prepare(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)

	# Now we train the model
	for epoch in range(lowerCAmelCase_):
		model.train()
		for step, batch in enumerate(lowerCAmelCase_):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			lowerCamelCase_			:							Any      =       {k: v.to(accelerator.device) for k, v in batch.items()}
			lowerCamelCase_			:							Any      =       (batch["image"] - mean) / std
			lowerCamelCase_			:							str      =       model(lowerCAmelCase_)
			lowerCamelCase_			:							Union[str, Any]      =       torch.nn.functional.cross_entropy(lowerCAmelCase_      ,					batch["label"])
			accelerator.backward(lowerCAmelCase_)
			optimizer.step()
			lr_scheduler.step()
			optimizer.zero_grad()

		model.eval()
		lowerCamelCase_			:							Optional[int]      =       0
		lowerCamelCase_			:							Optional[Any]      =       0
		for _, batch in enumerate(lowerCAmelCase_):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			lowerCamelCase_			:							Optional[Any]      =       {k: v.to(accelerator.device) for k, v in batch.items()}
			lowerCamelCase_			:							Tuple      =       (batch["image"] - mean) / std
			with torch.no_grad():
				lowerCamelCase_			:							Optional[int]      =       model(lowerCAmelCase_)
			lowerCamelCase_			:							Tuple      =       outputs.argmax(dim=-1)
			lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       accelerator.gather_for_metrics((predictions, batch["label"]))
			lowerCamelCase_			:							Optional[int]      =       predictions == references
			num_elems += accurate_preds.shape[0]
			accurate += accurate_preds.long().sum()

		lowerCamelCase_			:							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}""")







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument("--data_dir"      ,					required=lowerCAmelCase_      ,					help="The data folder on disk.")
	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(
	    "--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("--cpu"      ,					action="store_true"      ,					help="If passed, will train on the CPU.")
	lowerCamelCase_			:							Optional[Any]      =       parser.parse_args()
	lowerCamelCase_			:							Union[str, Any]      =       {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
	training_function(lowerCAmelCase_      ,					lowerCAmelCase_)


if __name__ == "__main__":
							main()





def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

