'''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 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							_lowercase				=	fname.split(os.path.sep					)[-1]
							return re.search(r"""^(.*)_\d+\.jpg$""" ,_A					).groups()[0]


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														_lowercase				=	file_names
														_lowercase				=	image_transform
														_lowercase				=	label_to_id



							def __len__(    self  ):







														'''simple docstring'''






														return len(self.file_names  )



							def __getitem__(    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






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






def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							_lowercase				=	Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision					)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowercase				=	config["""lr"""]
							_lowercase				=	int(config["""num_epochs"""]					)
							_lowercase				=	int(config["""seed"""]					)
							_lowercase				=	int(config["""batch_size"""]					)
							_lowercase				=	config["""image_size"""]
							if not isinstance(_A ,(list, tuple)					):
														_lowercase				=	(image_size, image_size)

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

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

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

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

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

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

							# Instantiate dataloaders.
							_lowercase				=	DataLoader(_A ,shuffle=_A ,batch_size=_A ,num_workers=4					)
							_lowercase				=	DataLoader(_A ,shuffle=_A ,batch_size=_A ,num_workers=4					)

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

							# 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				=	model.to(accelerator.device					)

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

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

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

							# Instantiate learning rate scheduler
							_lowercase				=	OneCycleLR(optimizer=_A ,max_lr=_A ,epochs=_A ,steps_per_epoch=len(_A					)					)

							# 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				=	accelerator.prepare(
							    _A ,_A ,_A ,_A ,_A					)

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

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

														_lowercase				=	accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''					)


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							_lowercase				=	argparse.ArgumentParser(description="""Simple example of training script."""					)
							parser.add_argument("""--data_dir""" ,required=_A ,help="""The data folder on disk."""					)
							parser.add_argument(
							    """--mixed_precision""" ,type=_A ,default=_A ,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=_A ,default=_A ,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."""					)
							_lowercase				=	parser.parse_args()
							_lowercase				=	{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(_A ,_A					)


if __name__ == "__main__":
				main()



import math
from timeit import timeit


def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							_lowercase				=	0
							_lowercase				=	abs(_A					)
							while True:
														_lowercase				=	n // 10
														digits += 1
														if n == 0:
																					break
							return digits


def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							return 1 if n == 0 else math.floor(math.log(abs(_A					) ,10					) + 1					)


def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							return len(str(abs(_A					)					)					)


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							from collections.abc import Callable

							def benchmark_a_function(_A ,_A					) -> None:
														_lowercase				=	f'''{func.__name__}({value})'''
														_lowercase				=	timeit(f'''__main__.{call}''' ,setup="""import __main__"""					)
														print(f'''{call}: {func(_A					)} -- {timing} seconds'''					)

							for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
														for func in (num_digits, num_digits_fast, num_digits_faster):
																					benchmark_a_function(_A ,_A					)
														print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()


