

'''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 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     fname.split(os.path.sep	)[-1]
	return re.search(r"""^(.*)_\d+\.jpg$"""							,       snake_case_	).groups()[0]
class 					__a   (		_snake_case  ):

						def __init__(		self	:						Any    ,			lowercase__	:						Any    ,			lowercase__	:						List[str]=None    ,			lowercase__	:						Optional[Any]=None)						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     file_names
							_lowercase							=     image_transform
							_lowercase							=     label_to_id

						def __len__(		self	:						str)						->Dict:

							"""simple docstring"""



							return len(self.file_names)


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

							"""simple docstring"""



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






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_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(snake_case_							,       (list, tuple)	):
		_lowercase							=     (image_size, image_size)

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

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

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

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

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

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

	# Instantiate dataloaders.
	_lowercase							=     DataLoader(snake_case_							,       shuffle=snake_case_							,       batch_size=snake_case_							,       num_workers=4	)
	_lowercase							=     DataLoader(snake_case_							,       shuffle=snake_case_							,       batch_size=snake_case_							,       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=snake_case_							,       num_classes=len(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).
	_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=snake_case_							,       max_lr=snake_case_							,       epochs=snake_case_							,       steps_per_epoch=len(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.
	_lowercase		,		_lowercase		,		_lowercase		,		_lowercase		,		_lowercase							=     accelerator.prepare(
	    snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	)

	# Now we train the model
	for epoch in range(snake_case_	):
		model.train()
		for step, batch in enumerate(snake_case_	):
			# 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(snake_case_	)
			_lowercase							=     torch.nn.functional.cross_entropy(snake_case_							,       batch["""label"""]	)
			accelerator.backward(snake_case_	)
			optimizer.step()
			lr_scheduler.step()
			optimizer.zero_grad()

		model.eval()
		_lowercase							=     0
		_lowercase							=     0
		for _, batch in enumerate(snake_case_	):
			# 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(snake_case_	)
			_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 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     argparse.ArgumentParser(description="""Simple example of training script."""	)
	parser.add_argument("""--data_dir"""							,       required=snake_case_							,       help="""The data folder on disk."""	)
	parser.add_argument(
	    """--mixed_precision"""							,       type=snake_case_							,       default=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(
	    """--checkpointing_steps"""							,       type=snake_case_							,       default=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("""--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(snake_case_							,       snake_case_	)


if __name__ == "__main__":
							main()






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




