




'''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 __UpperCAmelCase	(   _UpperCAmelCase		:   str						)      ->      int:
			__snake_case			=		fname.split(os.path.sep						)[-1]
			return re.search(R"^(.*)_\d+\.jpg$"		,				_UpperCAmelCase						).groups()[0]



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Optional[Any]      ,		a_						:    str      ,		a_						:    Union[str, Any]=None      ,		a_						:    str=None							):



						"""simple docstring"""




						__snake_case			=		file_names
						__snake_case			=		image_transform
						__snake_case			=		label_to_id





			def __len__(      self						:    str							):



						"""simple docstring"""




						return len(self.file_names							)






			def __getitem__(      self						:    int      ,		a_						:    str							):



						"""simple docstring"""




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







def __UpperCAmelCase	(   _UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Dict						)      ->      List[Any]:
			# Initialize accelerator
			if args.with_tracking:
						__snake_case			=		Accelerator(
						    cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				log_with="all"		,				project_dir=args.project_dir						)
			else:
						__snake_case			=		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision						)

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

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

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

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

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

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

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

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

			# Instantiate dataloaders.
			__snake_case			=		DataLoader(_UpperCAmelCase		,				shuffle=_UpperCAmelCase		,				batch_size=_UpperCAmelCase		,				num_workers=4						)
			__snake_case			=		DataLoader(_UpperCAmelCase		,				shuffle=_UpperCAmelCase		,				batch_size=_UpperCAmelCase		,				num_workers=4						)

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

			# 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).
			__snake_case			=		model.to(accelerator.device						)

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

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

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

			# Instantiate learning rate scheduler
			__snake_case			=		OneCycleLR(optimizer=_UpperCAmelCase		,				max_lr=_UpperCAmelCase		,				epochs=_UpperCAmelCase		,				steps_per_epoch=len(_UpperCAmelCase						)						)

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

						if "epoch" in training_difference:
									__snake_case			=		int(training_difference.replace("epoch_"		,				""						)						) + 1
									__snake_case			=		None
						else:
									__snake_case			=		int(training_difference.replace("step_"		,				""						)						)
									__snake_case			=		resume_step // len(_UpperCAmelCase						)
									resume_step -= starting_epoch * len(_UpperCAmelCase						)

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

						__snake_case			=		accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''						)
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": 1_00 * eval_metric,
									        "train_loss": total_loss.item() / len(_UpperCAmelCase						),
									        "epoch": epoch,
									    }		,				step=_UpperCAmelCase		,				)
						if checkpointing_steps == "epoch":
									__snake_case			=		F'''epoch_{epoch}'''
									if args.output_dir is not None:
												__snake_case			=		os.path.join(args.output_dir		,				_UpperCAmelCase						)
									accelerator.save_state(_UpperCAmelCase						)

			if args.with_tracking:
						accelerator.end_training()


def __UpperCAmelCase	(   )      ->      str:
			__snake_case			=		argparse.ArgumentParser(description="Simple example of training script."						)
			parser.add_argument("--data_dir"		,				required=_UpperCAmelCase		,				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=_UpperCAmelCase		,				default=_UpperCAmelCase		,				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=_UpperCAmelCase		,				default=_UpperCAmelCase		,				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=_UpperCAmelCase		,				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=_UpperCAmelCase		,				default=_UpperCAmelCase		,				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=_UpperCAmelCase		,				default="logs"		,				help="Location on where to store experiment tracking logs` and relevent project information"		,				)
			__snake_case			=		parser.parse_args()
			__snake_case			=		{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
			training_function(_UpperCAmelCase		,				_UpperCAmelCase						)


if __name__ == "__main__":
						main()













import numpy as np

import datasets


A__      :     int       =					'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

A__      :     Optional[int]       =					'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

A__      :     Optional[int]       =					'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''X''': datasets.Sequence(datasets.Value('''float''',    id='''sequence'''    ),    id='''X'''    ),
								        }    ),    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								# convert to numpy arrays
								lowercase__  					=     np.array(lowerCamelCase    )
								lowercase__  					=     np.array(lowerCamelCase    )

								# Assert that arrays are 2D
								if len(X.shape    ) != 2:
												raise ValueError('''Expected `X` to be a 2D vector'''    )
								if len(reference_distribution.shape    ) != 2:
												raise ValueError('''Expected `reference_distribution` to be a 2D vector'''    )
								if reference_distribution.shape[0] < 2:
												raise ValueError(
												    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''    )

								# Get mahalanobis distance for each prediction
								lowercase__  					=     X - np.mean(lowerCamelCase    )
								lowercase__  					=     np.cov(reference_distribution.T    )
								try:
												lowercase__  					=     np.linalg.inv(lowerCamelCase    )
								except np.linalg.LinAlgError:
												lowercase__  					=     np.linalg.pinv(lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    X_minus_mu.T    ).diagonal()

								return {"mahalanobis": mahal_dist}




