Datasets:

anonymous347928
/

pcbm_metashift

import argparse
import pickle
import logging
from omegaconf import OmegaConf
import re
import random
import tarfile
from pydantic import BaseModel
from pathlib import Path

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def setup_parser():
    parser = argparse.ArgumentParser(description="Generate a domain shift dataset")
    parser.add_argument("--config", type=str, required=True, help="Path to config file")
    parser.add_argument(
        "--full_candidate_subsets_path",
        type=str,
        required=True,
        help="Path to full-candidate-subsets.pkl",
    )
    parser.add_argument(
        "--visual_genome_images_dir",
        type=str,
        required=True,
        help="Path to VisualGenome images directory allImages/images",
    )
    parser.add_argument(
        "--save_images",
        action=argparse.BooleanOptionalAction,
        required=True,
        help="Save images to output directory",
    )
    return parser


def get_ms_domain_name(obj: str, context: str) -> str:
    return f"{obj}({context})"


class DataSplits(BaseModel):
    train: dict[str, list[str]]
    test: dict[str, list[str]]


class MetashiftData(BaseModel):
    selected_classes: list[str]
    spurious_class: str
    train_context: str
    test_context: str
    data_splits: DataSplits


class MetashiftFactory(object):
    object_context_to_id: dict[str, list[int]]
    visual_genome_images_dir: str

    def __init__(
        self,
        full_candidate_subsets_path: str,
        visual_genome_images_dir: str,
    ):
        """
        full_candidate_subsets_path: Path to `full-candidate-subsets.pkl`
        visual_genome_images_dir: Path to VisualGenome images directory `allImages/images`
        """
        with open(full_candidate_subsets_path, "rb") as f:
            self.object_context_to_id = pickle.load(f)
        self.visual_genome_images_dir = visual_genome_images_dir

    def _get_all_domains_with_object(self, obj: str) -> set[str]:
        """Get all domains with given object and any context.
        Example:
            - _get_all_domains_with_object(table) => [table(dog), table(cat), ...]
        """
        return {
            key
            for key in self.object_context_to_id.keys()
            if re.match(f"^{obj}\\(.*\\)$", key)
        }

    def _get_all_image_ids_with_object(self, obj: str) -> set[str]:
        """Get all image ids with given object and any context.
        Example:
            - get_all_image_ids_with_object(table) => [id~table(dog), id~table(cat), ...]
            - where id~domain, means an image sampled from the given domain.
        """
        domains = self._get_all_domains_with_object(obj)
        return {_id for domain in domains for _id in self.object_context_to_id[domain]}

    def _get_image_ids(self, obj: str, context: str | None) -> set[str]:
        """Get image ids for the domain `obj(context)`."""
        if context is None:
            return self._get_all_image_ids_with_object(obj)
        else:
            return self.object_context_to_id[get_ms_domain_name(obj, context)]

    def _get_class_domains(
        self, domains_specification: dict[str, tuple[str, str | None]]
    ) -> dict[str, tuple[list[str], list[str]]]:
        """Get train and test image ids for the given domains specification."""
        domain_ids = dict()
        for cls, (train_context, test_context) in domains_specification.items():
            if train_context == test_context:
                ids = self._get_image_ids(cls, train_context)
                domain_ids[cls] = [ids, ids]
                logger.info(
                    f"{get_ms_domain_name(cls, train_context or '*')}: {len(ids)}"
                    " -> "
                    f"{get_ms_domain_name(cls, test_context or '*')}: {len(ids)}"
                )
            else:
                train_ids = self._get_image_ids(cls, train_context)
                test_ids = self._get_image_ids(cls, test_context)
                domain_ids[cls] = [train_ids, test_ids]
                logger.info(
                    f"{get_ms_domain_name(cls, train_context or '*')}: {len(train_ids)}"
                    " -> "
                    f"{get_ms_domain_name(cls, test_context or '*')}: {len(test_ids)}"
                )
        return domain_ids

    def _get_unique_ids_from_domains(
        self, domains: dict[str, tuple[list[str], list[str]]]
    ) -> set[str]:
        """Get unique image ids from the given domains."""
        unique_ids: set[str] = set()
        for _, (train_ids, test_ids) in domains.items():
            unique_ids = unique_ids | set(train_ids) | set(test_ids)
        return unique_ids

    def _interactive_sample(
        self,
        image_ids: set[str],
        seed: float,
        num_images: int,
        class_name: str,
        spurious_context: str,
    ) -> set[str]:
        from tkinter import Tk, Button, Label
        from PIL import Image, ImageTk

        """ 
        Given a list of image paths, `image_ids`,
        draw tkinter user interface that displays each image one by one
        along with a button that,
        allows the user to select that image.
        When `num_images` images are selected,
        stop  and return the selected image ids.
        """
        UNVISITED, SELECTED, REJECTED = 0, 1, -1
        CANVAS_WIDTH, CANVAS_HEIGHT, IMAGE_SIZE = 400, 300, 224

        image_ids: list[str] = sorted(
            list(image_ids)
        )  # Ensure canonical ordering for determinism
        status: list[int] = [UNVISITED] * len(image_ids)
        cnt: dict[int, int] = {SELECTED: 0, REJECTED: 0}
        ix: int = -1
        random.Random(seed).shuffle(image_ids)
        root = Tk()
        root.title("Select images")
        root.geometry(f"{CANVAS_WIDTH}x{CANVAS_HEIGHT}")  # Width x Height

        def get_next(s: int):
            """Mark current image as `s` and move to the next image."""
            nonlocal status, ix, image_ids
            status[ix] = s
            cnt[s] += 1
            assert s in [SELECTED, REJECTED], "Cannot mark image as unvisited"
            if s == SELECTED:
                logger.info(f"SELECT IMAGE {image_ids[ix]}")
            next_image()

        def draw_image():
            nonlocal image_label, image_ids, ix
            image_id = image_ids[ix]
            logging.info(f"Drawing image {image_id}")
            image = Image.open(Path(self.visual_genome_images_dir) / f"{image_id}.jpg")
            image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
            image = ImageTk.PhotoImage(image)
            image_label.config(image=image)
            image_label.image = image

        def undo():
            nonlocal ix, status, cnt, image_ids
            ix = (ix - 1) % len(image_ids)
            if status[ix] == UNVISITED: # do nothing
                ix = (ix + 1) % len(image_ids)
                return
            cnt[status[ix]] -= 1
            status[ix] = UNVISITED
            draw_image()
            draw_progbars()

        def draw_progbars():
            nonlocal progress_label, position_label, ix, status, num_images
            progress_label.config(
                text=f"Selected: {sum(s for s in status if s == SELECTED)}/{num_images}"
            )
            position_label.config(text=f"Position: {ix}/{len(status)}")

        def next_image():
            nonlocal ix, status, cnt, num_images, image_ids
            if cnt[SELECTED] == num_images:
                root.destroy()
                return
            for dx in range(1, len(image_ids)):
                # We can select previously rejected image, in case we run out of unvisited
                if status[(ix + dx) % len(image_ids)] in [UNVISITED, REJECTED]:
                    break
            assert dx != 0, "No free image left"
            ix = (ix + dx) % len(image_ids)
            draw_image()
            draw_progbars()

        image_label = Label(root)
        image_label.pack()
        image_label.place(x=(CANVAS_WIDTH - IMAGE_SIZE) // 2, y=0)
        select_btn = Button(root, text="Select", command=lambda: get_next(SELECTED))
        reject_btn = Button(root, text="Reject", command=lambda: get_next(REJECTED))
        undo_btn = Button(root, text="Undo", command=undo)
        progress_label = Label(root, text=f"Selected: 0/{num_images}")
        position_label = Label(root, text=f"Position: 0/{len(image_ids)}")
        class_label = Label(root, text=f"{class_name}({spurious_context})")
        class_label.pack()
        class_label.place(x=170, y=250)
        position_label.pack()
        position_label.place(x=0, y=250)
        progress_label.pack()
        progress_label.place(x=300, y=250)
        select_btn.pack()
        select_btn.place(x=85, y=225)
        reject_btn.pack()
        reject_btn.place(x=165, y=225)
        undo_btn.pack()
        undo_btn.place(x=250, y=225)
        next_image()
        root.mainloop()

        sampled_images: set[str] = set()
        for i, s in enumerate(status):
            if s == SELECTED:
                sampled_images.add(image_ids[i])
        return sampled_images

    def _sample_from_domains(
        self,
        seed: int,
        domains: dict[str, tuple[list[str], list[str]]],
        num_train_images_per_class: int,
        num_test_images_per_class: int,
        spurious_class: str,
        train_spurious_context: str,
        test_spurious_context: str,
    ) -> dict[str, tuple[list[str], list[str]]]:
        """Return sampled domain data from the given full domains."""
        # Maintain order of original domains
        sampled_domains = {cls: (set(), set()) for cls in domains.keys()}
        # First process the spurious class, to maximize the number of images available for it
        classes = list(domains.keys())
        spurious_class_ix = classes.index(spurious_class)
        classes[0], classes[spurious_class_ix] = classes[spurious_class_ix], classes[0]
        sampled_ids = set()

        for cls in classes:
            train_ids, test_ids = domains[cls]
            try:
                if cls == spurious_class:
                    train_ids = train_ids - sampled_ids
                    sampled_train_ids = self._interactive_sample(
                        image_ids=train_ids,
                        seed=seed,
                        num_images=num_train_images_per_class,
                        class_name=spurious_class,
                        spurious_context=train_spurious_context,
                    )
                    sampled_ids = sampled_ids | set(sampled_train_ids)
                    test_ids = test_ids - sampled_ids
                    sampled_test_ids = self._interactive_sample(
                        image_ids=test_ids,
                        seed=seed,
                        num_images=num_test_images_per_class,
                        class_name=spurious_class,
                        spurious_context=test_spurious_context,
                    )
                    sampled_ids = sampled_ids | set(sampled_test_ids)
                else:
                    train_ids = train_ids - sampled_ids
                    sampled_train_ids = random.Random(seed).sample(
                        sorted(list(train_ids)), num_train_images_per_class
                    )
                    sampled_ids = sampled_ids | set(sampled_train_ids)
                    test_ids = test_ids - sampled_ids
                    sampled_test_ids = random.Random(seed).sample(
                        sorted(list(test_ids)), num_test_images_per_class
                    )
                    sampled_ids = sampled_ids | set(sampled_test_ids)
            except ValueError:
                logger.error(
                    f"{cls}: {len(train_ids)} train images, {len(test_ids)} test images"
                )
                raise Exception("Not enough images for this class")
            sampled_domains[cls] = (sampled_train_ids, sampled_test_ids)
        return sampled_domains

    def create(
        self,
        seed: int,
        selected_classes: list[str],
        spurious_class: str,
        train_spurious_context: str,
        test_spurious_context: str,
        num_train_images_per_class: int,
        num_test_images_per_class: int,
    ) -> MetashiftData:
        """Return (metadata, data) splits for the given data shift."""
        domains_specification = {
            **{cls: (None, None) for cls in selected_classes},
            spurious_class: (
                train_spurious_context,
                test_spurious_context,
            ),  # overwrite spurious_class
        }
        domains = self._get_class_domains(domains_specification)
        logger.info(
            f"Total number of images: {len(self._get_unique_ids_from_domains(domains))}"
        )
        sampled_domains = self._sample_from_domains(
            seed=seed,
            domains=domains,
            num_train_images_per_class=num_train_images_per_class,
            num_test_images_per_class=num_test_images_per_class,
            spurious_class=spurious_class,
            train_spurious_context=train_spurious_context,
            test_spurious_context=test_spurious_context,
        )
        logger.info(
            f"Total number of images after sampling: {len(self._get_unique_ids_from_domains(sampled_domains))}"
        )
        data_splits = {"train": dict(), "test": dict()}
        for cls, (train_ids, test_ids) in sampled_domains.items():
            data_splits["train"][cls] = train_ids
            data_splits["test"][cls] = test_ids

        return MetashiftData(
            selected_classes=selected_classes,
            spurious_class=spurious_class,
            train_context=train_spurious_context,
            test_context=test_spurious_context,
            data_splits=DataSplits(
                train=data_splits["train"],
                test=data_splits["test"],
            ),
        )

    def _get_unique_ids_from_info(self, info: dict[str, MetashiftData]):
        """Get unique ids from info struct."""
        unique_ids = set()
        for data in info.values():
            for ids in data.data_splits.train.values():
                unique_ids.update(ids)
            for ids in data.data_splits.test.values():
                unique_ids.update(ids)
        return unique_ids

    def _replace_ids_with_paths(
        self, info: dict[str, MetashiftData], data_path: Path
    ) -> MetashiftData:
        """Replace ids with paths."""
        new_data = dict()
        for dataset_name, data in info.items():
            for cls, ids in data.data_splits.train.items():
                data.data_splits.train[cls] = [
                    str(data_path / f"{_id}.jpg") for _id in ids
                ]
            for cls, ids in data.data_splits.test.items():
                data.data_splits.test[cls] = [
                    str(data_path / f"{_id}.jpg") for _id in ids
                ]
            new_data[dataset_name] = data
        return new_data

    def save_all(self, info: dict[str, MetashiftData], save_images: bool):
        """Save all datasets to the given directory."""
        out_path = Path(".")
        data_path = out_path / "data"
        data_path.mkdir(parents=True, exist_ok=True)
        scenarios_path = out_path / "scenarios" / "cherrypicked"
        scenarios_path.mkdir(parents=True, exist_ok=True)

        unique_ids = self._get_unique_ids_from_info(info)
        data = self._replace_ids_with_paths(info, data_path)
        for dataset_name, data in info.items():
            with open(scenarios_path / f"{dataset_name}.json", "w") as f:
                f.write(data.model_dump_json(indent=2))
        if save_images:
            with tarfile.open(data_path / "images.tar.gz", "w:gz") as tar:
                for _id in unique_ids:
                    tar.add(
                        Path(self.visual_genome_images_dir) / f"{_id}.jpg",
                    )


def get_dataset_name(task_name: str, experiment_name: str) -> str:
    return f"{task_name}_{experiment_name}"


def main():
    parser = setup_parser()
    args = parser.parse_args()
    config = OmegaConf.load(args.config)
    metashift_factory = MetashiftFactory(
        full_candidate_subsets_path=args.full_candidate_subsets_path,
        visual_genome_images_dir=args.visual_genome_images_dir,
    )
    info: dict[str, MetashiftData] = dict()
    for task_config in config.tasks:
        for experiment_config in task_config.experiments:
            data = metashift_factory.create(
                seed=task_config.seed,
                selected_classes=task_config.selected_classes,
                spurious_class=experiment_config.spurious_class,
                train_spurious_context=experiment_config.train_context,
                test_spurious_context=experiment_config.test_context,
                num_test_images_per_class=task_config.num_images_per_class_test,
                num_train_images_per_class=task_config.num_images_per_class_train,
            )
            dataset_name = get_dataset_name(task_config.name, experiment_config.name)
            assert dataset_name not in info
            info[dataset_name] = data

    metashift_factory.save_all(info, save_images=args.save_images)


if __name__ == "__main__":
    main()

	import argparse
	import pickle
	import logging
	from omegaconf import OmegaConf
	import re
	import random
	import tarfile
	from pydantic import BaseModel
	from pathlib import Path

	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)


	def setup_parser():
	parser = argparse.ArgumentParser(description="Generate a domain shift dataset")
	parser.add_argument("--config", type=str, required=True, help="Path to config file")
	parser.add_argument(
	"--full_candidate_subsets_path",
	type=str,
	required=True,
	help="Path to full-candidate-subsets.pkl",
	)
	parser.add_argument(
	"--visual_genome_images_dir",
	type=str,
	required=True,
	help="Path to VisualGenome images directory allImages/images",
	)
	parser.add_argument(
	"--save_images",
	action=argparse.BooleanOptionalAction,
	required=True,
	help="Save images to output directory",
	)
	return parser


	def get_ms_domain_name(obj: str, context: str) -> str:
	return f"{obj}({context})"


	class DataSplits(BaseModel):
	train: dict[str, list[str]]
	test: dict[str, list[str]]


	class MetashiftData(BaseModel):
	selected_classes: list[str]
	spurious_class: str
	train_context: str
	test_context: str
	data_splits: DataSplits


	class MetashiftFactory(object):
	object_context_to_id: dict[str, list[int]]
	visual_genome_images_dir: str

	def __init__(
	self,
	full_candidate_subsets_path: str,
	visual_genome_images_dir: str,
	):
	"""
	full_candidate_subsets_path: Path to `full-candidate-subsets.pkl`
	visual_genome_images_dir: Path to VisualGenome images directory `allImages/images`
	"""
	with open(full_candidate_subsets_path, "rb") as f:
	self.object_context_to_id = pickle.load(f)
	self.visual_genome_images_dir = visual_genome_images_dir

	def _get_all_domains_with_object(self, obj: str) -> set[str]:
	"""Get all domains with given object and any context.
	Example:
	- _get_all_domains_with_object(table) => [table(dog), table(cat), ...]
	"""
	return {
	key
	for key in self.object_context_to_id.keys()
	if re.match(f"^{obj}\\(.*\\)$", key)
	}

	def _get_all_image_ids_with_object(self, obj: str) -> set[str]:
	"""Get all image ids with given object and any context.
	Example:
	- get_all_image_ids_with_object(table) => [id~table(dog), id~table(cat), ...]
	- where id~domain, means an image sampled from the given domain.
	"""
	domains = self._get_all_domains_with_object(obj)
	return {_id for domain in domains for _id in self.object_context_to_id[domain]}

	def _get_image_ids(self, obj: str, context: str \| None) -> set[str]:
	"""Get image ids for the domain `obj(context)`."""
	if context is None:
	return self._get_all_image_ids_with_object(obj)
	else:
	return self.object_context_to_id[get_ms_domain_name(obj, context)]

	def _get_class_domains(
	self, domains_specification: dict[str, tuple[str, str \| None]]
	) -> dict[str, tuple[list[str], list[str]]]:
	"""Get train and test image ids for the given domains specification."""
	domain_ids = dict()
	for cls, (train_context, test_context) in domains_specification.items():
	if train_context == test_context:
	ids = self._get_image_ids(cls, train_context)
	domain_ids[cls] = [ids, ids]
	logger.info(
	f"{get_ms_domain_name(cls, train_context or '*')}: {len(ids)}"
	" -> "
	f"{get_ms_domain_name(cls, test_context or '*')}: {len(ids)}"
	)
	else:
	train_ids = self._get_image_ids(cls, train_context)
	test_ids = self._get_image_ids(cls, test_context)
	domain_ids[cls] = [train_ids, test_ids]
	logger.info(
	f"{get_ms_domain_name(cls, train_context or '*')}: {len(train_ids)}"
	" -> "
	f"{get_ms_domain_name(cls, test_context or '*')}: {len(test_ids)}"
	)
	return domain_ids

	def _get_unique_ids_from_domains(
	self, domains: dict[str, tuple[list[str], list[str]]]
	) -> set[str]:
	"""Get unique image ids from the given domains."""
	unique_ids: set[str] = set()
	for _, (train_ids, test_ids) in domains.items():
	unique_ids = unique_ids \| set(train_ids) \| set(test_ids)
	return unique_ids

	def _interactive_sample(
	self,
	image_ids: set[str],
	seed: float,
	num_images: int,
	class_name: str,
	spurious_context: str,
	) -> set[str]:
	from tkinter import Tk, Button, Label
	from PIL import Image, ImageTk

	"""
	Given a list of image paths, `image_ids`,
	draw tkinter user interface that displays each image one by one
	along with a button that,
	allows the user to select that image.
	When `num_images` images are selected,
	stop and return the selected image ids.
	"""
	UNVISITED, SELECTED, REJECTED = 0, 1, -1
	CANVAS_WIDTH, CANVAS_HEIGHT, IMAGE_SIZE = 400, 300, 224

	image_ids: list[str] = sorted(
	list(image_ids)
	) # Ensure canonical ordering for determinism
	status: list[int] = [UNVISITED] * len(image_ids)
	cnt: dict[int, int] = {SELECTED: 0, REJECTED: 0}
	ix: int = -1
	random.Random(seed).shuffle(image_ids)
	root = Tk()
	root.title("Select images")
	root.geometry(f"{CANVAS_WIDTH}x{CANVAS_HEIGHT}") # Width x Height

	def get_next(s: int):
	"""Mark current image as `s` and move to the next image."""
	nonlocal status, ix, image_ids
	status[ix] = s
	cnt[s] += 1
	assert s in [SELECTED, REJECTED], "Cannot mark image as unvisited"
	if s == SELECTED:
	logger.info(f"SELECT IMAGE {image_ids[ix]}")
	next_image()

	def draw_image():
	nonlocal image_label, image_ids, ix
	image_id = image_ids[ix]
	logging.info(f"Drawing image {image_id}")
	image = Image.open(Path(self.visual_genome_images_dir) / f"{image_id}.jpg")
	image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
	image = ImageTk.PhotoImage(image)
	image_label.config(image=image)
	image_label.image = image

	def undo():
	nonlocal ix, status, cnt, image_ids
	ix = (ix - 1) % len(image_ids)
	if status[ix] == UNVISITED: # do nothing
	ix = (ix + 1) % len(image_ids)
	return
	cnt[status[ix]] -= 1
	status[ix] = UNVISITED
	draw_image()
	draw_progbars()

	def draw_progbars():
	nonlocal progress_label, position_label, ix, status, num_images
	progress_label.config(
	text=f"Selected: {sum(s for s in status if s == SELECTED)}/{num_images}"
	)
	position_label.config(text=f"Position: {ix}/{len(status)}")

	def next_image():
	nonlocal ix, status, cnt, num_images, image_ids
	if cnt[SELECTED] == num_images:
	root.destroy()
	return
	for dx in range(1, len(image_ids)):
	# We can select previously rejected image, in case we run out of unvisited
	if status[(ix + dx) % len(image_ids)] in [UNVISITED, REJECTED]:
	break
	assert dx != 0, "No free image left"
	ix = (ix + dx) % len(image_ids)
	draw_image()
	draw_progbars()

	image_label = Label(root)
	image_label.pack()
	image_label.place(x=(CANVAS_WIDTH - IMAGE_SIZE) // 2, y=0)
	select_btn = Button(root, text="Select", command=lambda: get_next(SELECTED))
	reject_btn = Button(root, text="Reject", command=lambda: get_next(REJECTED))
	undo_btn = Button(root, text="Undo", command=undo)
	progress_label = Label(root, text=f"Selected: 0/{num_images}")
	position_label = Label(root, text=f"Position: 0/{len(image_ids)}")
	class_label = Label(root, text=f"{class_name}({spurious_context})")
	class_label.pack()
	class_label.place(x=170, y=250)
	position_label.pack()
	position_label.place(x=0, y=250)
	progress_label.pack()
	progress_label.place(x=300, y=250)
	select_btn.pack()
	select_btn.place(x=85, y=225)
	reject_btn.pack()
	reject_btn.place(x=165, y=225)
	undo_btn.pack()
	undo_btn.place(x=250, y=225)
	next_image()
	root.mainloop()

	sampled_images: set[str] = set()
	for i, s in enumerate(status):
	if s == SELECTED:
	sampled_images.add(image_ids[i])
	return sampled_images

	def _sample_from_domains(
	self,
	seed: int,
	domains: dict[str, tuple[list[str], list[str]]],
	num_train_images_per_class: int,
	num_test_images_per_class: int,
	spurious_class: str,
	train_spurious_context: str,
	test_spurious_context: str,
	) -> dict[str, tuple[list[str], list[str]]]:
	"""Return sampled domain data from the given full domains."""
	# Maintain order of original domains
	sampled_domains = {cls: (set(), set()) for cls in domains.keys()}
	# First process the spurious class, to maximize the number of images available for it
	classes = list(domains.keys())
	spurious_class_ix = classes.index(spurious_class)
	classes[0], classes[spurious_class_ix] = classes[spurious_class_ix], classes[0]
	sampled_ids = set()

	for cls in classes:
	train_ids, test_ids = domains[cls]
	try:
	if cls == spurious_class:
	train_ids = train_ids - sampled_ids
	sampled_train_ids = self._interactive_sample(
	image_ids=train_ids,
	seed=seed,
	num_images=num_train_images_per_class,
	class_name=spurious_class,
	spurious_context=train_spurious_context,
	)
	sampled_ids = sampled_ids \| set(sampled_train_ids)
	test_ids = test_ids - sampled_ids
	sampled_test_ids = self._interactive_sample(
	image_ids=test_ids,
	seed=seed,
	num_images=num_test_images_per_class,
	class_name=spurious_class,
	spurious_context=test_spurious_context,
	)
	sampled_ids = sampled_ids \| set(sampled_test_ids)
	else:
	train_ids = train_ids - sampled_ids
	sampled_train_ids = random.Random(seed).sample(
	sorted(list(train_ids)), num_train_images_per_class
	)
	sampled_ids = sampled_ids \| set(sampled_train_ids)
	test_ids = test_ids - sampled_ids
	sampled_test_ids = random.Random(seed).sample(
	sorted(list(test_ids)), num_test_images_per_class
	)
	sampled_ids = sampled_ids \| set(sampled_test_ids)
	except ValueError:
	logger.error(
	f"{cls}: {len(train_ids)} train images, {len(test_ids)} test images"
	)
	raise Exception("Not enough images for this class")
	sampled_domains[cls] = (sampled_train_ids, sampled_test_ids)
	return sampled_domains

	def create(
	self,
	seed: int,
	selected_classes: list[str],
	spurious_class: str,
	train_spurious_context: str,
	test_spurious_context: str,
	num_train_images_per_class: int,
	num_test_images_per_class: int,
	) -> MetashiftData:
	"""Return (metadata, data) splits for the given data shift."""
	domains_specification = {
	**{cls: (None, None) for cls in selected_classes},
	spurious_class: (
	train_spurious_context,
	test_spurious_context,
	), # overwrite spurious_class
	}
	domains = self._get_class_domains(domains_specification)
	logger.info(
	f"Total number of images: {len(self._get_unique_ids_from_domains(domains))}"
	)
	sampled_domains = self._sample_from_domains(
	seed=seed,
	domains=domains,
	num_train_images_per_class=num_train_images_per_class,
	num_test_images_per_class=num_test_images_per_class,
	spurious_class=spurious_class,
	train_spurious_context=train_spurious_context,
	test_spurious_context=test_spurious_context,
	)
	logger.info(
	f"Total number of images after sampling: {len(self._get_unique_ids_from_domains(sampled_domains))}"
	)
	data_splits = {"train": dict(), "test": dict()}
	for cls, (train_ids, test_ids) in sampled_domains.items():
	data_splits["train"][cls] = train_ids
	data_splits["test"][cls] = test_ids

	return MetashiftData(
	selected_classes=selected_classes,
	spurious_class=spurious_class,
	train_context=train_spurious_context,
	test_context=test_spurious_context,
	data_splits=DataSplits(
	train=data_splits["train"],
	test=data_splits["test"],
	),
	)

	def _get_unique_ids_from_info(self, info: dict[str, MetashiftData]):
	"""Get unique ids from info struct."""
	unique_ids = set()
	for data in info.values():
	for ids in data.data_splits.train.values():
	unique_ids.update(ids)
	for ids in data.data_splits.test.values():
	unique_ids.update(ids)
	return unique_ids

	def _replace_ids_with_paths(
	self, info: dict[str, MetashiftData], data_path: Path
	) -> MetashiftData:
	"""Replace ids with paths."""
	new_data = dict()
	for dataset_name, data in info.items():
	for cls, ids in data.data_splits.train.items():
	data.data_splits.train[cls] = [
	str(data_path / f"{_id}.jpg") for _id in ids
	]
	for cls, ids in data.data_splits.test.items():
	data.data_splits.test[cls] = [
	str(data_path / f"{_id}.jpg") for _id in ids
	]
	new_data[dataset_name] = data
	return new_data

	def save_all(self, info: dict[str, MetashiftData], save_images: bool):
	"""Save all datasets to the given directory."""
	out_path = Path(".")
	data_path = out_path / "data"
	data_path.mkdir(parents=True, exist_ok=True)
	scenarios_path = out_path / "scenarios" / "cherrypicked"
	scenarios_path.mkdir(parents=True, exist_ok=True)

	unique_ids = self._get_unique_ids_from_info(info)
	data = self._replace_ids_with_paths(info, data_path)
	for dataset_name, data in info.items():
	with open(scenarios_path / f"{dataset_name}.json", "w") as f:
	f.write(data.model_dump_json(indent=2))
	if save_images:
	with tarfile.open(data_path / "images.tar.gz", "w:gz") as tar:
	for _id in unique_ids:
	tar.add(
	Path(self.visual_genome_images_dir) / f"{_id}.jpg",
	)


	def get_dataset_name(task_name: str, experiment_name: str) -> str:
	return f"{task_name}_{experiment_name}"


	def main():
	parser = setup_parser()
	args = parser.parse_args()
	config = OmegaConf.load(args.config)
	metashift_factory = MetashiftFactory(
	full_candidate_subsets_path=args.full_candidate_subsets_path,
	visual_genome_images_dir=args.visual_genome_images_dir,
	)
	info: dict[str, MetashiftData] = dict()
	for task_config in config.tasks:
	for experiment_config in task_config.experiments:
	data = metashift_factory.create(
	seed=task_config.seed,
	selected_classes=task_config.selected_classes,
	spurious_class=experiment_config.spurious_class,
	train_spurious_context=experiment_config.train_context,
	test_spurious_context=experiment_config.test_context,
	num_test_images_per_class=task_config.num_images_per_class_test,
	num_train_images_per_class=task_config.num_images_per_class_train,
	)
	dataset_name = get_dataset_name(task_config.name, experiment_config.name)
	assert dataset_name not in info
	info[dataset_name] = data

	metashift_factory.save_all(info, save_images=args.save_images)


	if __name__ == "__main__":
	main()