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import math
import torch
import json
from pathlib import Path
import pytorch_lightning as pl
from PIL import Image
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
from typing import Dict, NamedTuple, List, Optional, Union

from models.clip.prompt import PromptProcessor


def prepare_prompt(prompt: Union[str, Dict[str, str]]):
    return {"content": prompt} if isinstance(prompt, str) else prompt


class CSVDataItem(NamedTuple):
    instance_image_path: Path
    class_image_path: Path
    prompt: str
    nprompt: str


class CSVDataModule(pl.LightningDataModule):
    def __init__(
            self,
            batch_size: int,
            data_file: str,
            prompt_processor: PromptProcessor,
            instance_identifier: str,
            class_identifier: Optional[str] = None,
            class_subdir: str = "cls",
            num_class_images: int = 100,
            size: int = 512,
            repeats: int = 1,
            interpolation: str = "bicubic",
            center_crop: bool = False,
            valid_set_size: Optional[int] = None,
            generator: Optional[torch.Generator] = None,
            collate_fn=None,
            num_workers: int = 0
    ):
        super().__init__()

        self.data_file = Path(data_file)

        if not self.data_file.is_file():
            raise ValueError("data_file must be a file")

        self.data_root = self.data_file.parent
        self.class_root = self.data_root.joinpath(class_subdir)
        self.class_root.mkdir(parents=True, exist_ok=True)
        self.num_class_images = num_class_images

        self.prompt_processor = prompt_processor
        self.instance_identifier = instance_identifier
        self.class_identifier = class_identifier
        self.size = size
        self.repeats = repeats
        self.center_crop = center_crop
        self.interpolation = interpolation
        self.valid_set_size = valid_set_size
        self.generator = generator
        self.collate_fn = collate_fn
        self.num_workers = num_workers
        self.batch_size = batch_size

    def prepare_subdata(self, template, data, num_class_images=1):
        image = template["image"] if "image" in template else "{}"
        prompt = template["prompt"] if "prompt" in template else "{content}"
        nprompt = template["nprompt"] if "nprompt" in template else "{content}"

        image_multiplier = max(math.ceil(num_class_images / len(data)), 1)

        return [
            CSVDataItem(
                self.data_root.joinpath(image.format(item["image"])),
                self.class_root.joinpath(f"{Path(item['image']).stem}_{i}{Path(item['image']).suffix}"),
                prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
                nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else ""))
            )
            for item in data
            for i in range(image_multiplier)
        ]

    def prepare_data(self):
        with open(self.data_file, 'rt') as f:
            metadata = json.load(f)
        template = metadata["template"] if "template" in metadata else {}
        items = metadata["items"] if "items" in metadata else []

        items = [item for item in items if not "skip" in item or item["skip"] != True]
        num_images = len(items)

        valid_set_size = int(num_images * 0.1)
        if self.valid_set_size:
            valid_set_size = min(valid_set_size, self.valid_set_size)
        valid_set_size = max(valid_set_size, 1)
        train_set_size = num_images - valid_set_size

        data_train, data_val = random_split(items, [train_set_size, valid_set_size], self.generator)

        self.data_train = self.prepare_subdata(template, data_train, self.num_class_images)
        self.data_val = self.prepare_subdata(template, data_val)

    def setup(self, stage=None):
        train_dataset = CSVDataset(self.data_train, self.prompt_processor, batch_size=self.batch_size,
                                   instance_identifier=self.instance_identifier, class_identifier=self.class_identifier,
                                   num_class_images=self.num_class_images,
                                   size=self.size, interpolation=self.interpolation,
                                   center_crop=self.center_crop, repeats=self.repeats)
        val_dataset = CSVDataset(self.data_val, self.prompt_processor, batch_size=self.batch_size,
                                 instance_identifier=self.instance_identifier,
                                 size=self.size, interpolation=self.interpolation,
                                 center_crop=self.center_crop)
        self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
                                            shuffle=True, pin_memory=True, collate_fn=self.collate_fn,
                                            num_workers=self.num_workers)
        self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size,
                                          pin_memory=True, collate_fn=self.collate_fn,
                                          num_workers=self.num_workers)

    def train_dataloader(self):
        return self.train_dataloader_

    def val_dataloader(self):
        return self.val_dataloader_


class CSVDataset(Dataset):
    def __init__(
        self,
        data: List[CSVDataItem],
        prompt_processor: PromptProcessor,
        instance_identifier: str,
        batch_size: int = 1,
        class_identifier: Optional[str] = None,
        num_class_images: int = 0,
        size: int = 512,
        repeats: int = 1,
        interpolation: str = "bicubic",
        center_crop: bool = False,
    ):

        self.data = data
        self.prompt_processor = prompt_processor
        self.batch_size = batch_size
        self.instance_identifier = instance_identifier
        self.class_identifier = class_identifier
        self.num_class_images = num_class_images
        self.image_cache = {}

        self.num_instance_images = len(self.data)
        self._length = self.num_instance_images * repeats

        self.interpolation = {"linear": transforms.InterpolationMode.NEAREST,
                              "bilinear": transforms.InterpolationMode.BILINEAR,
                              "bicubic": transforms.InterpolationMode.BICUBIC,
                              "lanczos": transforms.InterpolationMode.LANCZOS,
                              }[interpolation]
        self.image_transforms = transforms.Compose(
            [
                transforms.Resize(size, interpolation=self.interpolation),
                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

    def __len__(self):
        return math.ceil(self._length / self.batch_size) * self.batch_size

    def get_image(self, path):
        if path in self.image_cache:
            return self.image_cache[path]

        image = Image.open(path)
        if not image.mode == "RGB":
            image = image.convert("RGB")
        self.image_cache[path] = image

        return image

    def get_input_ids(self, prompt, identifier):
        return self.prompt_processor.get_input_ids(prompt.format(identifier))

    def get_example(self, i):
        item = self.data[i % self.num_instance_images]

        example = {}

        example["prompts"] = item.prompt
        example["nprompts"] = item.nprompt

        example["instance_images"] = self.get_image(item.instance_image_path)
        example["instance_prompt_ids"] = self.get_input_ids(item.prompt, self.instance_identifier)

        if self.num_class_images != 0:
            example["class_images"] = self.get_image(item.class_image_path)
            example["class_prompt_ids"] = self.get_input_ids(item.nprompt, self.class_identifier)

        return example

    def __getitem__(self, i):
        example = {}
        unprocessed_example = self.get_example(i)

        example["prompts"] = unprocessed_example["prompts"]
        example["nprompts"] = unprocessed_example["nprompts"]
        example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
        example["instance_prompt_ids"] = unprocessed_example["instance_prompt_ids"]

        if self.num_class_images != 0:
            example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
            example["class_prompt_ids"] = unprocessed_example["class_prompt_ids"]

        return example