# This code is built from the Huggingface repository: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py, and
# https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
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import os
import random
from pathlib import Path
import numpy as np
import PIL
from PIL import Image
import torch
from torch.utils.data import Dataset
from torchvision import transforms


def preprocess(image, scale, resample):
    image = image.resize((scale, scale), resample=resample)
    image = np.array(image).astype(np.uint8)
    image = (image / 127.5 - 1.0).astype(np.float32)
    return image


def collate_fn(examples, with_prior_preservation):
    input_ids = [example["instance_prompt_ids"] for example in examples]
    pixel_values = [example["instance_images"] for example in examples]
    mask = [example["mask"] for example in examples]
    # Concat class and instance examples for prior preservation.
    # We do this to avoid doing two forward passes.
    if with_prior_preservation:
        input_ids += [example["class_prompt_ids"] for example in examples]
        pixel_values += [example["class_images"] for example in examples]
        mask += [example["class_mask"] for example in examples]

    input_ids = torch.cat(input_ids, dim=0)
    pixel_values = torch.stack(pixel_values)
    mask = torch.stack(mask)
    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
    mask = mask.to(memory_format=torch.contiguous_format).float()

    batch = {
        "input_ids": input_ids,
        "pixel_values": pixel_values,
        "mask": mask.unsqueeze(1)
    }
    return batch


class PromptDataset(Dataset):
    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."

    def __init__(self, prompt, num_samples):
        self.prompt = prompt
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, index):
        example = {}
        example["prompt"] = self.prompt
        example["index"] = index
        return example


class CustomDiffusionDataset(Dataset):
    """
    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
    It pre-processes the images and the tokenizes prompts.
    """

    def __init__(
        self,
        concepts_list,
        tokenizer,
        size=512,
        center_crop=False,
        with_prior_preservation=False,
        num_class_images=200,
        hflip=False,
    ):
        self.size = size
        self.center_crop = center_crop
        self.tokenizer = tokenizer
        self.interpolation = PIL.Image.BILINEAR

        self.instance_images_path = []
        self.class_images_path = []
        self.with_prior_preservation = with_prior_preservation
        for concept in concepts_list:
            inst_img_path = [(x, concept["instance_prompt"]) for x in Path(concept["instance_data_dir"]).iterdir() if x.is_file()]
            self.instance_images_path.extend(inst_img_path)

            if with_prior_preservation:
                class_data_root = Path(concept["class_data_dir"])
                if os.path.isdir(class_data_root):
                    class_images_path = list(class_data_root.iterdir())
                    class_prompt = [concept["class_prompt"] for _ in range(len(class_images_path))]
                else:
                    with open(class_data_root, "r") as f:
                        class_images_path = f.read().splitlines()
                    with open(concept["class_prompt"], "r") as f:
                        class_prompt = f.read().splitlines()

                class_img_path = [(x, y) for (x, y) in zip(class_images_path, class_prompt)]
                self.class_images_path.extend(class_img_path[:num_class_images])

        random.shuffle(self.instance_images_path)
        self.num_instance_images = len(self.instance_images_path)
        self.num_class_images = len(self.class_images_path)
        self._length = max(self.num_class_images, self.num_instance_images)
        self.flip = transforms.RandomHorizontalFlip(0.5 * hflip)

        self.image_transforms = transforms.Compose(
            [
                self.flip,
                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

    def __len__(self):
        return self._length

    def __getitem__(self, index):
        example = {}
        instance_image, instance_prompt = self.instance_images_path[index % self.num_instance_images]
        instance_image = Image.open(instance_image)
        if not instance_image.mode == "RGB":
            instance_image = instance_image.convert("RGB")
        instance_image = self.flip(instance_image)

        ##############################################################################
        #### apply resize augmentation and create a valid image region mask ##########
        ##############################################################################
        if np.random.randint(0, 3) < 2:
            random_scale = np.random.randint(self.size // 3, self.size+1)
        else:
            random_scale = np.random.randint(int(1.2*self.size), int(1.4*self.size))

        if random_scale % 2 == 1:
            random_scale += 1

        if random_scale < 0.6*self.size:
            add_to_caption = np.random.choice(["a far away ", "very small "])
            instance_prompt = add_to_caption + instance_prompt
            cx = np.random.randint(random_scale // 2, self.size - random_scale // 2 + 1)
            cy = np.random.randint(random_scale // 2, self.size - random_scale // 2 + 1)
            instance_image1 = preprocess(instance_image, random_scale, self.interpolation)
            instance_image = np.zeros((self.size, self.size, 3), dtype=np.float32)
            instance_image[cx - random_scale // 2: cx + random_scale // 2, cy - random_scale // 2: cy + random_scale // 2, :] = instance_image1

            mask = np.zeros((self.size // 8, self.size // 8))
            mask[(cx - random_scale // 2) // 8 + 1: (cx + random_scale // 2) // 8 - 1, (cy - random_scale // 2) // 8 + 1: (cy + random_scale // 2) // 8 - 1] = 1.
        elif random_scale > self.size:
            add_to_caption = np.random.choice(["zoomed in ", "close up "])
            instance_prompt = add_to_caption + instance_prompt
            cx = np.random.randint(self.size // 2, random_scale - self.size // 2 + 1)
            cy = np.random.randint(self.size // 2, random_scale - self.size // 2 + 1)

            instance_image = preprocess(instance_image, random_scale, self.interpolation)
            instance_image = instance_image[cx - self.size // 2: cx + self.size // 2, cy - self.size // 2: cy + self.size // 2, :]
            mask = np.ones((self.size // 8, self.size // 8))
        else:
            instance_image = preprocess(instance_image, self.size, self.interpolation)
            mask = np.ones((self.size // 8, self.size // 8))
        ########################################################################

        example["instance_images"] = torch.from_numpy(instance_image).permute(2, 0, 1)
        example["mask"] = torch.from_numpy(mask)
        example["instance_prompt_ids"] = self.tokenizer(
            instance_prompt,
            truncation=True,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            return_tensors="pt",
        ).input_ids

        if self.with_prior_preservation:
            class_image, class_prompt = self.class_images_path[index % self.num_class_images]
            class_image = Image.open(class_image)
            if not class_image.mode == "RGB":
                class_image = class_image.convert("RGB")
            example["class_images"] = self.image_transforms(class_image)
            example["class_mask"] = torch.ones_like(example["mask"])
            example["class_prompt_ids"] = self.tokenizer(
                class_prompt,
                truncation=True,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            ).input_ids

        return example
