#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
JTP2 (Joint Tagger Project 2) Image Classification Script
This script implements a multi-label classifier for furry images using the
PILOT2 model. It processes images, generates tags, and saves the results. The
model is based on a Vision Transformer architecture and uses a custom GatedHead
for classification.
Key features:
- Image preprocessing and transformation
- Model inference using PILOT2
- Tag generation with customizable threshold
- Batch processing of image directories
- Saving results as text files alongside images
Usage:
python jtp2.py <directory> [--threshold <float>]
"""
import os
import json
import argparse
from PIL import Image
import safetensors.torch
import timm
from timm.models import VisionTransformer
import torch
from torchvision.transforms import transforms
from torchvision.transforms import InterpolationMode
import torchvision.transforms.functional as TF
import pillow_jxl

torch.set_grad_enabled(False)


class Fit(torch.nn.Module):
    """
    A custom transform module for resizing and padding images.
    Args:
        bounds (tuple[int, int] | int): The target dimensions for the image.
        interpolation (InterpolationMode): The interpolation method for resizing.
        grow (bool): Whether to allow upscaling of images.
        pad (float | None): The padding value to use if padding is applied.
    """
    def __init__(
        self,
        bounds: tuple[int, int] | int,
        interpolation=InterpolationMode.LANCZOS,
        grow: bool = True,
        pad: float | None = None
    ):
        super().__init__()
        self.bounds = (bounds, bounds) if isinstance(bounds, int) else bounds
        self.interpolation = interpolation
        self.grow = grow
        self.pad = pad

    def forward(self, img: Image) -> Image:
        """
        Applies the Fit transform to the input image.
        Args:
            img (Image): The input PIL Image.
        Returns:
            Image: The transformed PIL Image.
        """
        wimg, himg = img.size
        hbound, wbound = self.bounds
        hscale = hbound / himg
        wscale = wbound / wimg
        if not self.grow:
            hscale = min(hscale, 1.0)
            wscale = min(wscale, 1.0)
        scale = min(hscale, wscale)
        if scale == 1.0:
            return img
        hnew = min(round(himg * scale), hbound)
        wnew = min(round(wimg * scale), wbound)
        img = TF.resize(img, (hnew, wnew), self.interpolation)
        if self.pad is None:
            return img
        hpad = hbound - hnew
        wpad = wbound - wnew
        tpad = hpad 
        bpad = hpad - tpad
        lpad = wpad 
        rpad = wpad - lpad
        return TF.pad(img, (lpad, tpad, rpad, bpad), self.pad)
    def __repr__(self) -> str:
        """
        Returns a string representation of the Fit module.
        Returns:
            str: A string describing the module's parameters.
        """
        return (
            f"{self.__class__.__name__}(bounds={self.bounds}, "
            f"interpolation={self.interpolation.value}, grow={self.grow}, "
            f"pad={self.pad})"
        )


class CompositeAlpha(torch.nn.Module):
    """
    A module for compositing images with alpha channels over a background color.
    Args:
        background (tuple[float, float, float] | float): The background color to
        use for compositing.
    """
    def __init__(self, background: tuple[float, float, float] | float):
        super().__init__()
        self.background = (
            (background, background, background)
            if isinstance(background, float)
            else background
        )
        self.background = torch.tensor(self.background).unsqueeze(1).unsqueeze(2)

    def forward(self, img: torch.Tensor) -> torch.Tensor:
        """
        Applies alpha compositing to the input image tensor.
        Args:
            img (torch.Tensor): The input image tensor.
        Returns:
            torch.Tensor: The composited image tensor.
        """
        if img.shape[-3] == 3:
            return img
        alpha = img[..., 3, None, :, :]
        img[..., :3, :, :] *= alpha
        background = self.background.expand(-1, img.shape[-2], img.shape[-1])
        if background.ndim == 1:
            background = background[:, None, None]
        elif background.ndim == 2:
            background = background[None, :, :]
        img[..., :3, :, :] += (1.0 - alpha) * background
        return img[..., :3, :, :]

    def __repr__(self) -> str:
        """
        Returns a string representation of the CompositeAlpha module.
        Returns:
            str: A string describing the module's parameters.
        """
        return f"{self.__class__.__name__}(background={self.background})"


transform = transforms.Compose([
    Fit((384, 384)),
    transforms.ToTensor(),
    CompositeAlpha(0.5),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
    transforms.CenterCrop((384, 384)),
])
model = timm.create_model(
    "vit_so400m_patch14_siglip_384.webli",
    pretrained=False,
    num_classes=9083
)  # type: VisionTransformer


class GatedHead(torch.nn.Module):
    """
    A custom head module with gating mechanism for the classifier.
    Args:
        num_features (int): The number of input features.
        num_classes (int): The number of output classes.
    """
    def __init__(self, num_features: int, num_classes: int):
        super().__init__()
        self.num_classes = num_classes
        self.linear = torch.nn.Linear(num_features, num_classes * 2)
        self.act = torch.nn.Sigmoid()
        self.gate = torch.nn.Sigmoid()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies the gated head to the input tensor.
        Args:
            x (torch.Tensor): The input tensor.
        Returns:
            torch.Tensor: The output tensor after applying the gated head.
        """
        x = self.linear(x)
        x = self.act(x[:, :self.num_classes]) * self.gate(x[:, self.num_classes:])
        return x


model.head = GatedHead(min(model.head.weight.shape), 9083)
safetensors.torch.load_model(
    model, "/home/kade/source/repos/JTP2/JTP_PILOT2-e3-vit_so400m_patch14_siglip_384.safetensors"
)
if torch.cuda.is_available():
    model.cuda()
    if torch.cuda.get_device_capability()[0] >= 7:  # tensor cores
        model.to(dtype=torch.float16, memory_format=torch.channels_last)
model.eval()
with open("/home/kade/source/repos/JTP2/tags.json", "r", encoding="utf-8") as file:
    tags = json.load(file)  # type: dict
allowed_tags = list(tags.keys())
for idx, tag in enumerate(allowed_tags):
    allowed_tags[idx] = tag.replace("_", " ")
sorted_tag_score = {}


def run_classifier(image, threshold):
    """
    Runs the classifier on a single image and returns tags based on the threshold.
    Args:
        image (PIL.Image): The input image.
        threshold (float): The probability threshold for including tags.
    Returns:
        tuple: A tuple containing the comma-separated tags and a dictionary of
        tag probabilities.
    """
    global sorted_tag_score
    img = image.convert('RGBA')
    tensor = transform(img).unsqueeze(0)
    if torch.cuda.is_available():
        tensor = tensor.cuda()
        if torch.cuda.get_device_capability()[0] >= 7:  # tensor cores
            tensor = tensor.to(dtype=torch.float16, memory_format=torch.channels_last)
    with torch.no_grad():
        probits = model(tensor)[0].cpu()
        values, indices = probits.topk(250)
    tag_score = dict()
    for i in range(indices.size(0)):
        tag_score[allowed_tags[indices[i]]] = values[i].item()
    sorted_tag_score = dict(
        sorted(tag_score.items(), key=lambda item: item[1], reverse=True)
    )
    return create_tags(threshold)

def create_tags(threshold):
    """
    Creates a list of tags based on the current sorted_tag_score and the given
    threshold.
    Args:
        threshold (float): The probability threshold for including tags.
    Returns:
        tuple: A tuple containing the comma-separated tags and a dictionary of
        filtered tag probabilities.
    """
    global sorted_tag_score
    filtered_tag_score = {
        key: value for key, value in sorted_tag_score.items() if value > threshold
    }
    text_no_impl = ", ".join(filtered_tag_score.keys())
    return text_no_impl, filtered_tag_score

def process_directory(directory, threshold):
    """
    Processes all images in a directory and its subdirectories, generating tags
    for each image.
    Args:
        directory (str): The path to the directory containing images.
        threshold (float): The probability threshold for including tags.
    Returns:
        dict: A dictionary mapping image paths to their generated tags.
    """
    results = {}
    for root, _, files in os.walk(directory):
        for file in files:
            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.jxl')):
                image_path = os.path.join(root, file)
                text_file_path = os.path.splitext(image_path)[0] + ".txt"
                
                # Skip if a corresponding .txt file already exists
                if os.path.exists(text_file_path):
                    continue
                
                image = Image.open(image_path)
                tags, _ = run_classifier(image, threshold)
                results[image_path] = tags
                
                # Save tags to a text file with the same name as the image, using UTF-8 encoding
                with open(text_file_path, "w", encoding="utf-8") as text_file:
                    text_file.write(tags)
    return results


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Run inference on a directory of images."
    )
    parser.add_argument("directory", type=str, help="Target directory containing images.")
    parser.add_argument(
        "--threshold", type=float, default=0.2, help="Threshold for tag filtering."
    )
    args = parser.parse_args()
    results = process_directory(args.directory, args.threshold)
    for image_path, tags in results.items():
        print(f"{image_path}: {tags}")