import os
import urllib
from functools import lru_cache
from random import randint
from typing import Any, Callable, Dict, List, Tuple

import clip
import cv2
import gradio as gr
import numpy as np
import PIL
import torch
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
from collections import OrderedDict

try:
    from modules.paths_internal import extensions_dir
except Exception:
    from modules.extensions import extensions_dir

from modules.safe import unsafe_torch_load, load
from modules.devices import device

model_cache = OrderedDict()
sam_model_dir = os.path.join(
    extensions_dir, "PBRemTools/models/")
model_list = [f for f in os.listdir(sam_model_dir) if os.path.isfile(
    os.path.join(sam_model_dir, f)) and f.split('.')[-1] != 'txt']

MAX_WIDTH = MAX_HEIGHT = 800
CLIP_WIDTH = CLIP_HEIGHT = 300
THRESHOLD = 0.05
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def load_sam_model(sam_checkpoint):
    model_type = '_'.join(sam_checkpoint.split('_')[1:-1])
    sam_checkpoint = os.path.join(sam_model_dir, sam_checkpoint)
    torch.load = unsafe_torch_load
    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
    sam.to(device=device)
    torch.load = load
    return sam


def load_mask_generator(model_name) -> SamAutomaticMaskGenerator:
    sam = load_sam_model(model_name)
    mask_generator = SamAutomaticMaskGenerator(sam)
    torch.load = load
    return mask_generator

def load_clip(
    name: str = "ViT-B/32",
) -> Tuple[torch.nn.Module, Callable[[PIL.Image.Image], torch.Tensor]]:
    model, preprocess = clip.load(name, device=device)
    return model.to(device), preprocess


def adjust_image_size(image: np.ndarray) -> np.ndarray:
    height, width = image.shape[:2]
    if height > width:
        if height > MAX_HEIGHT:
            height, width = MAX_HEIGHT, int(MAX_HEIGHT / height * width)
    else:
        if width > MAX_WIDTH:
            height, width = int(MAX_WIDTH / width * height), MAX_WIDTH
    image = cv2.resize(image, (width, height))
    return image


@torch.no_grad()
def get_scores(crops: List[PIL.Image.Image], query: str) -> torch.Tensor:
    model, preprocess = load_clip()
    preprocessed = [preprocess(crop) for crop in crops]
    preprocessed = torch.stack(preprocessed).to(device)
    token = clip.tokenize(query).to(device)
    img_features = model.encode_image(preprocessed)
    txt_features = model.encode_text(token)
    img_features /= img_features.norm(dim=-1, keepdim=True)
    txt_features /= txt_features.norm(dim=-1, keepdim=True)
    probs = 100.0 * img_features @ txt_features.T
    return probs[:, 0].softmax(dim=0)


def filter_masks(
    image: np.ndarray,
    masks: List[Dict[str, Any]],
    predicted_iou_threshold: float,
    stability_score_threshold: float,
    query: str,
    clip_threshold: float,
) -> List[Dict[str, Any]]:
    cropped_masks: List[PIL.Image.Image] = []
    filtered_masks: List[Dict[str, Any]] = []

    for mask in masks:
        if (
            mask["predicted_iou"] < predicted_iou_threshold
            or mask["stability_score"] < stability_score_threshold
        ):
            continue

        filtered_masks.append(mask)

        x, y, w, h = mask["bbox"]
        crop = image[y : y + h, x : x + w]
        crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
        crop = PIL.Image.fromarray(np.uint8(crop * 255)).convert("RGB")
        crop.resize((CLIP_WIDTH, CLIP_HEIGHT))
        cropped_masks.append(crop)

    if query and filtered_masks:
        scores = get_scores(cropped_masks, query)
        filtered_masks = [
            filtered_masks[i]
            for i, score in enumerate(scores)
            if score > clip_threshold
        ]

    return filtered_masks


def draw_masks(image, masks, alpha: float = 0.7) -> np.ndarray:
    for mask in masks:
        color = [randint(127, 255) for _ in range(3)]

        # draw mask overlay
        colored_mask = np.expand_dims(mask["segmentation"], 0).repeat(3, axis=0)
        colored_mask = np.moveaxis(colored_mask, 0, -1)
        masked = np.ma.MaskedArray(image, mask=colored_mask, fill_value=color)
        image_overlay = masked.filled()
        image = cv2.addWeighted(image, 1 - alpha, image_overlay, alpha, 0)

        # draw contour
        contours, _ = cv2.findContours(
            np.uint8(mask["segmentation"]), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
        )
        cv2.drawContours(image, contours, -1, (255, 0, 0), 2)
    return image


def segment(predicted_iou_threshold, stability_score_threshold, clip_threshold, image, query, model_name):
    mask_generator = load_mask_generator(model_name)
    image = adjust_image_size(image)
    masks = mask_generator.generate(image)
    masks = filter_masks(
        image,
        masks,
        predicted_iou_threshold,
        stability_score_threshold,
        query,
        clip_threshold,
    )
    image = draw_masks(image, masks)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = PIL.Image.fromarray(np.uint8(image)).convert("RGB")
    return masks


def get_sa_mask(image, query, model_name, predicted_iou_threshold, stability_score_threshold, clip_threshold):
    masks = segment(predicted_iou_threshold, stability_score_threshold, clip_threshold, image, query, model_name)
    mask_list = []
    for mask in masks:
        colored_mask = np.expand_dims(mask["segmentation"], 0).repeat(3, axis=0)
        colored_mask = np.moveaxis(colored_mask, 0, -1)
        colored_mask = np.where(colored_mask, 0, 255)
        mask_list.append(colored_mask)
    combined_mask = np.minimum.reduce(mask_list)

    return 255 - combined_mask