#!/usr/bin/env python3

import argparse
import io
import multiprocessing
import os
import time
from distutils.util import strtobool
from typing import Union

import cv2
import numpy as np
import torch

try:
    torch._C._jit_override_can_fuse_on_cpu(False)
    torch._C._jit_override_can_fuse_on_gpu(False)
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_nvfuser_enabled(False)
except:
    pass

from flask import Flask, request, send_file
from flask_cors import CORS

from lama_cleaner.helper import (
    download_model,
    load_img,
    norm_img,
    numpy_to_bytes,
    pad_img_to_modulo,
    resize_max_size,
)

NUM_THREADS = str(multiprocessing.cpu_count())

os.environ["OMP_NUM_THREADS"] = NUM_THREADS
os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
os.environ["MKL_NUM_THREADS"] = NUM_THREADS
os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
if os.environ.get("CACHE_DIR"):
    os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]

BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "./lama_cleaner/app/build")

app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
app.config["JSON_AS_ASCII"] = False
CORS(app)

model = None
device = None


@app.route("/inpaint", methods=["POST"])
def process():
    input = request.files
    image = load_img(input["image"].read())
    original_shape = image.shape
    interpolation = cv2.INTER_CUBIC

    size_limit: Union[int, str] = request.form.get("sizeLimit", "1080")
    if size_limit == "Original":
        size_limit = max(image.shape)
    else:
        size_limit = int(size_limit)

    print(f"Origin image shape: {original_shape}")
    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
    print(f"Resized image shape: {image.shape}")
    image = norm_img(image)

    mask = load_img(input["mask"].read(), gray=True)
    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
    mask = norm_img(mask)

    res_np_img = run(image, mask)

    # resize to original size
    # res_np_img = cv2.resize(
    #     res_np_img,
    #     dsize=(original_shape[1], original_shape[0]),
    #     interpolation=interpolation,
    # )

    return send_file(
        io.BytesIO(numpy_to_bytes(res_np_img)),
        mimetype="image/jpeg",
        as_attachment=True,
        attachment_filename="result.jpeg",
    )


@app.route("/")
def index():
    return send_file(os.path.join(BUILD_DIR, "index.html"))


def run(image, mask):
    """
    image: [C, H, W]
    mask: [1, H, W]
    return: BGR IMAGE
    """
    origin_height, origin_width = image.shape[1:]
    image = pad_img_to_modulo(image, mod=8)
    mask = pad_img_to_modulo(mask, mod=8)

    mask = (mask > 0) * 1
    image = torch.from_numpy(image).unsqueeze(0).to(device)
    mask = torch.from_numpy(mask).unsqueeze(0).to(device)

    start = time.time()
    with torch.no_grad():
        inpainted_image = model(image, mask)

    print(f"process time: {(time.time() - start)*1000}ms")
    cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
    cur_res = cur_res[0:origin_height, 0:origin_width, :]
    cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
    cur_res = cv2.cvtColor(cur_res, cv2.COLOR_BGR2RGB)
    return cur_res


def get_args_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument("--port", default=8080, type=int)
    parser.add_argument("--device", default="cuda", type=str)
    parser.add_argument("--debug", action="store_true")
    return parser.parse_args()


def main():
    global model
    global device
    args = get_args_parser()
    device = torch.device(args.device)

    if os.environ.get("LAMA_MODEL"):
        model_path = os.environ.get("LAMA_MODEL")
        if not os.path.exists(model_path):
            raise FileNotFoundError(f"lama torchscript model not found: {model_path}")
    else:
        model_path = download_model()

    model = torch.jit.load(model_path, map_location="cpu")
    model = model.to(device)
    model.eval()
    app.run(host="0.0.0.0", port=args.port, debug=args.debug)


if __name__ == "__main__":
    main()