# flake8: noqa
# This file is used for deploying replicate models
import os

os.system('python setup.py develop')
os.system('pip install realesrgan')

import cv2
import shutil
import tempfile
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact

from gfpgan import GFPGANer

try:
    from cog import BasePredictor, Input, Path
    from realesrgan.utils import RealESRGANer
except Exception:
    print('please install cog and realesrgan package')


class Predictor(BasePredictor):

    def setup(self):
        # download weights
        if not os.path.exists('realesr-general-x4v3.pth'):
            os.system(
                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .')
        if not os.path.exists('GFPGANv1.2.pth'):
            os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .')
        if not os.path.exists('GFPGANv1.3.pth'):
            os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .')

        # background enhancer with RealESRGAN
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
        model_path = 'realesr-general-x4v3.pth'
        half = True if torch.cuda.is_available() else False
        upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)

        # Use GFPGAN for face enhancement
        self.face_enhancer_v3 = GFPGANer(
            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
        self.face_enhancer_v2 = GFPGANer(
            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
        os.makedirs('output', exist_ok=True)

    def predict(
        self,
        img: Path = Input(description='Input'),
        version: str = Input(description='GFPGAN version', choices=['v1.2', 'v1.3'], default='v1.3'),
        scale: float = Input(description='Rescaling factor', default=2)
    ) -> Path:
        try:
            img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
            if len(img.shape) == 3 and img.shape[2] == 4:
                img_mode = 'RGBA'
            else:
                img_mode = None

            h, w = img.shape[0:2]
            if h < 300:
                img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)

            if version == 'v1.2':
                face_enhancer = self.face_enhancer_v2
            else:
                face_enhancer = self.face_enhancer_v3
            try:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            except RuntimeError as error:
                print('Error', error)
            else:
                extension = 'png'

            try:
                if scale != 2:
                    interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
                    h, w = img.shape[0:2]
                    output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
            except Exception as error:
                print('wrong scale input.', error)
            if img_mode == 'RGBA':  # RGBA images should be saved in png format
                extension = 'png'
            else:
                extension = 'jpg'
            save_path = f'output/out.{extension}'
            cv2.imwrite(save_path, output)
            out_path = os.path.join(tempfile.mkdtemp(), 'output.png')
            cv2.imwrite(str(out_path), output)
        except Exception as error:
            print('global exception', error)
        finally:
            clean_folder('output')
        return out_path


def clean_folder(folder):
    for filename in os.listdir(folder):
        file_path = os.path.join(folder, filename)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print(f'Failed to delete {file_path}. Reason: {e}')