from basicsr.archs.rrdbnet_arch import RRDBNet
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    StableDiffusionUpscalePipeline,
)
from gfpgan import GFPGANer
from os import path
from PIL import Image
from realesrgan import RealESRGANer
from typing import Literal, Union

import numpy as np

from .image import (
    process_tiles
)
from .onnx import (
    ONNXNet,
    OnnxStableDiffusionUpscalePipeline,
)
from .utils import (
    ServerContext,
    Size,
)

# TODO: these should all be params or config
pre_pad = 0
tile_pad = 10


class UpscaleParams():
    def __init__(
        self,
        upscale_model: str,
        provider: str,
        correction_model: Union[str, None] = None,
        denoise: float = 0.5,
        faces=True,
        face_strength: float = 0.5,
        format: Literal['onnx', 'pth'] = 'onnx',
        half=False,
        outscale: int = 1,
        scale: int = 4,
    ) -> None:
        self.upscale_model = upscale_model
        self.provider = provider
        self.correction_model = correction_model
        self.denoise = denoise
        self.faces = faces
        self.face_strength = face_strength
        self.format = format
        self.half = half
        self.outscale = outscale
        self.scale = scale

    def resize(self, size: Size) -> Size:
        return Size(size.width * self.outscale, size.height * self.outscale)


def make_resrgan(ctx: ServerContext, params: UpscaleParams, tile=0):
    model_file = '%s.%s' % (params.upscale_model, params.format)
    model_path = path.join(ctx.model_path, model_file)
    if not path.isfile(model_path):
        raise Exception('Real ESRGAN model not found at %s' % model_path)

    # use ONNX acceleration, if available
    if params.format == 'onnx':
        model = ONNXNet(ctx, model_file, provider=params.provider)
    elif params.format == 'pth':
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
                        num_block=23, num_grow_ch=32, scale=params.scale)
    else:
        raise Exception('unknown platform %s' % params.format)

    dni_weight = None
    if params.upscale_model == 'realesr-general-x4v3' and params.denoise != 1:
        wdn_model_path = model_path.replace(
            'realesr-general-x4v3', 'realesr-general-wdn-x4v3')
        model_path = [model_path, wdn_model_path]
        dni_weight = [params.denoise, 1 - params.denoise]

    # TODO: shouldn't need the PTH file
    upsampler = RealESRGANer(
        scale=params.scale,
        model_path=path.join(ctx.model_path, '%s.pth' % params.upscale_model),
        dni_weight=dni_weight,
        model=model,
        tile=tile,
        tile_pad=tile_pad,
        pre_pad=pre_pad,
        half=params.half)

    return upsampler


def upscale_resrgan(ctx: ServerContext, params: UpscaleParams, source_image: Image) -> Image:
    print('upscaling image with Real ESRGAN', params.scale)

    output = np.array(source_image)
    upsampler = make_resrgan(ctx, params, tile=512)

    output, _ = upsampler.enhance(output, outscale=params.outscale)

    output = Image.fromarray(output, 'RGB')
    print('final output image size', output.size)
    return output


def upscale_gfpgan(ctx: ServerContext, params: UpscaleParams, image, upsampler=None) -> Image:
    print('correcting faces with GFPGAN model: %s' % params.correction_model)

    if params.correction_model is None:
        print('no face model given, skipping')
        return image

    if upsampler is None:
        upsampler = make_resrgan(ctx, params)

    face_path = path.join(ctx.model_path, '%s.pth' % (params.correction_model))

    # TODO: doesn't have a model param, not sure how to pass ONNX model
    face_enhancer = GFPGANer(
        model_path=face_path,
        upscale=params.outscale,
        arch='clean',
        channel_multiplier=2,
        bg_upsampler=upsampler)

    _, _, output = face_enhancer.enhance(
        image, has_aligned=False, only_center_face=False, paste_back=True, weight=params.face_strength)

    return output


def upscale_stable_diffusion(ctx: ServerContext, params: UpscaleParams, image: Image) -> Image:
    print('upscaling with Stable Diffusion')
    model_path = '../models/%s' % params.upscale_model
    # ValueError: Pipeline <class 'onnx_web.onnx.pipeline_onnx_stable_diffusion_upscale.OnnxStableDiffusionUpscalePipeline'>
    # expected {'vae', 'unet', 'text_encoder', 'tokenizer', 'scheduler', 'low_res_scheduler'},
    # but only {'scheduler', 'tokenizer', 'text_encoder', 'unet'} were passed.
    # pipeline = OnnxStableDiffusionUpscalePipeline.from_pretrained(
    #     model_path,
    #     vae=AutoencoderKL.from_pretrained(model_path, subfolder='vae_encoder'),
    #     low_res_scheduler=DDPMScheduler.from_pretrained(model_path, subfolder='scheduler'),
    # )
    # result = pipeline('', image=image)

    pipeline = StableDiffusionUpscalePipeline.from_pretrained('stabilityai/stable-diffusion-x4-upscaling')
    upscale = lambda i: pipeline('an astronaut eating a hamburger', image=i).images[0]
    result = process_tiles(image, 64, 4, [upscale])
    return result


def run_upscale_correction(ctx: ServerContext, params: UpscaleParams, image: Image) -> Image:
    print('running upscale pipeline')

    if params.scale > 1:
        if 'esrgan' in params.upscale_model:
            image = upscale_resrgan(ctx, params, image)
        elif 'stable-diffusion' in params.upscale_model:
            image = upscale_stable_diffusion(ctx, params, image)

    if params.faces:
        image = upscale_gfpgan(ctx, params, image)

    return image