from logging import getLogger
from os import path
from typing import Optional

import numpy as np
from PIL import Image

from ..models.onnx import OnnxModel
from ..params import (
    DeviceParams,
    ImageParams,
    Size,
    SizeChart,
    StageParams,
    UpscaleParams,
)
from ..server import ModelTypes, ServerContext
from ..utils import run_gc
from ..worker import WorkerContext
from .base import BaseStage
from .result import StageResult

logger = getLogger(__name__)


class UpscaleBSRGANStage(BaseStage):
    max_tile = SizeChart.micro

    def load(
        self,
        server: ServerContext,
        _stage: StageParams,
        upscale: UpscaleParams,
        device: DeviceParams,
    ):
        # must be within the load function for patch to take effect
        model_path = path.join(server.model_path, "%s.onnx" % (upscale.upscale_model))
        cache_key = (model_path,)
        cache_pipe = server.cache.get(ModelTypes.upscaling, cache_key)

        if cache_pipe is not None:
            logger.debug("reusing existing BSRGAN pipeline")
            return cache_pipe

        logger.info("loading BSRGAN model from %s", model_path)

        pipe = OnnxModel(
            server,
            model_path,
            provider=device.ort_provider(),
            sess_options=device.sess_options(),
        )

        server.cache.set(ModelTypes.upscaling, cache_key, pipe)
        run_gc([device])

        return pipe

    def run(
        self,
        worker: WorkerContext,
        server: ServerContext,
        stage: StageParams,
        _params: ImageParams,
        sources: StageResult,
        *,
        upscale: UpscaleParams,
        stage_source: Optional[Image.Image] = None,
        **kwargs,
    ) -> StageResult:
        upscale = upscale.with_args(**kwargs)

        if upscale.upscale_model is None:
            logger.warning("no upscaling model given, skipping")
            return sources

        logger.info("upscaling with BSRGAN model: %s", upscale.upscale_model)
        device = worker.get_device()
        bsrgan = self.load(server, stage, upscale, device)

        outputs = []
        for source in sources.as_numpy():
            image = source / 255.0
            image = image[:, :, [2, 1, 0]].astype(np.float32).transpose((2, 0, 1))
            image = np.expand_dims(image, axis=0)
            logger.trace("BSRGAN input shape: %s", image.shape)

            scale = upscale.outscale
            logger.trace(
                "BSRGAN output shape: %s",
                (
                    image.shape[0],
                    image.shape[1],
                    image.shape[2] * scale,
                    image.shape[3] * scale,
                ),
            )

            output = bsrgan(image)

            output = np.clip(np.squeeze(output, axis=0), 0, 1)
            output = output[[2, 1, 0], :, :].transpose((1, 2, 0))
            output = (output * 255.0).round().astype(np.uint8)

            logger.debug("output image shape: %s", output.shape)
            outputs.append(output)

        return StageResult(arrays=outputs)

    def steps(
        self,
        params: ImageParams,
        size: Size,
    ) -> int:
        tile = min(params.unet_tile, self.max_tile)
        return size.width // tile * size.height // tile