import numpy as np
import torch
from PIL import Image
from tqdm import tqdm

from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.fields import (
    FieldDescriptions,
    ImageField,
    InputField,
    UIType,
    WithBoard,
    WithMetadata,
)
from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.session_processor.session_processor_common import CanceledException
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
from invokeai.backend.tiles.tiles import calc_tiles_min_overlap
from invokeai.backend.tiles.utils import TBLR, Tile


@invocation("spandrel_image_to_image", title="Image-to-Image", tags=["upscale"], category="upscale", version="1.1.0")
class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
    """Run any spandrel image-to-image model (https://github.com/chaiNNer-org/spandrel)."""

    image: ImageField = InputField(description="The input image")
    image_to_image_model: ModelIdentifierField = InputField(
        title="Image-to-Image Model",
        description=FieldDescriptions.spandrel_image_to_image_model,
        ui_type=UIType.SpandrelImageToImageModel,
    )
    tile_size: int = InputField(
        default=512, description="The tile size for tiled image-to-image. Set to 0 to disable tiling."
    )

    def _scale_tile(self, tile: Tile, scale: int) -> Tile:
        return Tile(
            coords=TBLR(
                top=tile.coords.top * scale,
                bottom=tile.coords.bottom * scale,
                left=tile.coords.left * scale,
                right=tile.coords.right * scale,
            ),
            overlap=TBLR(
                top=tile.overlap.top * scale,
                bottom=tile.overlap.bottom * scale,
                left=tile.overlap.left * scale,
                right=tile.overlap.right * scale,
            ),
        )

    @torch.inference_mode()
    def invoke(self, context: InvocationContext) -> ImageOutput:
        # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
        # revisit this.
        image = context.images.get_pil(self.image.image_name, mode="RGB")

        # Compute the image tiles.
        if self.tile_size > 0:
            min_overlap = 20
            tiles = calc_tiles_min_overlap(
                image_height=image.height,
                image_width=image.width,
                tile_height=self.tile_size,
                tile_width=self.tile_size,
                min_overlap=min_overlap,
            )
        else:
            # No tiling. Generate a single tile that covers the entire image.
            min_overlap = 0
            tiles = [
                Tile(
                    coords=TBLR(top=0, bottom=image.height, left=0, right=image.width),
                    overlap=TBLR(top=0, bottom=0, left=0, right=0),
                )
            ]

        # Sort tiles first by left x coordinate, then by top y coordinate. During tile processing, we want to iterate
        # over tiles left-to-right, top-to-bottom.
        tiles = sorted(tiles, key=lambda x: x.coords.left)
        tiles = sorted(tiles, key=lambda x: x.coords.top)

        # Prepare input image for inference.
        image_tensor = SpandrelImageToImageModel.pil_to_tensor(image)

        # Load the model.
        spandrel_model_info = context.models.load(self.image_to_image_model)

        # Run the model on each tile.
        with spandrel_model_info as spandrel_model:
            assert isinstance(spandrel_model, SpandrelImageToImageModel)

            # Scale the tiles for re-assembling the final image.
            scale = spandrel_model.scale
            scaled_tiles = [self._scale_tile(tile, scale=scale) for tile in tiles]

            # Prepare the output tensor.
            _, channels, height, width = image_tensor.shape
            output_tensor = torch.zeros(
                (height * scale, width * scale, channels), dtype=torch.uint8, device=torch.device("cpu")
            )

            image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)

            for tile, scaled_tile in tqdm(list(zip(tiles, scaled_tiles, strict=True)), desc="Upscaling Tiles"):
                # Exit early if the invocation has been canceled.
                if context.util.is_canceled():
                    raise CanceledException

                # Extract the current tile from the input tensor.
                input_tile = image_tensor[
                    :, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right
                ].to(device=spandrel_model.device, dtype=spandrel_model.dtype)

                # Run the model on the tile.
                output_tile = spandrel_model.run(input_tile)

                # Convert the output tile into the output tensor's format.
                # (N, C, H, W) -> (C, H, W)
                output_tile = output_tile.squeeze(0)
                # (C, H, W) -> (H, W, C)
                output_tile = output_tile.permute(1, 2, 0)
                output_tile = output_tile.clamp(0, 1)
                output_tile = (output_tile * 255).to(dtype=torch.uint8, device=torch.device("cpu"))

                # Merge the output tile into the output tensor.
                # We only keep half of the overlap on the top and left side of the tile. We do this in case there are
                # edge artifacts. We don't bother with any 'blending' in the current implementation - for most upscalers
                # it seems unnecessary, but we may find a need in the future.
                top_overlap = scaled_tile.overlap.top // 2
                left_overlap = scaled_tile.overlap.left // 2
                output_tensor[
                    scaled_tile.coords.top + top_overlap : scaled_tile.coords.bottom,
                    scaled_tile.coords.left + left_overlap : scaled_tile.coords.right,
                    :,
                ] = output_tile[top_overlap:, left_overlap:, :]

        # Convert the output tensor to a PIL image.
        np_image = output_tensor.detach().numpy().astype(np.uint8)
        pil_image = Image.fromarray(np_image)
        image_dto = context.images.save(image=pil_image)
        return ImageOutput.build(image_dto)