# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)

from datetime import datetime, timezone
from typing import Literal, Union

from pydantic import Field

from invokeai.app.models.image import ImageField, ImageType
from ..services.invocation_services import InvocationServices
from .baseinvocation import BaseInvocation, InvocationContext
from .image import ImageOutput


class UpscaleInvocation(BaseInvocation):
    """Upscales an image."""
    #fmt: off
    type: Literal["upscale"] = "upscale"

    # Inputs
    image: Union[ImageField, None] = Field(description="The input image", default=None)
    strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
    level: Literal[2, 4] = Field(default=2, description="The upscale level")
    #fmt: on

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get(
            self.image.image_type, self.image.image_name
        )
        results = context.services.restoration.upscale_and_reconstruct(
            image_list=[[image, 0]],
            upscale=(self.level, self.strength),
            strength=0.0,  # GFPGAN strength
            save_original=False,
            image_callback=None,
        )

        # Results are image and seed, unwrap for now
        # TODO: can this return multiple results?
        image_type = ImageType.RESULT
        image_name = context.services.images.create_name(
            context.graph_execution_state_id, self.id
        )
        context.services.images.save(image_type, image_name, results[0][0])
        return ImageOutput(
            image=ImageField(image_type=image_type, image_name=image_name)
        )