from __future__ import annotations

from typing import Type, TYPE_CHECKING
from comfy_api.internal import ComfyAPIBase
from comfy_api.internal.singleton import ProxiedSingleton
from comfy_api.internal.async_to_sync import create_sync_class
from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
from comfy_execution.utils import get_executing_context
from comfy_execution.progress import get_progress_state, PreviewImageTuple
from PIL import Image
from comfy.cli_args import args
import numpy as np


class ComfyAPI_latest(ComfyAPIBase):
    VERSION = "latest"
    STABLE = False

    class Execution(ProxiedSingleton):
        async def set_progress(
            self,
            value: float,
            max_value: float,
            node_id: str | None = None,
            preview_image: Image.Image | ImageInput | None = None,
            ignore_size_limit: bool = False,
        ) -> None:
            """
            Update the progress bar displayed in the ComfyUI interface.

            This function allows custom nodes and API calls to report their progress
            back to the user interface, providing visual feedback during long operations.

            Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
            """
            executing_context = get_executing_context()
            if node_id is None and executing_context is not None:
                node_id = executing_context.node_id
            if node_id is None:
                raise ValueError("node_id must be provided if not in executing context")

            # Convert preview_image to PreviewImageTuple if needed
            to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image
            if to_display is not None:
                # First convert to PIL Image if needed
                if isinstance(to_display, ImageInput):
                    # Convert ImageInput (torch.Tensor) to PIL Image
                    # Handle tensor shape [B, H, W, C] -> get first image if batch
                    tensor = to_display
                    if len(tensor.shape) == 4:
                        tensor = tensor[0]

                    # Convert to numpy array and scale to 0-255
                    image_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
                    to_display = Image.fromarray(image_np)

                if isinstance(to_display, Image.Image):
                    # Detect image format from PIL Image
                    image_format = to_display.format if to_display.format else "JPEG"
                    # Use None for preview_size if ignore_size_limit is True
                    preview_size = None if ignore_size_limit else args.preview_size
                    to_display = (image_format, to_display, preview_size)

            get_progress_state().update_progress(
                node_id=node_id,
                value=value,
                max_value=max_value,
                image=to_display,
            )

    execution: Execution

class Input:
    Image = ImageInput
    Audio = AudioInput
    Mask = MaskInput
    Latent = LatentInput
    Video = VideoInput

class InputImpl:
    VideoFromFile = VideoFromFile
    VideoFromComponents = VideoFromComponents

class Types:
    VideoCodec = VideoCodec
    VideoContainer = VideoContainer
    VideoComponents = VideoComponents

ComfyAPI = ComfyAPI_latest

# Create a synchronous version of the API
if TYPE_CHECKING:
    import comfy_api.latest.generated.ComfyAPISyncStub  # type: ignore

    ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
ComfyAPISync = create_sync_class(ComfyAPI_latest)