from comfy_api.latest import io
import torch

windows = {'bartlet':torch.bartlett_window, 'blackman':torch.blackman_window, 'hamming':torch.hamming_window,'hann':torch.hann_window}

class AudioToSpectrogram(io.ComfyNode):
    """
    Converts Audio to an Image spectrogram.
    Red = Real, Green = logarithm of value (just so it looks good), Blue = Imaginary.
    Each audio channel is stacked vertically.
    """
    @classmethod
    def define_schema(cls) -> io.Schema:
        return io.Schema(
            node_id="mrmth_AudioToImageSpectrogram",
            category="More math",
            display_name="Audio to Image Spectrogram",
            inputs=[
                io.Audio.Input(id="audio", tooltip="Input audio"),
                io.Int.Input(id="window_length", default=1024, min=16, max=4096, tooltip="Window length in samples"),
                io.Int.Input(id="hop_length", default=256, min=1, max=4096, tooltip="Stride of the window (hop length) in samples"),
                io.Int.Input(id="bucket_count", default=513, min=2, max=4096, tooltip="Number of frequency buckets (determines resolution)"),
                io.Combo.Input(id="window_type", default="hann", options=list(windows.keys()), tooltip="Type of window function to apply"),
            ],
            outputs=[
                io.Image.Output(id="image"),
                io.Int.Output(id="channel_count", display_name="Channel count", tooltip="Number of channels in the output image"),
                io.Int.Output(id="sample_rate", display_name="Sample rate", tooltip="Sample rate of the input audio"),
            ],
        )

    @classmethod
    def execute(cls, audio, window_length, hop_length, bucket_count,window_type):
        waveform = audio['waveform']
        sample_rate = audio['sample_rate']
        B, C, S = waveform.shape
        n_fft = (bucket_count - 1) * 2
        flat_waveform = waveform.reshape(B * C, S)
        window = windows[window_type](window_length, device=waveform.device)

        stft_out = torch.stft(
            flat_waveform, n_fft=n_fft, hop_length=hop_length, win_length=window_length,
            window=window, center=True, pad_mode='reflect', normalized=False,
            onesided=True, return_complex=True
        )

        F, T = stft_out.shape[1], stft_out.shape[2]
        stft_out = stft_out.reshape(B, C, F, T).reshape(B, C * F, T)
        image = torch.stack([
            stft_out.real,
            torch.log1p(torch.abs(stft_out)),
            stft_out.imag
        ], dim=-1)
        print(image.type())
        return (image, C, sample_rate)