import torch
from .helper_functions import generate_dim_variables, getIndexTensorAlongDim, eval_tensor_expr, as_tensor

from comfy_api.latest import io


from .MathNodeBase import MathNodeBase


class AudioMathNode(MathNodeBase):
    """
    Enables math expressions on Audio tensors.

    Inputs:
        a, b, c, d: Audio inputs (b, c, d default to zero if not provided)
        w, x, y, z: Float variables for expressions
        AudioExpr: Expression to apply on audio tensors

    Outputs:
        AUDIO: Result of applying expression to input audio
    """

    @classmethod
    def define_schema(cls) -> io.Schema:
        return io.Schema(
            node_id="mrmth_AudioMathNode",
            category="More math",
            display_name="Audio math",
            inputs=[
                io.Audio.Input(id="a", tooltip="Input audio tensor"),
                io.Audio.Input(id="b", optional=True, lazy=True, tooltip="Second input audio tensor"),
                io.Audio.Input(id="c", optional=True, lazy=True, tooltip="Third input audio tensor"),
                io.Audio.Input(id="d", optional=True, lazy=True, tooltip="Fourth input audio tensor"),
                io.Float.Input(id="w", default=0.0, optional=True, lazy=True, force_input=True),
                io.Float.Input(id="x", default=0.0, optional=True, lazy=True, force_input=True),
                io.Float.Input(id="y", default=0.0, optional=True, lazy=True, force_input=True),
                io.Float.Input(id="z", default=0.0, optional=True, lazy=True, force_input=True),
                io.String.Input(id="AudioExpr", default="a*(1-w)+b*w", tooltip="Expression to apply on input audio tensors"),
            ],
            outputs=[
                io.Audio.Output(),
            ],
        )

    @classmethod
    def execute(cls, AudioExpr, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
        av = a["waveform"]
        sample_rate = a["sample_rate"]

        a, b, c, d = cls.prepare_inputs(a, b, c, d)

        bv, cv, dv = b["waveform"], c["waveform"], d["waveform"]

        variables = {
            "a": av,
            "b": bv,
            "c": cv,
            "d": dv,
            "w": w,
            "x": x,
            "y": y,
            "z": z,
            "B": getIndexTensorAlongDim(av, 0),
            "C": getIndexTensorAlongDim(av, 1),
            "S": getIndexTensorAlongDim(av, 2),
            "R": torch.full_like(av, sample_rate, dtype=torch.float32),
            "T": torch.full_like(av, av.shape[2], dtype=torch.float32),
            "N": av.shape[1],
            "batch": getIndexTensorAlongDim(av, 0),
            "channel": getIndexTensorAlongDim(av, 1),
            "sample": getIndexTensorAlongDim(av, 2),
            "sample_rate": torch.full_like(av, sample_rate, dtype=torch.float32),
            "sample_count": torch.full_like(av, av.shape[2], dtype=torch.float32),
            "channel_count": av.shape[1],
        } | generate_dim_variables(av)

        result_tensor = eval_tensor_expr(AudioExpr, variables, av.shape)

        return ({"waveform": as_tensor(result_tensor, a.shape), "sample_rate": sample_rate},)