from inspect import cleandoc
from comfy_api.latest import io
import copy
from .helper_functions import checkLazyNew
from .Stack import MrmthStack
from .ParseTree import MrmthParseTree
import comfy.utils

class VAEMathNode(io.ComfyNode):
    """
    This node enables the use of math expressions on VAE weights using Autogrow inputs.
    """

    @classmethod
    def define_schema(cls) -> io.Schema:
        return io.Schema(
            node_id="mrmth_ag_VAEMathNode",
            display_name="VAE Math",
            category="More math/math",
            inputs=[
                io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Vae.Input("values"), prefix="V", min=1, max=50)),
                io.Autogrow.Input(id="F", template=io.Autogrow.TemplatePrefix(io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True), prefix="F", min=1, max=50)),
                io.MultiType.Input(
                    io.String.Input("Expression", default="V0", multiline=False),
                    types=[io.String,MrmthParseTree],
                    tooltip="Expression to apply on weights",
                ),
                io.Combo.Input(
                    id="length_mismatch",
                    options=["do nothing","error","tile", "pad"],
                    display_name="on size mismatch",
                    default="error",
                    tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
                ),
                io.Boolean.Input(
                    id="dict_mode",
                    default=False,
                    display_name="Dictionary mode",
                    tooltip="Evaluates VAE weights as dictionaries. Per-layer and dimension variables are unavailable.",
                ),
                io.Boolean.Input(
                    id="use_compute_device",
                    default=True,
                    display_name="Move tensors to GPU",
                    tooltip="Temporarily copies VAE tensors to the compute device for math and moves the patches back afterwards.",
                ),
                MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
            ],
            outputs=[
                io.Vae.Output(),
                MrmthStack.Output(),
            ],
        )

    tooltip = cleandoc(__doc__)

    @classmethod
    def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",use_compute_device=True,stack={},dict_mode=False):
        return checkLazyNew(Expression,V,F)

    @classmethod
    def execute(cls, V, F, Expression, length_mismatch="tile",use_compute_device=True,stack={},dict_mode=False) -> io.NodeOutput:
        # Determine reference VAE
        a = V.get("V0")
        if a is None:
             for m in V.values():
                 if m is not None:
                      a = m
                      break
        if a is None:
             raise ValueError("At least one input VAE is required.")
        stack = copy.deepcopy(stack) if stack is not None else {}

        # Prepare VAE patchers for calculation
        # We need to map VAE wrappers to their patchers for `calculate_patches`

        patchers_V = {}
        for k, v in V.items():
            if v is not None:
                patchers_V[k] = v.patcher

        # Calculate patches using the patchers (weights are in patcher.model.state_dict)
        from .modelLikeCommon import calculate_patches_autogrow, calculate_patches_dict_mode
        aliases = {"a": "V0", "b": "V1", "c": "V2", "d": "V3", "w": "F0", "x": "F1", "y": "F2", "z": "F3"}
        ref_patcher = getattr(V.get("V0"), "patcher", None)
        layer_count = ref_patcher.model.state_dict().__len__() if ref_patcher is not None and hasattr(ref_patcher, "model") and hasattr(ref_patcher.model, "state_dict") else 0
        pbar = comfy.utils.ProgressBar(layer_count)
        calculate_patches = calculate_patches_dict_mode if dict_mode else calculate_patches_autogrow
        patches = calculate_patches(Expression, V=patchers_V, F=F, pbar=pbar, mapping=aliases, stack=stack, use_compute_device=use_compute_device)

        # VAE does not have a clone method, so we shallow copy and clone the patcher
        out_vae = copy.copy(a)
        out_vae.patcher = a.patcher.clone()

        if patches:
            out_vae.patcher.add_patches(patches, 1.0, 1.0)

        return (out_vae,stack)