from inspect import cleandoc
from comfy_api.latest import io
import copy
from antlr4 import InputStream, CommonTokenStream

from .Parser.MathExprLexer import MathExprLexer
from .Parser.MathExprParser import MathExprParser
import re
from .Stack import MrmthStack

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",
            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.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on weights"),
                io.Combo.Input(
                    id="length_mismatch",
                    options=["tile", "error", "pad"],
                    default="error",
                    tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
                ),
                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",stack={}):

        input_stream = InputStream(Expression)
        lexer = MathExprLexer(input_stream)
        stream = CommonTokenStream(lexer)
        stream.fill()

        # Support aliases
        aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
        aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}

        needed = []
        needed1 = []
        for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
            var_name = token.text

            if re.match(r"[VF][0-9]+", var_name):
                needed.append(var_name)
            elif var_name in aliases_img:
                needed.append(aliases_img[var_name])
            elif var_name in aliases_flt:
                needed.append(aliases_flt[var_name])
        for v in needed:
            if v.startswith("V"):
                if v not in V or V[v] is None:
                    needed1.append(v)
            elif v.startswith("F"):
                if v not in F or F[v] is None:
                    needed1.append(v)
        return needed1

    @classmethod
    def execute(cls, V, F, Expression, length_mismatch="tile",stack={}) -> 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.")


        # 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
        aliases = {"a": "V0", "b": "V1", "c": "V2", "d": "V3", "w": "F0", "x": "F1", "y": "F2", "z": "F3"}
        patches = calculate_patches_autogrow(Expression, V=patchers_V, F=F, mapping=aliases,stack=stack)

        # 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,)