import logging
import torch
import comfy.utils
import comfy.model_management
import comfy.model_base
from comfy.lora import weight_decompose, pad_tensor_to_shape

from .base import WeightAdapterBase


class GLoRAAdapter(WeightAdapterBase):
    name = "glora"

    def __init__(self, loaded_keys, weights):
        self.loaded_keys = loaded_keys
        self.weights = weights

    @classmethod
    def load(
        cls,
        x: str,
        lora: dict[str, torch.Tensor],
        alpha: float,
        dora_scale: torch.Tensor,
        loaded_keys: set[str] = None,
    ) -> "GLoRAAdapter" | None:
        if loaded_keys is None:
            loaded_keys = set()
        a1_name = "{}.a1.weight".format(x)
        a2_name = "{}.a2.weight".format(x)
        b1_name = "{}.b1.weight".format(x)
        b2_name = "{}.b2.weight".format(x)
        if a1_name in lora:
            weights = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
            loaded_keys.add(a1_name)
            loaded_keys.add(a2_name)
            loaded_keys.add(b1_name)
            loaded_keys.add(b2_name)
            return cls(loaded_keys, weights)
        else:
            return None

    def calculate_weight(
        self,
        weight,
        key,
        strength,
        strength_model,
        offset,
        function,
        intermediate_dtype=torch.float32,
        original_weight=None,
    ):
        pass