from torch import Tensor
import torch
from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion
from .logger import logger


WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")


class DefaultWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "optional": {
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"

    def load_weights(self, cn_extras: dict[str]={}):
        weights = ControlWeights.default(extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 


class ScaledSoftMaskedUniversalWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mask": ("MASK", ),
                "min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
                "max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
                #"lock_min": ("BOOLEAN", {"default": False}, ),
                #"lock_max": ("BOOLEAN", {"default": False}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"

    def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        # normalize mask
        mask = mask.clone()
        x_min = 0.0 if lock_min else mask.min()
        x_max = 1.0 if lock_max else mask.max()
        if x_min == x_max:
            mask = torch.ones_like(mask) * max_base_multiplier
        else:
            mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
        weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))


class ScaledSoftUniversalWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"

    def load_weights(self, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))


class SoftControlNetWeightsSD15:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "output_0": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_1": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_2": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_3": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_4": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_5": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_6": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_7": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_8": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_9": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"

    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
                     output_7, output_8, output_9, output_10, output_11, middle_0,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        return CustomControlNetWeightsSD15.load_weights(self,
                                                        output_0=output_0, output_1=output_1, output_2=output_2, output_3=output_3,
                                                        output_4=output_4, output_5=output_5, output_6=output_6, output_7=output_7,
                                                        output_8=output_8, output_9=output_9, output_10=output_10, output_11=output_11,
                                                        middle_0=middle_0,
                                                        uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)


class CustomControlNetWeightsSD15:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "output_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "output_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"

    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
                     output_7, output_8, output_9, output_10, output_11, middle_0,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                          output_7, output_8, output_9, output_10, output_11]
        weights_middle = [middle_0]
        weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))


class CustomControlNetWeightsFlux:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_13": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_14": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_15": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_16": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_17": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_18": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"

    def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6, 
                     input_7, input_8, input_9, input_10, input_11, input_12, input_13,
                     input_14, input_15, input_16, input_17, input_18,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        weights_input = [input_0, input_1, input_2, input_3, input_4, input_5,
                         input_6, input_7, input_8, input_9, input_10, input_11,
                         input_12, input_13, input_14, input_15, input_16, input_17, input_18]
        weights = ControlWeights.controlnet(weights_input=weights_input, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))


class SoftT2IAdapterWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_0": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_1": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_2": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"

    def load_weights(self, input_0, input_1, input_2, input_3,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        return CustomT2IAdapterWeights.load_weights(self, input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
                                                    uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)


class CustomT2IAdapterWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
            },
            "optional": {
                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"

    def load_weights(self, input_0, input_1, input_2, input_3,
                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
        weights = [input_0, input_1, input_2, input_3]
        weights = get_properly_arranged_t2i_weights(weights)
        weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))