from __future__ import annotations

import torch

import comfy.utils
import comfy_extras.nodes_post_processing
from comfy_api.v3 import io


def reshape_latent_to(target_shape, latent, repeat_batch=True):
    if latent.shape[1:] != target_shape[1:]:
        latent = comfy.utils.common_upscale(
            latent, target_shape[-1], target_shape[-2], "bilinear", "center"
        )
    if repeat_batch:
        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
    return latent


class LatentAdd(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentAdd_V3",
            category="latent/advanced",
            inputs=[
                io.Latent.Input(id="samples1"),
                io.Latent.Input(id="samples2"),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples1, samples2):
        samples_out = samples1.copy()

        s1 = samples1["samples"]
        s2 = samples2["samples"]

        s2 = reshape_latent_to(s1.shape, s2)
        samples_out["samples"] = s1 + s2
        return io.NodeOutput(samples_out)


class LatentApplyOperation(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentApplyOperation_V3",
            category="latent/advanced/operations",
            is_experimental=True,
            inputs=[
                io.Latent.Input(id="samples"),
                io.LatentOperation.Input(id="operation"),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples, operation):
        samples_out = samples.copy()

        s1 = samples["samples"]
        samples_out["samples"] = operation(latent=s1)
        return io.NodeOutput(samples_out)


class LatentApplyOperationCFG(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentApplyOperationCFG_V3",
            category="latent/advanced/operations",
            is_experimental=True,
            inputs=[
                io.Model.Input(id="model"),
                io.LatentOperation.Input(id="operation"),
            ],
            outputs=[
                io.Model.Output(),
            ],
        )

    @classmethod
    def execute(cls, model, operation):
        m = model.clone()

        def pre_cfg_function(args):
            conds_out = args["conds_out"]
            if len(conds_out) == 2:
                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
            else:
                conds_out[0] = operation(latent=conds_out[0])
            return conds_out

        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
        return io.NodeOutput(m)


class LatentBatch(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentBatch_V3",
            category="latent/batch",
            inputs=[
                io.Latent.Input(id="samples1"),
                io.Latent.Input(id="samples2"),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples1, samples2):
        samples_out = samples1.copy()
        s1 = samples1["samples"]
        s2 = samples2["samples"]

        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
        s = torch.cat((s1, s2), dim=0)
        samples_out["samples"] = s
        samples_out["batch_index"] = (samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) +
                                      samples2.get("batch_index", [x for x in range(0, s2.shape[0])]))
        return io.NodeOutput(samples_out)


class LatentBatchSeedBehavior(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentBatchSeedBehavior_V3",
            category="latent/advanced",
            inputs=[
                io.Latent.Input(id="samples"),
                io.Combo.Input(id="seed_behavior", options=["random", "fixed"], default="fixed"),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples, seed_behavior):
        samples_out = samples.copy()
        latent = samples["samples"]
        if seed_behavior == "random":
            if 'batch_index' in samples_out:
                samples_out.pop('batch_index')
        elif seed_behavior == "fixed":
            batch_number = samples_out.get("batch_index", [0])[0]
            samples_out["batch_index"] = [batch_number] * latent.shape[0]

        return io.NodeOutput(samples_out)


class LatentInterpolate(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentInterpolate_V3",
            category="latent/advanced",
            inputs=[
                io.Latent.Input(id="samples1"),
                io.Latent.Input(id="samples2"),
                io.Float.Input(id="ratio", default=1.0, min=0.0, max=1.0, step=0.01),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples1, samples2, ratio):
        samples_out = samples1.copy()

        s1 = samples1["samples"]
        s2 = samples2["samples"]

        s2 = reshape_latent_to(s1.shape, s2)

        m1 = torch.linalg.vector_norm(s1, dim=(1))
        m2 = torch.linalg.vector_norm(s2, dim=(1))

        s1 = torch.nan_to_num(s1 / m1)
        s2 = torch.nan_to_num(s2 / m2)

        t = (s1 * ratio + s2 * (1.0 - ratio))
        mt = torch.linalg.vector_norm(t, dim=(1))
        st = torch.nan_to_num(t / mt)

        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
        return io.NodeOutput(samples_out)


class LatentMultiply(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentMultiply_V3",
            category="latent/advanced",
            inputs=[
                io.Latent.Input(id="samples"),
                io.Float.Input(id="multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples, multiplier):
        samples_out = samples.copy()

        s1 = samples["samples"]
        samples_out["samples"] = s1 * multiplier
        return io.NodeOutput(samples_out)


class LatentOperationSharpen(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentOperationSharpen_V3",
            category="latent/advanced/operations",
            is_experimental=True,
            inputs=[
                io.Int.Input(id="sharpen_radius", default=9, min=1, max=31, step=1),
                io.Float.Input(id="sigma", default=1.0, min=0.1, max=10.0, step=0.1),
                io.Float.Input(id="alpha", default=0.1, min=0.0, max=5.0, step=0.01),
            ],
            outputs=[
                io.LatentOperation.Output(),
            ],
        )

    @classmethod
    def execute(cls, sharpen_radius, sigma, alpha):
        def sharpen(latent, **kwargs):
            luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
            normalized_latent = latent / luminance
            channels = latent.shape[1]

            kernel_size = sharpen_radius * 2 + 1
            kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
            center = kernel_size // 2

            kernel *= alpha * -10
            kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0

            padded_image = torch.nn.functional.pad(
                normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), "reflect"
            )
            sharpened = torch.nn.functional.conv2d(
                padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels
            )[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]

            return luminance * sharpened
        return io.NodeOutput(sharpen)


class LatentOperationTonemapReinhard(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentOperationTonemapReinhard_V3",
            category="latent/advanced/operations",
            is_experimental=True,
            inputs=[
                io.Float.Input(id="multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
            ],
            outputs=[
                io.LatentOperation.Output(),
            ],
        )

    @classmethod
    def execute(cls, multiplier):
        def tonemap_reinhard(latent, **kwargs):
            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
            normalized_latent = latent / latent_vector_magnitude

            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)

            top = (std * 5 + mean) * multiplier

            #reinhard
            latent_vector_magnitude *= (1.0 / top)
            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
            new_magnitude *= top

            return normalized_latent * new_magnitude
        return io.NodeOutput(tonemap_reinhard)


class LatentSubtract(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LatentSubtract_V3",
            category="latent/advanced",
            inputs=[
                io.Latent.Input(id="samples1"),
                io.Latent.Input(id="samples2"),
            ],
            outputs=[
                io.Latent.Output(),
            ],
        )

    @classmethod
    def execute(cls, samples1, samples2):
        samples_out = samples1.copy()

        s1 = samples1["samples"]
        s2 = samples2["samples"]

        s2 = reshape_latent_to(s1.shape, s2)
        samples_out["samples"] = s1 - s2
        return io.NodeOutput(samples_out)


NODES_LIST = [
    LatentAdd,
    LatentApplyOperation,
    LatentApplyOperationCFG,
    LatentBatch,
    LatentBatchSeedBehavior,
    LatentInterpolate,
    LatentMultiply,
    LatentOperationSharpen,
    LatentOperationTonemapReinhard,
    LatentSubtract,
]
