"""
    This file is part of ComfyUI.
    Copyright (C) 2024 Stability AI

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""

import torch
import nodes
import comfy.utils

from comfy_api.v3 import io


class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
    @classmethod
    def DEFINE_SCHEMA(cls):
        return io.SchemaV3(
            node_id="StableCascade_EmptyLatentImage_V3",
            category="latent/stable_cascade",
            inputs=[
                io.Int.Input(
                    "width",
                    default=1024,
                    min=256,
                    max=nodes.MAX_RESOLUTION, step=8,
                ),
                io.Int.Input(
                    "height",
                    default=1024,
                    min=256,
                    max=nodes.MAX_RESOLUTION,
                    step=8,
                ),
                io.Int.Input(
                    "compression",
                    default=42,
                    min=4,
                    max=128,
                    step=1,
                ),
                io.Int.Input(
                    "batch_size",
                    default=1,
                    min=1,
                    max=4096,
                ),
            ],
            outputs=[
                io.Latent.Output(
                    "stage_c",
                    display_name="stage_c",
                ),
                io.Latent.Output(
                    "stage_b",
                    display_name="stage_b",
                ),
            ],
        )

    @classmethod
    def execute(cls, width, height, compression, batch_size=1):
        c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
        b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
        return ({
            "samples": c_latent,
        }, {
            "samples": b_latent,
        })


class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
    @classmethod
    def DEFINE_SCHEMA(cls):
        return io.SchemaV3(
            node_id="StableCascade_StageC_VAEEncode_V3",
            category="latent/stable_cascade",
            inputs=[
                io.Image.Input(
                    "image",
                ),
                io.Vae.Input(
                    "vae",
                ),
                io.Int.Input(
                    "compression",
                    default=42,
                    min=4,
                    max=128,
                    step=1,
                ),
            ],
            outputs=[
                io.Latent.Output(
                    "stage_c",
                    display_name="stage_c",
                ),
                io.Latent.Output(
                    "stage_b",
                    display_name="stage_b",
                ),
            ],
        )

    @classmethod
    def execute(cls, image, vae, compression):
        width = image.shape[-2]
        height = image.shape[-3]
        out_width = (width // compression) * vae.downscale_ratio
        out_height = (height // compression) * vae.downscale_ratio

        s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)

        c_latent = vae.encode(s[:,:,:,:3])
        b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
        return ({
            "samples": c_latent,
        }, {
            "samples": b_latent,
        })


class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
    @classmethod
    def DEFINE_SCHEMA(cls):
        return io.SchemaV3(
            node_id="StableCascade_StageB_Conditioning_V3",
            category="conditioning/stable_cascade",
            inputs=[
                io.Conditioning.Input(
                    "conditioning",
                ),
                io.Latent.Input(
                    "stage_c",
                ),
            ],
            outputs=[
              io.Conditioning.Output(
                  "CONDITIONING",
              ),
            ],
        )

    @classmethod
    def execute(cls, conditioning, stage_c):
        c = []
        for t in conditioning:
            d = t[1].copy()
            d['stable_cascade_prior'] = stage_c['samples']
            n = [t[0], d]
            c.append(n)
        return (c, )


class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
    @classmethod
    def DEFINE_SCHEMA(cls):
        return io.SchemaV3(
            node_id="StableCascade_SuperResolutionControlnet_V3",
            category="_for_testing/stable_cascade",
            is_experimental=True,
            inputs=[
                io.Image.Input(
                    "image",
                ),
                io.Vae.Input(
                    "vae",
                ),
            ],
            outputs=[
                io.Image.Output(
                    "controlnet_input",
                    display_name="controlnet_input",
                ),
                io.Latent.Output(
                    "stage_c",
                    display_name="stage_c",
                ),
                io.Latent.Output(
                    "stage_b",
                    display_name="stage_b",
                ),
            ],
        )

    @classmethod
    def execute(cls, image, vae):
        width = image.shape[-2]
        height = image.shape[-3]
        batch_size = image.shape[0]
        controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)

        c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
        b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
        return (controlnet_input, {
            "samples": c_latent,
        }, {
            "samples": b_latent,
        })


NODES_LIST: list[type[io.ComfyNodeV3]] = [
    StableCascade_EmptyLatentImage_V3,
    StableCascade_StageB_Conditioning_V3,
    StableCascade_StageC_VAEEncode_V3,
    StableCascade_SuperResolutionControlnet_V3,
]