# Open Source Model Licensed under the Apache License Version 2.0
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.

# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.

# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.

# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

import os
import random

import numpy as np
import torch
from diffusers import DiffusionPipeline
from diffusers import EulerAncestralDiscreteScheduler


class Multiview_Diffusion_Net():
    def __init__(self, config) -> None:
        self.device = config.device
        self.view_size = 512
        multiview_ckpt_path = config.multiview_ckpt_path

        current_file_path = os.path.abspath(__file__)
        custom_pipeline_path = os.path.join(os.path.dirname(current_file_path), '..', 'hunyuanpaint')

        pipeline = DiffusionPipeline.from_pretrained(
            multiview_ckpt_path,
            custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16)

        pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config,
                                                                         timestep_spacing='trailing')

        pipeline.set_progress_bar_config(disable=True)
        self.pipeline = pipeline.to(self.device)

    def seed_everything(self, seed):
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        os.environ["PL_GLOBAL_SEED"] = str(seed)

    def __call__(self, input_image, control_images, camera_info):

        self.seed_everything(0)

        input_image = input_image.resize((self.view_size, self.view_size))
        for i in range(len(control_images)):
            control_images[i] = control_images[i].resize((self.view_size, self.view_size))
            if control_images[i].mode == 'L':
                control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1')

        kwargs = dict(generator=torch.Generator(device=self.pipeline.device).manual_seed(0))

        num_view = len(control_images) // 2
        normal_image = [[control_images[i] for i in range(num_view)]]
        position_image = [[control_images[i + num_view] for i in range(num_view)]]

        camera_info_gen = [camera_info]
        camera_info_ref = [[0]]
        kwargs['width'] = self.view_size
        kwargs['height'] = self.view_size
        kwargs['num_in_batch'] = num_view
        kwargs['camera_info_gen'] = camera_info_gen
        kwargs['camera_info_ref'] = camera_info_ref
        kwargs["normal_imgs"] = normal_image
        kwargs["position_imgs"] = position_image

        mvd_image = self.pipeline(input_image, num_inference_steps=30, **kwargs).images
        return mvd_image