# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
from omegaconf import OmegaConf
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from latentsync.models.unet import UNet3DConditionModel
from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline
from accelerate.utils import set_seed
from latentsync.whisper.audio2feature import Audio2Feature


def main(config, args):
    if not os.path.exists(args.video_path):
        raise RuntimeError(f"Video path '{args.video_path}' not found")
    if not os.path.exists(args.audio_path):
        raise RuntimeError(f"Audio path '{args.audio_path}' not found")

    # Check if the GPU supports float16
    is_fp16_supported = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] > 7
    dtype = torch.float16 if is_fp16_supported else torch.float32

    print(f"Input video path: {args.video_path}")
    print(f"Input audio path: {args.audio_path}")
    print(f"Loaded checkpoint path: {args.inference_ckpt_path}")

    # Use relative path for scheduler configuration
    current_dir = os.path.dirname(os.path.abspath(__file__))
    scheduler_path = os.path.join(current_dir, "..", "configs", "scheduler")
    
    # Check if scheduler directory exists
    if not os.path.exists(scheduler_path):
        print(f"Creating scheduler directory at {scheduler_path}")
        os.makedirs(scheduler_path, exist_ok=True)
        
        # Create scheduler config file if it doesn't exist
        scheduler_config_file = os.path.join(scheduler_path, "scheduler_config.json")
        config_file = os.path.join(scheduler_path, "config.json")
        
        if not os.path.exists(scheduler_config_file):
            # Default scheduler config
            scheduler_config = {
                "_class_name": "DDIMScheduler",
                "beta_end": 0.012,
                "beta_schedule": "scaled_linear",
                "beta_start": 0.00085,
                "clip_sample": False,
                "num_train_timesteps": 1000,
                "set_alpha_to_one": False,
                "steps_offset": 1,
                "trained_betas": None,
                "skip_prk_steps": True
            }
            
            import json
            with open(scheduler_config_file, 'w') as f:
                json.dump(scheduler_config, f, indent=2)
            
            # Also create a copy as config.json for compatibility
            with open(config_file, 'w') as f:
                json.dump(scheduler_config, f, indent=2)
    
    print(f"Loading scheduler from: {scheduler_path}")
    try:
        scheduler = DDIMScheduler.from_pretrained(scheduler_path)
    except Exception as e:
        print(f"Error loading scheduler: {e}")
        # Fallback to creating scheduler directly
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
            steps_offset=1,
            skip_prk_steps=True
        )

    # Use relative paths for whisper models as well
    if config.model.cross_attention_dim == 768:
        whisper_model_path = os.path.join(current_dir, "..", "checkpoints", "whisper", "small.pt")
    elif config.model.cross_attention_dim == 384:
        whisper_model_path = os.path.join(current_dir, "..", "checkpoints", "whisper", "tiny.pt")
    else:
        raise NotImplementedError("cross_attention_dim must be 768 or 384")

    audio_encoder = Audio2Feature(
        model_path=whisper_model_path,
        device="cuda",
        num_frames=config.data.num_frames,
        audio_feat_length=config.data.audio_feat_length,
    )

    vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=dtype)
    vae.config.scaling_factor = 0.18215
    vae.config.shift_factor = 0

    denoising_unet, _ = UNet3DConditionModel.from_pretrained(
        OmegaConf.to_container(config.model),
        args.inference_ckpt_path,
        device="cpu",
    )

    denoising_unet = denoising_unet.to(dtype=dtype)

    pipeline = LipsyncPipeline(
        vae=vae,
        audio_encoder=audio_encoder,
        denoising_unet=denoising_unet,
        scheduler=scheduler,
    ).to("cuda")

    if args.seed != -1:
        set_seed(args.seed)
    else:
        torch.seed()

    print(f"Initial seed: {torch.initial_seed()}")

    pipeline(
        video_path=args.video_path,
        audio_path=args.audio_path,
        video_out_path=args.video_out_path,
        video_mask_path=args.video_out_path.replace(".mp4", "_mask.mp4"),
        num_frames=config.data.num_frames,
        num_inference_steps=args.inference_steps,
        guidance_scale=args.guidance_scale,
        weight_dtype=dtype,
        width=config.data.resolution,
        height=config.data.resolution,
        mask_image_path=config.data.mask_image_path,
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--unet_config_path", type=str, default="configs/unet.yaml")
    parser.add_argument("--inference_ckpt_path", type=str, required=True)
    parser.add_argument("--video_path", type=str, required=True)
    parser.add_argument("--audio_path", type=str, required=True)
    parser.add_argument("--video_out_path", type=str, required=True)
    parser.add_argument("--inference_steps", type=int, default=20)
    parser.add_argument("--guidance_scale", type=float, default=1.0)
    parser.add_argument("--seed", type=int, default=1247)
    args = parser.parse_args()

    config = OmegaConf.load(args.unet_config_path)

    main(config, args)