import argparse
import os
from yaml import safe_load, safe_dump
import argparse

from nerf_triplane.provider import NeRFDataset
from nerf_triplane.utils import *
from nerf_triplane.network import NeRFNetwork
from data_utils.hubert import process_audio

def parse_arguments():
    parser = argparse.ArgumentParser(description="NeRF Inference Script")

    # File paths and general configuration options
    parser.add_argument('--path', type=str, help="Path to the input data")
    parser.add_argument('--workspace', type=str, default='workspace', help="Directory for storing intermediate and final results")
    parser.add_argument('--seed', type=int, default=0, help="Random seed for reproducibility")
       # 加载配置文件参数
    parser.add_argument('--config', type=str, default=None, help='Path to configuration file (YAML)') 

    # Test modes
    parser.add_argument('--test', action='store_true', help="Test mode using the test dataset")
    parser.add_argument('--test_train', action='store_true', help="Test mode using the train dataset")

    # Data range
    parser.add_argument('--data_range', type=int, nargs=2, default=[0, -1], help="Range of data indices to use [start, end)")

    # Training options
    parser.add_argument('--iters', type=int, default=200000, help="Number of training iterations")
    parser.add_argument('--lr', type=float, default=1e-2, help="Initial learning rate for the main network")
    parser.add_argument('--lr_net', type=float, default=1e-3, help="Initial learning rate for other networks")

    # Checkpoint management
    parser.add_argument('--ckpt', type=str, default='latest', help="Checkpoint to load or save")

    # Ray sampling settings
    parser.add_argument('--num_rays', type=int, default=4096 * 16, help="Number of rays sampled per image for each training step")
    parser.add_argument('--cuda_ray', action='store_true', help="Use CUDA raymarching instead of PyTorch")
    parser.add_argument('--max_steps', type=int, default=16, help="Max num steps sampled per ray (only valid when using --cuda_ray)")
    parser.add_argument('--num_steps', type=int, default=16, help="Num steps sampled per ray (only valid when NOT using --cuda_ray)")
    parser.add_argument('--upsample_steps', type=int, default=0, help="Num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
    parser.add_argument('--update_extra_interval', type=int, default=16, help="Iter interval to update extra status (only valid when using --cuda_ray)")
    parser.add_argument('--max_ray_batch', type=int, default=4096, help="Batch size of rays at inference to avoid OOM (only valid when NOT using --cuda_ray)")

    # Loss settings
    parser.add_argument('--warmup_step', type=int, default=10000, help="Number of warm up steps")
    parser.add_argument('--amb_aud_loss', action='store_true', help="Use ambient audio loss")
    parser.add_argument('--amb_eye_loss', action='store_true', help="Use ambient eye loss")
    parser.add_argument('--unc_loss', action='store_true', help="Use uncertainty loss")
    parser.add_argument('--lambda_amb', type=float, default=1e-4, help="Lambda for ambient loss")
    parser.add_argument('--pyramid_loss', action='store_true', help="Use perceptual loss")

    # Network backbone options
    parser.add_argument('--fp16', action='store_true', help="Use AMP mixed precision training")
    parser.add_argument('--bg_img', type=str, default='', help="Background image path")
    parser.add_argument('--fbg', action='store_true', help="Frame-wise background")
    parser.add_argument('--exp_eye', action='store_true', help="Explicitly control the eyes")
    parser.add_argument('--fix_eye', type=float, default=-1, help="Fixed eye area (negative to disable)")
    parser.add_argument('--smooth_eye', action='store_true', help="Smooth the eye area sequence")
    parser.add_argument('--bs_area', choices=['upper', 'eye'], default="upper", help="Area for background subtraction ('upper' or 'eye')")
    parser.add_argument('--au45', action='store_true', help="Use OpenFace AU45")
    parser.add_argument('--torso_shrink', type=float, default=0.8, help="Shrink bg coords to allow more flexibility in deform")

    # Dataset options
    parser.add_argument('--color_space', choices=['linear', 'srgb'], default='srgb', help="Color space (supports linear or srgb)")
    parser.add_argument('--preload', type=int, choices=[0, 1, 2], default=0, help="Preload data (0: on-the-fly, 1: CPU, 2: GPU)")
    parser.add_argument('--bound', type=float, default=1, help="Assume the scene is bounded in box[-bound, bound]^3")
    parser.add_argument('--scale', type=float, default=4, help="Scale camera location into box[-bound, bound]^3")
    parser.add_argument('--offset', type=float, nargs=3, default=[0, 0, 0], help="Offset of camera location [x, y, z]")
    parser.add_argument('--dt_gamma', type=float, default=1/256, help="Dt_gamma for adaptive ray marching")
    parser.add_argument('--min_near', type=float, default=0.05, help="Minimum near distance for camera")
    parser.add_argument('--density_thresh', type=float, default=10, help="Threshold for density grid to be occupied (sigma)")
    parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="Threshold for density grid to be occupied (alpha)")
    parser.add_argument('--patch_size', type=int, choices=[1] + [64, 32, 16], default=1, help="Render patches in training for LPIPS loss")

    # Specific training options
    parser.add_argument('--init_lips', action='store_true', help="Initialize lips region")
    parser.add_argument('--finetune_lips', action='store_true', help="Fine tune lips region using LPIPS and landmarks")
    parser.add_argument('--smooth_lips', action='store_true', help="Smooth enc_a in a exponential decay way")
    parser.add_argument('--torso', action='store_true', help="Fix head and train torso")
    parser.add_argument('--head_ckpt', type=str, default='', help="Path to the pre-trained head model")

    # GUI options
    parser.add_argument('--gui', action='store_true', help="Start a GUI interface")
    parser.add_argument('--W', type=int, default=450, help="GUI width (pixels)")
    parser.add_argument('--H', type=int, default=450, help="GUI height (pixels)")
    parser.add_argument('--radius', type=float, default=3.35, help="Default GUI camera radius from center")
    parser.add_argument('--fovy', type=float, default=21.24, help="Default GUI camera field of view in degrees")
    parser.add_argument('--max_spp', type=int, default=1, help="Max samples per pixel for GUI rendering")

    # Other options
    parser.add_argument('--fullbody', action='store_true', help="Enable full body mode")
    parser.add_argument('--att', type=int, choices=[0, 1, 2], default=2, help="Audio attention mode (0 = off, 1 = left-direction, 2 = bi-direction)")
    parser.add_argument('--aud', type=str, default='', help="Path to audio source (empty for default)")
    parser.add_argument('--emb', action='store_true', help="Use audio class + embedding instead of logits")
    parser.add_argument('--portrait', action='store_true', help="Only render face")
   
   # Other options (continued)
    parser.add_argument('--ind_dim', type=int, default=4, help="Dimension of individual codes (0 to turn off)")
    parser.add_argument('--ind_num', type=int, default=20000, help="Number of individual codes (should be larger than training dataset size)")
    parser.add_argument('--ind_dim_torso', type=int, default=8, help="Dimension of torso individual codes (0 to turn off)")
    parser.add_argument('--amb_dim', type=int, default=2, help="Ambient dimension")
    parser.add_argument('--part', action='store_true', help="Use partial training data (1/10)")
    parser.add_argument('--part2', action='store_true', help="Use partial training data (first 15s)")
    parser.add_argument('--train_camera', action='store_true', help="Optimize camera pose")
    parser.add_argument('--smooth_path', action='store_true', help="Smooth camera pose trajectory with a window size")
    parser.add_argument('--smooth_path_window', type=int, default=7, help="Smoothing window size for camera path")

    # ASR settings
    parser.add_argument('--asr', action='store_true', help="Load ASR for real-time application")
    parser.add_argument('--asr_wav', type=str, default='', help="Path to WAV file for input")
    parser.add_argument('--asr_play', action='store_true', help="Play out the audio in real time")
    parser.add_argument('--asr_model', type=str, default='deepspeech', help="ASR model to use")
    parser.add_argument('--asr_save_feats', action='store_true', help="Save features extracted by the ASR model")

    # Audio processing settings
    parser.add_argument('--fps', type=int, default=50, help="Audio frames per second")
    parser.add_argument('-l', type=int, default=10, help="Length of sliding window (left) in 20ms units")
    parser.add_argument('-m', type=int, default=50, help="Length of sliding window (middle) in 20ms units")
    parser.add_argument('-r', type=int, default=10, help="Length of sliding window (right) in 20ms units")

    # Shortcut options for common combinations
    parser.add_argument('--O', action='store_true', help="Shortcut for --fp16 --cuda_ray --exp_eye")

    return parser.parse_args()

def load_config(config_path):
    with open(config_path, 'r', encoding='utf-8') as f:
        config = safe_load(f)
    return config

def merge_configs(base_config, override_config):
    """
    Merge two configurations. Override_config values take precedence.
    """
    for key, value in override_config.items():
        if isinstance(value, dict) and key in base_config and isinstance(base_config[key], dict):
            merge_configs(base_config[key], value)
        else:
            base_config[key] = value
    return base_config

def setup_args(args=None, config_override=None):
    """
    Setup the configuration by merging default, file-based, command-line, and function parameters.
    
    Parameters:
        args: argparse.Namespace or None
            Command-line arguments parsed using argparse.
        config_override: dict or None
            Function-based overrides to merge into the final configuration.
            
    Returns:
        opt: argparse.Namespace
            The final configuration as an argparse.Namespace object.
    """
    # Load default configuration (if available)
    default_config_path = os.path.join(os.path.dirname(__file__), 'default_config.yaml')
    if os.path.exists(default_config_path):
        with open(default_config_path, 'r', encoding='utf-8') as f:
            config = safe_load(f)
    else:
        raise FileNotFoundError("Default configuration file not found.")

    # Override with user-specified configuration file
    if args and args.config is not None:
        override_config = load_config(args.config)
        config = merge_configs(config, override_config)

    # Merge command-line arguments into the configuration
    if args:
        for key, value in vars(args).items():
            if value is not None:
                config[key] = value

    # Override with function-based parameters
    if config_override:
        config = merge_configs(config, config_override)
    
    return argparse.Namespace(**config)

def inference(opt):
    """
    Perform inference using the NeRF model.
    
    Parameters:
        opt: argparse.Namespace
            The options that define how to perform inference, similar to command-line arguments.
            
    Returns:
        results: list or dict
            The output of the test (e.g., metrics) and possibly images/video frames.
    """
    # Close tf32 features. Fix low numerical accuracy on rtx30xx gpu.
    try:
        torch.backends.cuda.matmul.allow_tf32 = False
        torch.backends.cudnn.allow_tf32 = False
    except AttributeError as e:
        print('Info. This PyTorch version is not support with tf32.')
        
    
    seed_everything(opt.seed)
    
    if opt.O:
        opt.fp16 = True
        opt.exp_eye = True

    if opt.test and False:
        opt.smooth_path = True
        opt.smooth_eye = True
        opt.smooth_lips = True

    opt.cuda_ray = True
    # assert opt.cuda_ray, "Only support CUDA ray mode."

    if opt.patch_size > 1:
        # assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
        assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
    
    print(opt)  
    # Load audio features
    if opt.asr_model == 'hubert':
        process_audio(opt.aud)
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = NeRFNetwork(opt)

    # Manually load state dict for head
    if opt.torso and hasattr(opt, 'head_ckpt') and opt.head_ckpt != '':
        model_dict = torch.load(opt.head_ckpt, map_location='cpu')['model']
        missing_keys, unexpected_keys = model.load_state_dict(model_dict, strict=False)
        if len(missing_keys) > 0:
            print(f"[WARN] Missing keys: {missing_keys}")
        if len(unexpected_keys) > 0:
            print(f"[WARN] Unexpected keys: {unexpected_keys}")   
        
        # Freeze these keys
        for k, v in model.named_parameters():
            if k in model_dict:
                print(f'[INFO] Freezing {k}, {v.shape}')
                v.requires_grad = False

    criterion = torch.nn.L1Loss(reduction='none')

    metrics = [PSNRMeter(), LPIPSMeter(device=device), LMDMeter(backend='fan')]

    trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace.strip(), 
                      criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=getattr(opt, 'ckpt', 'latest'))

    if getattr(opt, 'test_train', False):
        test_set = NeRFDataset(opt, device=device, type='train')
        # A manual fix to test on the training dataset
        test_set.training = False 
        test_set.num_rays = -1
        test_loader = test_set.dataloader()
    else:
        test_loader = NeRFDataset(opt, device=device, type='test').dataloader()

    model.aud_features = test_loader._data.auds
    model.eye_areas = test_loader._data.eye_area
    results = None
    if getattr(opt, 'gui', False):
        from nerf_triplane.gui import NeRFGUI
        with NeRFGUI(opt, trainer, test_loader) as gui:
            gui.render()
        results = None  # GUI doesn't return results directly, you may handle it in a different way
    else:
        ### Test and save video (fast)
        results = trainer.test(test_loader)
    return results

if __name__ == "__main__":
    args = parse_arguments()
    if args.config is None:
        args = None
        
    opt = setup_args(args)
    
    # 验证参数（可选）,参数必须设置才能进行推理
    assert os.path.exists(opt.workspace), "Workspace directory does not exist."
    assert os.path.exists(opt.path), "Dataset path does not exist."
    
    results = inference(opt)