#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
import os
import torch
import tempfile
from contextlib import nullcontext

from mast3r.demo import get_args_parser, main_demo

from mast3r.model import AsymmetricMASt3R
from mast3r.utils.misc import hash_md5

import matplotlib.pyplot as pl
pl.ion()

torch.backends.cuda.matmul.allow_tf32 = True  # for GPU >= Ampere and PyTorch >= 1.12

import argparse

def get_args_parser():
    parser = argparse.ArgumentParser(description="MASt3R Demo")
    parser.add_argument("--weights", type=str, default=None, help="Path to the weights file.")
    parser.add_argument("--model_name", type=str, default=None, choices=[
        'MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric'], help="Name of the model to use.")
    parser.add_argument("--device", type=str, default='cuda', help="Device to run the model on.")
    parser.add_argument("--server_name", type=str, default=None, help="Server name to use.")
    parser.add_argument("--local_network", action='store_true', help="Run on local network.")
    parser.add_argument("--image_size", type=int, choices=[512, 224], default=512, help="Size of the images.")
    parser.add_argument("--server_port", type=int, default=None, help="Port for the server.")
    parser.add_argument("--tmp_dir", type=str, default=None, help="Temporary directory.")
    parser.add_argument("--silent", action='store_true', help="Run silently.")
    parser.add_argument("--share", action='store_true', help="Share the application.")
    parser.add_argument("--gradio_delete_cache", action='store_true', help="Delete Gradio cache.")
    return parser

def get_default_weights_path(model_name):
    # Construct default weights path based on model_name
    return f"naver/{model_name}"
if __name__ == '__main__':
    parser = get_args_parser()
    args = parser.parse_args()

    # Set default values for required arguments
    if args.weights is None and args.model_name is None:
        args.model_name = 'MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric'
    
    if args.weights is None:
        args.weights = f"naver/{args.model_name}"

    # Rest of the code for setting up the server and loading the model
    server_name = args.server_name or ('0.0.0.0' if args.local_network else '127.0.0.1')
    weights_path = args.weights

    # Load the model
    model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
    chkpt_tag = hash_md5(weights_path)

    def get_context(tmp_dir):
        return tempfile.TemporaryDirectory(suffix='_mast3r_gradio_demo') if tmp_dir is None \
            else nullcontext(tmp_dir)

    with get_context(args.tmp_dir) as tmpdirname:
        cache_path = os.path.join(tmpdirname, chkpt_tag)
        os.makedirs(cache_path, exist_ok=True)
        main_demo(cache_path, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent,
                  share=args.share, gradio_delete_cache=args.gradio_delete_cache)