import os
import shlex
import subprocess
import argparse

CUR_DIR = os.path.dirname(os.path.abspath(__file__))
def register_subparsers(subparser):
    parser = subparser.add_parser('evaluate', formatter_class=argparse.RawTextHelpFormatter)
    parser.add_argument(
        "--ngpus",
        type=int,
        default=1,
        help="Number of GPUs to run evaluation on"
        )
    parser.add_argument(
        "--output_path",
        type=str,
        default='./evaluation_results/',
        help="output path to save the evaluation results",
    )
    parser.add_argument(
        "--full_json_dir",
        type=str,
        default=f'{CUR_DIR}/../VBench2_full_info.json',
        help="path to save the json file that contains the prompt and dimension information",
    )
    parser.add_argument(
        "--videos_path",
        type=str,
        required=True,
        help="folder that contains the sampled videos",
    )
    parser.add_argument(
        "--dimension",
        type=str,
        required=True,
        help="list of evaluation dimensions, usage: --dimension <dim_1> <dim_2>",
    )
    parser.add_argument(
        "--load_ckpt_from_local",
        type=bool,
        required=False,
        help="whether load checkpoints from local default paths (assuming you have downloaded the checkpoints locally",
    )
    parser.add_argument(
        "--read_frame",
        type=bool,
        required=False,
        help="whether directly read frames, or directly read videos",
    )
    parser.add_argument(
        "--mode",
        choices=['vbench_standard'],
        default='vbench_standard',
        help="""This flags determine the mode of evaluations, choose one of the following:
        1. "vbench_standard": evaluate on standard prompt suite of VBench
        """,
    )
    parser.add_argument(
        "--prompt",
        type=str,
        default=None,
        help="""Specify the input prompt
        If not specified, filenames will be used as input prompts
        * Mutually exclusive to --prompt_file.
        ** This option must be used with --mode=custom_input flag
        """
    )
    parser.add_argument(
        "--prompt_file",
        type=str,
        required=False,
        help="""Specify the path of the file that contains prompt lists
        If not specified, filenames will be used as input prompts
        * Mutually exclusive to --prompt.
        ** This option must be used with --mode=custom_input flag
        """
    )

    ## for dimension specific params ###
    parser.add_argument(
        "--imaging_quality_preprocessing_mode",
        type=str,
        required=False,
        default='longer',
        help="""This is for setting preprocessing in imaging_quality
        1. 'shorter': if the shorter side is more than 512, the image is resized so that the shorter side is 512.
        2. 'longer': if the longer side is more than 512, the image is resized so that the longer side is 512.
        3. 'shorter_centercrop': if the shorter side is more than 512, the image is resized so that the shorter side is 512. 
        Then the center 512 x 512 after resized is used for evaluation.
        4. 'None': no preprocessing
        """,
    )
    parser.set_defaults(func=evaluate)

def stringify_cmd(cmd_ls):
    cmd = ""
    for string in cmd_ls:
        cmd += string + " "
    return cmd

## TODO
def evaluate(args):
    args_dict = vars(args)
    abs_path_main = os.path.abspath(f'{CUR_DIR}/../launch/evaluate.py')
    abs_path_json = os.path.abspath(f'{CUR_DIR}/../VBench2_full_info.json')
    cmd = [
        'python3',
        abs_path_main,
        '--output_path', os.path.join(args_dict['output_path'], args_dict['dimension']),
        '--full_json_dir', abs_path_json,
        '--videos_path', args_dict['videos_path'],
        '--dimension', args_dict['dimension'],
        '--mode', args_dict['mode'],
        '--imaging_quality_preprocessing_mode', 'longer'
    ]
    subprocess.run(cmd)