import argparse
import json
import os
from datetime import datetime
import subprocess

# the emprical settings for each dataset
full_datasets = {
    "MathVista_MINI": "train_prompt_sampling",
    "MathVision": "train_prompt_greedy",
    "MathVerse_MINI": "train_prompt_greedy",
    "MMMU_DEV_VAL": "origin_prompt_greedy",
    "MMStar": "train_prompt_greedy",
    "DynaMath": "train_prompt_greedy",
    "WeMath": "train_prompt_greedy",
    "TextVQA_VAL": "origin_prompt_greedy",
    "DocVQA_TEST": "origin_prompt_greedy",
    "MMVet": "origin_prompt_greedy",
}

settings = {
    "train_prompt_sampling": {
        "use_reasoning_prompt": 2,
        "do_sample": True,
        "top_p": 1,
        "top_k": -1,
        "temperature": 1,
    },
    "train_prompt_greedy": {
        "use_reasoning_prompt": 2,
        "do_sample": True,
        "top_p": 0.001,
        "top_k": 1,
        "temperature": 0.01,
    },
    "origin_prompt_greedy": {
        "use_reasoning_prompt": 0,
        "do_sample": True,
        "top_p": 0.001,
        "top_k": 1,
        "temperature": 0.01,
    },
}


def main():
    parser = argparse.ArgumentParser()

    parser.add_argument("--run_name", type=str, required=True, help="Name of the run")
    parser.add_argument("--gpus", type=int, default=8, help="Number of GPUs to use")
    parser.add_argument("--path", type=str, required=True, help="Path to the model")
    parser.add_argument(
        "--dataset", type=str, nargs="+", required=True, help="List of datasets to use"
    )

    parser.add_argument(
        "--min_pixels", type=int, default=3136, help="Minimum number of pixels"
    )
    parser.add_argument(
        "--max_pixels", type=int, default=12845056, help="Maximum number of pixels"
    )
    parser.add_argument(
        "--max_new_tokens", type=int, default=2048, help="Maximum number of new tokens"
    )

    args = parser.parse_args()
    assert len(args.dataset), "--dataset should be a list of datasets"

    datasets = args.dataset
    if len(args.dataset) == 1 and args.dataset[0] == "full":
        datasets = list(full_datasets.keys())

    for dataset in datasets:
        assert (
            dataset in full_datasets
        ), f"Dataset {dataset} is not in the list of available datasets: {list(full_datasets.keys())}"

    print("Datasets to be used:", datasets)
    print("Run name:", args.run_name)
    print("Number of GPUs:", args.gpus)
    print("Model path:", args.path)

    for dataset in datasets:
        config = {
            "model": {
                args.run_name: {
                    "class": "Qwen2VLChat",
                    "model_path": args.path,
                    "min_pixels": args.min_pixels,
                    "max_pixels": args.max_pixels,
                    "use_vllm": True,
                    "max_new_tokens": args.max_new_tokens,
                    **settings[full_datasets[dataset]],
                },
            },
            "datasets": datasets,
        }

        current_datetime = datetime.now().strftime("%Y%m%d")
        save_dir = f"public_eval/{args.run_name}/{dataset}/{current_datetime}"
        os.makedirs(save_dir, exist_ok=True)

        config_name = f"config.json"
        config_path = os.path.join(save_dir, config_name)
        with open(config_path, "w") as json_file:
            json.dump(config, json_file, indent=4)

        print(f"Start evaluating on {dataset}.")
        print(f"Eval config {full_datasets[dataset]}")
        
        env_vars = os.environ.copy()
        env_vars["VLLM_USE_V1"] = "0"

        command = [
            "torchrun",
            f"--nproc_per_node={args.gpus}",
            "run_for_bash.py",
            "--config",
            f"{config_path}",
            "--data",
            f"{dataset}",
            "--verbose",
            "--work-dir",
            f"{save_dir}",
        ]

        stdout_file = os.path.join(save_dir, f"{dataset}_stdout.log")
        stderr_file = os.path.join(save_dir, f"{dataset}_stderr.log")

        with open(stdout_file, "w") as stdout, open(stderr_file, "w") as stderr:
            try:
                print(f"Output redirected to {stdout_file}")
                print(f"Errors redirected to {stderr_file}")
                subprocess.run(
                    command, env=env_vars, check=True, stdout=stdout, stderr=stderr
                )
                # os.symlink(source, link_name)

            except subprocess.CalledProcessError as e:
                print(f"torchrun failed. Check {stderr_file} for error details.")


if __name__ == "__main__":
    main()