import torch
import torch.distributed as dist
import transformers
import shutil 

from llava.model.builder import load_pretrained_model

from vlmeval.config import supported_VLM
from vlmeval.dataset import build_dataset
from vlmeval.inference import infer_data_job
from vlmeval.inference_video import infer_data_job_video
from vlmeval.inference_mt import infer_data_job_mt
from vlmeval.smp import *
from vlmeval.utils.result_transfer import MMMU_result_transfer, MMTBench_result_transfer


def parse_args():
    parser = argparse.ArgumentParser()
    
    parser.add_argument('--model_name_or_path', type=str, default='/workspace/0612_llava/LLaVA/pruned_checkpoints/llava-v1.5-7b-temp', required=False)
    # Essential Args
    parser.add_argument('--data', type=str, nargs='+', required=True)
    parser.add_argument('--model', type=str, nargs='+', required=True)
    # Args that only apply to Video Dataset
    parser.add_argument('--nframe', type=int, default=8)
    parser.add_argument('--pack', action='store_true')
    parser.add_argument('--use-subtitle', action='store_true')
    parser.add_argument('--fps', type=float, default=-1)
    # Work Dir
    parser.add_argument('--work-dir', type=str, default='./outputs', help='select the output directory')
    # Infer + Eval or Infer Only
    parser.add_argument('--mode', type=str, default='all', choices=['all', 'infer'])
    # API Kwargs, Apply to API VLMs and Judge API LLMs
    parser.add_argument('--nproc', type=int, default=4, help='Parallel API calling')
    parser.add_argument('--retry', type=int, default=None, help='retry numbers for API VLMs')
    # Explicitly Set the Judge Model
    parser.add_argument('--judge', type=str, default=None)
    # Logging Utils
    parser.add_argument('--verbose', action='store_true')
    # Configuration for Resume
    # Ignore: will not rerun failed VLM inference
    parser.add_argument('--ignore', action='store_true', help='Ignore failed indices. ')
    # Rerun: will remove all evaluation temp files
    parser.add_argument('--rerun', action='store_true')
    parser.add_argument('--compressed', type=str, default="none")
    parser.add_argument('--limit', type=int, default=None)
    #  🔍 🔍 🔍 fastv
    parser.add_argument('--fast_v_sys_length', type=int, default=None)
    parser.add_argument('--fast_v_image_token_length', type=int, default=None)
    parser.add_argument('--fast_v_attention_rank', type=int, default=None)
    parser.add_argument('--fast_v_agg_layer', type=int, default=None)
    parser.add_argument('--fast_v_inplace', action='store_true')
    parser.add_argument('--use_fast_v', action='store_true')

    
    args = parser.parse_args()
    return args


def main():
    logger = get_logger('RUN')

    args = parse_args()
    assert len(args.data), '--data should be a list of data files'

    if 'MMEVAL_ROOT' in os.environ:
        args.work_dir = os.environ['MMEVAL_ROOT']

    if args.retry is not None:
        for k, v in supported_VLM.items():
            if hasattr(v, 'keywords') and 'retry' in v.keywords:
                v.keywords['retry'] = args.retry
                supported_VLM[k] = v
            if hasattr(v, 'keywords') and 'verbose' in v.keywords:
                v.keywords['verbose'] = args.verbose
                supported_VLM[k] = v

    rank, world_size = get_rank_and_world_size()
    if world_size > 1:
        local_rank = os.environ.get('LOCAL_RANK', 0)
        torch.cuda.set_device(int(local_rank))
        dist.init_process_group(backend='nccl', timeout=datetime.timedelta(seconds=10800))

    for _, model_name in enumerate(args.model):
        model = None

        pred_root = osp.join(args.work_dir, model_name)
        os.makedirs(pred_root, exist_ok=True)

        # 🔍🔍🔍 for pruned models, only one
        model_key = model_name
        
        for _, dataset_name in enumerate(args.data):
            try:
                dataset_kwargs = {}
                if dataset_name in ['MMLongBench_DOC', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI']:
                    dataset_kwargs['model'] = model_name
                if dataset_name == 'MMBench-Video':
                    dataset_kwargs['pack'] = args.pack
                if dataset_name == 'Video-MME':
                    dataset_kwargs['use_subtitle'] = args.use_subtitle

                # If distributed, first build the dataset on the main process for doing preparation works
                if world_size > 1:
                    dataset = build_dataset(dataset_name, **dataset_kwargs) if rank == 0 else None
                    dist.barrier()
                    dataset_list = [dataset]
                    dist.broadcast_object_list(dataset_list, src=0)
                    dataset = dataset_list[0]
                else:
                    dataset = build_dataset(dataset_name, **dataset_kwargs)
                if dataset is None:
                    logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ')
                    continue

                result_file = f'{pred_root}/{model_name}_{dataset_name}.xlsx'
                if args.fps > 0:  # For Video Dataset, set the fps for priority
                    if dataset_name == 'MVBench':
                        raise ValueError('MVBench does not support fps setting, please transfer to MVBench_MP4!')
                    args.nframe = 0
                if dataset_name in ['MMBench-Video']:
                    packstr = 'pack' if args.pack else 'nopack'
                    if args.nframe > 0:
                        result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
                    else:
                        result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.fps}fps_{packstr}.xlsx'
                elif dataset.MODALITY == 'VIDEO':
                    if args.pack:
                        logger.info(f'{dataset_name} not support Pack Mode, directly change to unpack')
                        args.pack = False
                    packstr = 'pack' if args.pack else 'nopack'
                    if args.nframe > 0:
                        result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
                    else:
                        result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.fps}fps_{packstr}.xlsx'
                    if dataset_name in ['Video-MME']:
                        subtitlestr = 'subs' if args.use_subtitle else 'nosubs'
                        result_file = result_file.replace('.xlsx', f'_{subtitlestr}.xlsx')

                if dataset.TYPE == 'MT':
                    result_file = result_file.replace('.xlsx', '.tsv')

                if osp.exists(result_file) and args.rerun:
                    for keyword in ['openai', 'gpt', 'auxmatch']:
                        os.system(f'rm {pred_root}/{model_name}_{dataset_name}_{keyword}*')

                if model is None:
                    model = model_name  # which is only a name
                
                # 🔍🔍🔍 copy the config.json of dropped models.
                model_path = supported_VLM[model_key].keywords['model_path']
                
                # print(f"args.compressed: {args.compressed}")
                # print(f"args.model_name_or_path: {args.model_name_or_path}")
                if args.model_name_or_path != model_path:
                    if args.compressed == "pruning": # dropping, copy the files. 
                        supported_VLM[model_key].keywords['model_path'] = args.model_name_or_path
                        model_path =  args.model_name_or_path
                    else:  # dropped or origin
                        shutil.copyfile(os.path.join(args.model_name_or_path, "config.json"), os.path.join(model_path, "config.json"))    
                        
                print(f"model_path: {model_path}")
                config = transformers.AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=True)

                # 🔍🔍🔍 change config for fastv. 
                if isinstance(args.fast_v_agg_layer, int):
                    drop_block_list = []
                    for i in range(args.fast_v_agg_layer, 32):
                        drop_block_list.append(i)
                    drop_block_list.sort()                
                    setattr(config, "drop_block_list", drop_block_list)

                    setattr(config, "use_fast_v", args.use_fast_v)
                    setattr(config, "fast_v_inplace", args.fast_v_inplace)
                    setattr(config, "fast_v_agg_layer", args.fast_v_agg_layer)
                    setattr(config, "fast_v_sys_length", args.fast_v_sys_length)
                    setattr(config, "fast_v_image_token_length", args.fast_v_image_token_length)
                    setattr(config, "fast_v_attention_rank", args.fast_v_attention_rank)
                    
                print(f"model_path: {supported_VLM[model_key].keywords['model_path']}")
                config.save_pretrained(model_path)
                
                # Perform the Inference
                if dataset.MODALITY == 'VIDEO':
                    model = infer_data_job_video(
                        model,
                        work_dir=pred_root,
                        model_name=model_name,
                        dataset=dataset,
                        nframe=args.nframe,
                        pack=args.pack,
                        verbose=args.verbose,
                        subtitle=args.use_subtitle,
                        api_nproc=args.nproc,
                        fps=args.fps, 
                        limit=args.limit,
                        )
                elif dataset.TYPE == 'MT':
                    model = infer_data_job_mt(
                        model,
                        work_dir=pred_root,
                        model_name=model_name,
                        dataset=dataset,
                        verbose=args.verbose,
                        api_nproc=args.nproc,
                        ignore_failed=args.ignore, 
                        limit=args.limit,
                        )
                else:
                    model = infer_data_job(
                        model,
                        work_dir=pred_root,
                        model_name=model_name,
                        dataset=dataset,
                        verbose=args.verbose,
                        api_nproc=args.nproc,
                        ignore_failed=args.ignore, 
                        limit=args.limit,
                        )

                # Set the judge kwargs first before evaluation or dumping
                judge_kwargs = {
                    'nproc': args.nproc,
                    'verbose': args.verbose,
                }
                if args.retry is not None:
                    judge_kwargs['retry'] = args.retry
                if args.judge is not None:
                    judge_kwargs['model'] = args.judge
                else:
                    if dataset.TYPE in ['MCQ', 'Y/N'] or listinstr(['MathVerse'], dataset_name):
                        judge_kwargs['model'] = 'chatgpt-0125'
                    elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'],
                                   dataset_name):
                        judge_kwargs['model'] = 'gpt-4-turbo'
                    elif listinstr(['MMLongBench', 'MMDU', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI'],
                                   dataset_name):
                        judge_kwargs['model'] = 'gpt-4o'
                if 'OPENAI_API_KEY_JUDGE' in os.environ and len(os.environ['OPENAI_API_KEY_JUDGE']):
                    judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
                if 'OPENAI_API_BASE_JUDGE' in os.environ and len(os.environ['OPENAI_API_BASE_JUDGE']):
                    judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']

                if rank == 0:
                    if dataset_name in ['MMMU_TEST']:
                        result_json = MMMU_result_transfer(result_file)
                        logger.info(f'Transfer MMMU_TEST result to json for official evaluation, '
                                    f'json file saved in {result_json}')  # noqa: E501
                        continue
                    elif 'MMT-Bench_ALL' in dataset_name:
                        submission_file = MMTBench_result_transfer(result_file, **judge_kwargs)
                        logger.info(f'Extract options from prediction of MMT-Bench FULL split for official evaluation '
                                    f'(https://eval.ai/web/challenges/challenge-page/2328/overview), '
                                    f'submission file saved in {submission_file}')  # noqa: E501
                        continue
                    elif 'MLLMGuard_DS' in dataset_name:
                        logger.info('The evaluation of MLLMGuard_DS is not supported yet. ')  # noqa: E501
                        continue
                    elif 'AesBench_TEST' == dataset_name:
                        logger.info(f'The results are saved in {result_file}. '
                                    f'Please send it to the AesBench Team via huangyipo@hotmail.com.')  # noqa: E501
                        continue
                    elif dataset_name in ['DocVQA_TEST', 'InfoVQA_TEST', 'Q-Bench1_TEST', 'A-Bench_TEST']:
                        logger.info(f'{dataset_name} is a test split without ground-truth. '
                                    'Thus only the inference part is supported for those datasets. ')  # noqa: E501

                if dataset_name in [
                    'MMBench_TEST_CN', 'MMBench_TEST_EN', 'MMBench', 'MMBench_CN',
                    'MMBench_TEST_CN_V11', 'MMBench_TEST_EN_V11', 'MMBench_V11', 'MMBench_CN_V11'
                ]:
                    if not MMBenchOfficialServer(dataset_name):
                        logger.error(
                            f'Can not evaluate {dataset_name} on non-official servers, '
                            'will skip the evaluation. '
                        )
                        continue

                eval_proxy = os.environ.get('EVAL_PROXY', None)
                old_proxy = os.environ.get('HTTP_PROXY', '')

                if rank == 0 and args.mode == 'all':
                    if eval_proxy is not None:
                        proxy_set(eval_proxy)

                    eval_results = dataset.evaluate(result_file, **judge_kwargs)
                    if eval_results is not None:
                        assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame)
                        logger.info(f'The evaluation of model {model_name} x dataset {dataset_name} has finished! ')
                        logger.info('Evaluation Results:')
                    if isinstance(eval_results, dict):
                        logger.info('\n' + json.dumps(eval_results, indent=4))
                    elif isinstance(eval_results, pd.DataFrame):
                        if len(eval_results) < len(eval_results.columns):
                            eval_results = eval_results.T
                        logger.info('\n' + tabulate(eval_results))

                    if eval_proxy is not None:
                        proxy_set(old_proxy)
            except Exception as e:
                logger.exception(f'Model {model_name} x Dataset {dataset_name} combination failed: {e}, '
                                 'skipping this combination.')
                continue

    if world_size > 1:
        dist.barrier()


if __name__ == '__main__':
    load_env()
    main()
