from opencompass.multimodal.models.qwen import QwenVLChatVQAPromptConstructor

# dataloader settings
val_pipeline = [
    dict(type='mmpretrain.LoadImageFromFile'),
    dict(type='mmpretrain.ToPIL', to_rgb=True),
    dict(type='mmpretrain.torchvision/Resize',
         size=(448, 448),
         interpolation=3),
    dict(type='mmpretrain.torchvision/ToTensor'),
    dict(type='mmpretrain.torchvision/Normalize',
         mean=(0.48145466, 0.4578275, 0.40821073),
         std=(0.26862954, 0.26130258, 0.27577711)),
    dict(
        type='mmpretrain.PackInputs',
        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
        meta_keys=['question_id', 'image_id'],
    )
]

dataset = dict(
    type='mmpretrain.COCOVQA',
    data_root='data/coco',
    data_prefix='images/val2014',
    question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
    ann_file='annotations/v2_mscoco_val2014_annotations.json',
    pipeline=val_pipeline)

qwen_vqav2_dataloader = dict(batch_size=1,
                  num_workers=4,
                  dataset=dataset,
                  collate_fn=dict(type='pseudo_collate'),
                  sampler=dict(type='DefaultSampler', shuffle=False))

# model settings
qwen_vqav2_model = dict(
    type='qwen-vl-chat',
    pretrained_path='Qwen/Qwen-VL-Chat',  # or Huggingface repo id
    prompt_constructor=dict(type=QwenVLChatVQAPromptConstructor)
)

# evaluation settings
qwen_vqav2_evaluator = [dict(type='mmpretrain.VQAAcc')]
