from opencompass.multimodal.models.llava import LLaVABasePromptConstructor, LLaVABasePostProcessor

# dataloader settings
val_pipeline = [
    dict(type='mmpretrain.LoadImageFromFile'),
    dict(type='mmpretrain.ToPIL', to_rgb=True),
    dict(type='mmpretrain.torchvision/Resize',
         size=(224, 224),
         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=['image_id']),
]


dataset = dict(type='mmpretrain.Flickr30kCaption',
               data_root='data/flickr30k',
               ann_file='annotations/dataset_flickr30k.json',
               data_prefix='images',
               split='val',
               pipeline=val_pipeline)

llava_flickr30k_dataloader = dict(
    batch_size=1,
    num_workers=4,
    dataset=dataset,
    collate_fn=dict(type='pseudo_collate'),
    sampler=dict(type='DefaultSampler', shuffle=False),
)

# model settings
llava_flickr30k_model = dict(
    type='llava',
    model_path='/path/to/llava',
    is_caption_task=True,
    prompt_constructor=dict(type=LLaVABasePromptConstructor),
    post_processor=dict(type=LLaVABasePostProcessor)
)  # noqa

# evaluation settings
llava_flickr30k_evaluator = [
    dict(
        type='mmpretrain.COCOCaption',
        ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
    )  # noqa
]