from opencompass.multimodal.models.instructblip import (
    InstructBlipVQAPromptConstructor,
    InstructBlipVQAPostProcessor,
)

# 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=['question', 'gt_answer', 'gt_answer_weight'],
        meta_keys=['question_id', 'image_id'],
    )
]

dataset = dict(
    type='mmpretrain.COCOVQA',
    data_root='data/okvqa',
    question_file='annotations/OpenEnded_mscoco_val2014_questions.json',
    ann_file='annotations/mscoco_val2014_annotations.json',
    pipeline=val_pipeline,
    data_prefix='images/val2014',
)

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

# model settings
instruct_blip_ok_vqa_model = dict(
    type='blip2-vicuna-instruct',
    prompt_constructor=dict(type=InstructBlipVQAPromptConstructor),
    post_processor=dict(type=InstructBlipVQAPostProcessor),
    freeze_vit=True,
    low_resource=False,
    llm_model='/path/to/vicuna-7b/',
    max_output_txt_len=10,
)

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

instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth'