from opencompass.multimodal.models.openflamingo import OpenFlamingoVQAPromptConstructor

# dataloader settings
val_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='mmpretrain.ResizeEdge',
         scale=224,
         interpolation='bicubic',
         backend='pillow'),
    dict(type='CenterCrop', crop_size=(224, 224)),
    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',
)

openflamingo_okvqa_dataloader = dict(
    batch_size=8,
    num_workers=4,
    dataset=dataset,
    sampler=dict(type='DefaultSampler', shuffle=False),
    collate_fn=dict(type='default_collate'),
    persistent_workers=True,
)

# model settings
openflamingo_okvqa_model = dict(
    type='openflamingo',
    data_preprocessor=dict(
        type='mmpretrain.MultiModalDataPreprocessor',
        mean=[122.770938, 116.7460125, 104.09373615],
        std=[68.5005327, 66.6321579, 70.32316305],
        to_rgb=True,
    ),
    tokenizer=dict(type='mmpretrain.LlamaTokenizer',
                   name_or_path='decapoda-research/llama-7b-hf'),
    vision_encoder=dict(
        type='mmpretrain.VisionTransformer',
        arch='l',
        patch_size=14,
        pre_norm=True,
        norm_cfg=dict(type='LN', eps=1e-5),
        layer_cfgs=dict(act_cfg=dict(type='mmpretrain.QuickGELU')),
        final_norm=False,
        out_type='raw',
        pretrained=  # noqa: E251
        '/path/to/vision/encoder',  # noqa
    ),
    lang_encoder=dict(
        base=dict(type='mmpretrain.AutoModelForCausalLM',
                  name_or_path=
                  'decapoda-research/llama-7b-hf',
                  local_files_only=True),
        adapter=dict(type='mmpretrain.FlamingoLMAdapter',
                     vis_hidden_size=1024,
                     cross_attn_every_n_layers=4,
                     use_media_placement_augmentation=False),
    ),
    task='vqa',
    generation_cfg=dict(num_beams=3, max_new_tokens=20, length_penalty=-2.0),
    prompt_constructor=dict(type=OpenFlamingoVQAPromptConstructor)
)

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

openflamingo_load_from = '/path/to/pretrained/weights'  # noqa