from typing import Union, Optional

from transformers import PretrainedConfig, AutoConfig
from .visual_tokenizer import ClipVisualTokenizerConfig

class OvisConfig(PretrainedConfig):
    model_type = "ovis"

    def __init__(self,
                 llm_config: Optional[Union[PretrainedConfig, dict]] = None,
                 visual_tokenizer_config: Optional[Union[PretrainedConfig, dict]] = None,
                 multimodal_max_length=2048,
                 hidden_size=None,
                 conversation_formatter_class=None,
                 **kwargs):
        super().__init__(**kwargs)
        if llm_config is not None:
            assert isinstance(llm_config, (PretrainedConfig, dict)), \
                f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
            if not isinstance(llm_config, PretrainedConfig):
                model_type = llm_config['model_type']
                llm_config.pop('model_type')
                llm_config = AutoConfig.for_model(model_type, **llm_config)
        self.llm_config = llm_config
        if visual_tokenizer_config is not None:
            assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
                f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
            if not isinstance(visual_tokenizer_config, PretrainedConfig):
                model_type = visual_tokenizer_config['model_type']
                visual_tokenizer_config.pop('model_type')
                visual_tokenizer_config = AutoConfig.for_model(model_type, **visual_tokenizer_config)
        self.visual_tokenizer_config = visual_tokenizer_config
        self.multimodal_max_length = multimodal_max_length
        self.hidden_size = hidden_size
        self.conversation_formatter_class = conversation_formatter_class