from transformers.configuration_utils import PretrainedConfig
import sys
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    LlamaConfig,
    LlamaForCausalLM,
    PreTrainedModel,
)
from .attrdict_config import AttrDict

class VisionConfig(PretrainedConfig):
    model_type = "vision"
    cls: str = ""
    params: AttrDict = {}

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cls = kwargs.get("cls", "")
        if not isinstance(self.cls, str):
            self.cls = self.cls.__name__

        self.params = AttrDict(kwargs.get("params", {}))


class AlignerConfig(PretrainedConfig):
    model_type = "aligner"
    cls: str = ""
    params: AttrDict = {}

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cls = kwargs.get("cls", "")
        if not isinstance(self.cls, str):
            self.cls = self.cls.__name__

        self.params = AttrDict(kwargs.get("params", {}))


class GenVisionConfig(PretrainedConfig):
    model_type = "gen_vision"
    cls: str = ""
    params: AttrDict = {}

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cls = kwargs.get("cls", "")
        if not isinstance(self.cls, str):
            self.cls = self.cls.__name__

        self.params = AttrDict(kwargs.get("params", {}))


class GenAlignerConfig(PretrainedConfig):
    model_type = "gen_aligner"
    cls: str = ""
    params: AttrDict = {}

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cls = kwargs.get("cls", "")
        if not isinstance(self.cls, str):
            self.cls = self.cls.__name__

        self.params = AttrDict(kwargs.get("params", {}))


class GenHeadConfig(PretrainedConfig):
    model_type = "gen_head"
    cls: str = ""
    params: AttrDict = {}

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.cls = kwargs.get("cls", "")
        if not isinstance(self.cls, str):
            self.cls = self.cls.__name__

        self.params = AttrDict(kwargs.get("params", {}))


class MultiModalityConfig(PretrainedConfig):
    model_type = "multi_modality"
    vision_config: VisionConfig
    aligner_config: AlignerConfig

    gen_vision_config: GenVisionConfig
    gen_aligner_config: GenAlignerConfig
    gen_head_config: GenHeadConfig

    language_config: LlamaConfig

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        vision_config = kwargs.get("vision_config", {})
        self.vision_config = VisionConfig(**vision_config)

        aligner_config = kwargs.get("aligner_config", {})
        self.aligner_config = AlignerConfig(**aligner_config)

        gen_vision_config = kwargs.get("gen_vision_config", {})
        self.gen_vision_config = GenVisionConfig(**gen_vision_config)

        gen_aligner_config = kwargs.get("gen_aligner_config", {})
        self.gen_aligner_config = GenAlignerConfig(**gen_aligner_config)

        gen_head_config = kwargs.get("gen_head_config", {})
        self.gen_head_config = GenHeadConfig(**gen_head_config)

        language_config = kwargs.get("language_config", {})
        if isinstance(language_config, LlamaConfig):
            self.language_config = language_config
        else:
            self.language_config = LlamaConfig(**language_config)