import copy

from transformers import AutoConfig, Qwen3Config
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

from .configuration_dinov3_vit import DINOv3ViTConfig

logger = logging.get_logger(__name__)

class ProjectorConfig(PretrainedConfig):
    model_type = "projector"
    _auto_class = "AutoConfig"

    def __init__(
        self,
        visual_hidden_size=4096,
        llm_hidden_size=4096,
        depth=2,
        hidden_act="gelu",
        bias=True,
        **kwargs,
    ):
        self.visual_hidden_size = visual_hidden_size
        self.llm_hidden_size = llm_hidden_size
        self.depth = depth
        self.hidden_act = hidden_act
        self.bias = bias
        super().__init__(**kwargs)

class VectorLLMConfig(PretrainedConfig):
    model_type = 'vectorllm'
    processor_class = "VectorLLMProcessor",
    is_composition = True

    def __init__(
            self,
            vision_config=None,
            llm_config=None,
            regression_size=(128, 128),
            projector_depth=2,
            pixel_idx=0,
            **kwargs):
        super().__init__(**kwargs)
        if vision_config is None:
            vision_config = {}
            logger.info('vision_config is None. Initializing the DinoV3Config with default values.')

        if llm_config is None:
            llm_config = {}
            logger.info('llm_config is None. Initializing the Qwen3 config with default values.')

        self.vision_config = DINOv3ViTConfig(**vision_config)
        self.llm_config = Qwen3Config(**llm_config)
        self.text_config = self.llm_config

        self.hidden_size = self.llm_config.hidden_size
        self.vision_hidden_size = self.vision_config.hidden_size

        self.projector_config = ProjectorConfig(
            visual_hidden_size=self.vision_hidden_size,
            llm_hidden_size=self.hidden_size,
            depth=projector_depth
        )

        self.regression_size = regression_size
        self.pixel_idx = pixel_idx
        self.tie_word_embeddings = False
        self.num_cls_register_tokens = 1 + self.vision_config.num_register_tokens

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output['vision_config'] = self.vision_config.to_dict()
        output['llm_config'] = self.llm_config.to_dict()
        output['text_config'] = output['llm_config']
        output['projector_config'] = self.projector_config.to_dict()
        output['model_type'] = self.__class__.model_type
        return output