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"""PerceptionLM model configuration"""

from ...configuration_utils import PreTrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
from ..timm_wrapper.configuration_timm_wrapper import TimmWrapperConfig


logger = logging.get_logger(__name__)


class PerceptionLMConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`PerceptionLMForConditionalGeneration`]. It is used to instantiate an
    PerceptionLM model according to the specified arguments, defining the model architecture.

    Example models:
    -  [facebook/Perception-LM-1B](https://huggingface.co/facebook/Perception-LM-1B).
    -  [facebook/Perception-LM-3B](https://huggingface.co/facebook/Perception-LM-3B).
    -  [facebook/Perception-LM-8B](https://huggingface.co/facebook/Perception-LM-8B).

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        vision_config (`Union[TimmWrapperConfig, dict]`, *optional*, defaults to `TimmWrapperConfig()`):
            The config object or dictionary of the vision backbone.
        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `LlamaConfig()`):
            The config object or dictionary of the text backbone.
        vision_use_cls_token (`bool`, *optional*, defaults to `True`):
            Whether CLS token is used in the vision backbone. If used, we remove CLS token embedding from vision output.
        projector_pooling_ratio (`int`, *optional*, defaults to 1):
            The pooling ratio used in the multimodal projector.
        image_token_id (`int`, *optional*, defaults to 128002):
            The image token index to encode the image prompt.
        video_token_id (`int`, *optional*, defaults to 128003):
            The video token index to encode the video prompt.
    """

    model_type = "perception_lm"
    sub_configs = {"text_config": AutoConfig, "vision_config": TimmWrapperConfig}

    def __init__(
        self,
        vision_config=None,
        text_config=None,
        vision_use_cls_token=True,
        projector_pooling_ratio=1,
        image_token_id=128002,
        video_token_id=128003,
        **kwargs,
    ):
        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        if isinstance(vision_config, dict):
            vision_config = TimmWrapperConfig(**vision_config)
        elif isinstance(vision_config, TimmWrapperConfig):
            pass
        elif vision_config is None:
            vision_config = TimmWrapperConfig()
        self.vision_config = vision_config
        self.vision_use_cls_token = vision_use_cls_token

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config.get("model_type", "llama")
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["llama"]()

        self.text_config = text_config
        self.projector_pooling_ratio = projector_pooling_ratio
        super().__init__(**kwargs)


__all__ = ["PerceptionLMConfig"]
