from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class HCXVisionConfig(PretrainedConfig):
    model_type = "hyperclovax_vlm"
    keys_to_ignore_at_inference = ["past_key_values"]

    # The `gpt2` class has a different name, so it needs to be updated accordingly.
    language_config_attribute_map = {
        "n_embd": "hidden_size",
        "n_positions": "max_position_embeddings",
        "n_head": "num_attention_heads",
        "n_layer": "num_hidden_layers",
    }

    def __init__(
        self,
        language_config=None,
        vision_config=None,
        use_nth_layer=-2,
        img_start_id=100009,  # <|dummy3|>
        decoder_max_length=4096,
        anyres=False,
        unpad=False,
        max_num_grids=-1,
        num_queries_vis_abstractor=-1,
        ignore_index=-100,
        proj_pos_emb=True,
        proj_prenorm=False,
        use_1x1_grid=False,
        **kwargs,
    ):
        for key, val in self.language_config_attribute_map.items():
            if language_config is not None and key in language_config:
                language_config[val] = language_config.pop(key)

        self.language_config = language_config
        self.vision_config = vision_config

        if language_config is not None:
            # In DeepSpeed ZeRO-3, the memory size is automatically determined based on the `hidden_size` specified in the config.
            self.hidden_size = (
                language_config["hidden_size"] if "hidden_size" in language_config else language_config["n_embd"]
            )
        # add VLM configs
        self.use_nth_layer = use_nth_layer
        self.decoder_max_length = decoder_max_length
        self.anyres = anyres
        self.unpad = unpad
        self.max_num_grids = max_num_grids
        self.num_queries_vis_abstractor = num_queries_vis_abstractor
        self.img_start_id = img_start_id
        self.ignore_index = ignore_index
        self.proj_pos_emb = proj_pos_emb
        self.proj_prenorm = proj_prenorm
        self.use_1x1_grid = use_1x1_grid
        super().__init__(**kwargs)