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import logging

from transformers.configuration_utils import PretrainedConfig

from .moe_lm import AriaMoELMConfig
from .vision_encoder import AriaVisionConfig

logger = logging.getLogger(__name__)


# adapted from transformers.models.llava.configuration_llava.LlavaConfig
class AriaConfig(PretrainedConfig):
    """
    Configuration class for Aria model.

    This class handles the configuration for both vision and text components of the Aria model,
    as well as additional parameters for image token handling and projector mapping.

    Args:
        vision_config (AriaVisionConfig or dict): Configuration for the vision component.
        text_config (AriaMoELMConfig or dict): Configuration for the text component.
        projector_patch_to_query_dict (dict): Mapping of patch sizes to query dimensions.
        ignore_index (int): Index to ignore in loss calculation.
        image_token_index (int): Index used to represent image tokens.
        **kwargs: Additional keyword arguments passed to the parent class.

    Attributes:
        model_type (str): Type of the model, set to "aria".
        is_composition (bool): Whether the model is a composition of multiple components.
        ignore_index (int): Index to ignore in loss calculation.
        image_token_index (int): Index used to represent image tokens.
        projector_patch_to_query_dict (dict): Mapping of patch sizes to query dimensions.
        vision_config (AriaVisionConfig): Configuration for the vision component.
        text_config (AriaMoELMConfig): Configuration for the text component.
    """

    model_type = "aria"
    is_composition = False

    def __init__(
        self,
        vision_config=AriaVisionConfig(),
        text_config=AriaMoELMConfig(),
        projector_patch_to_query_dict={
            1225: 128,
            4900: 256,
        },
        ignore_index=-100,
        image_token_index=32000,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.ignore_index = ignore_index
        self.image_token_index = image_token_index

        attn_implementation = kwargs.pop("attn_implementation", None)
        self._attn_implementation = attn_implementation

        # Convert the keys and values of projector_patch_to_query_dict to integers
        # This ensures consistency even if they were provided as strings
        self.projector_patch_to_query_dict = {
            int(k): int(v) for k, v in projector_patch_to_query_dict.items()
        }

        if isinstance(vision_config, dict) and "model_type" in vision_config:
            vision_config = AriaVisionConfig(**vision_config)
            if attn_implementation is None:
                vision_attn_implementation = "flash_attention_2"
            elif attn_implementation == "sdpa":
                logger.warning(
                    "SDPA is not supported for vit, using flash_attention_2 instead"
                )
                vision_attn_implementation = "flash_attention_2"
            else:
                vision_attn_implementation = attn_implementation
            vision_config._attn_implementation = vision_attn_implementation

        self.vision_config = vision_config

        if isinstance(text_config, dict) and "model_type" in text_config:
            text_attn_implementation = (
                "sdpa" if attn_implementation is None else attn_implementation
            )
            text_config = AriaMoELMConfig(**text_config)
            text_config._attn_implementation = text_attn_implementation

        self.text_config = text_config

        # This is needed for the static kv cache
        self.num_hidden_layers = self.text_config.num_hidden_layers