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"""Mllama model configuration"""

import os
from typing import Dict, List, Optional, Union

import transformers
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
from transformers import Wav2Vec2BertConfig, AutoConfig, LlamaConfig
from transformers.models.mllama.configuration_mllama import MllamaVisionConfig, MllamaTextConfig

logger = logging.get_logger(__name__)


class Llama3Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
    Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the Mllama-9B.

    e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)

    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[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
            The config object or dictionary of the vision backbone.
        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
            The config object or dictionary of the text backbone.
        image_token_index (`int`, *optional*, defaults to 128256):
            The image token index to encode the image prompt.

    Example:

    ```python
    >>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig

    >>> # Initializing a CLIP-vision config
    >>> vision_config = MllamaVisionConfig()

    >>> # Initializing a Llama config
    >>> text_config = MllamaTextConfig()

    >>> # Initializing a mllama-11b style configuration
    >>> configuration = MllamaConfig(vision_config, text_config)

    >>> # Initializing a model from the mllama-11b style configuration
    >>> model = MllamaForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "llama3"
    is_composition = True

    def __init__(
        self,
        text_config=None,
        audio_config=None,
        audio_token_index=128257,
        **kwargs,
    ):
        if audio_config is None:
            self.audio_config = Wav2Vec2BertConfig()
            logger.info("audio_config is None, using default mllama audio config")
        elif isinstance(audio_config, dict):
            self.audio_config = Wav2Vec2BertConfig(**audio_config)
        elif isinstance(audio_config, Wav2Vec2BertConfig):
            self.audio_config = audio_config
        
        self.audio_token_index = audio_token_index

        if text_config is None:
            self.text_config = LlamaConfig()
            logger.info("text_config is None, using default mllama text config")
        elif isinstance(text_config, dict):
            self.text_config = LlamaConfig(**text_config)
        elif isinstance(text_config, LlamaConfig):
            self.text_config = text_config

        super().__init__(**kwargs)

AutoConfig.register("llama3", Llama3Config)
transformers.Llama3Config = Llama3Config