# --------------------------------------------------------
# LLM-jp-VL
# Copyright (c) 2026 LLM-jp
# Licensed under The Apache License 2.0 [see LICENSE for details]
#
# Originally based on InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

import copy
from typing import Dict, Any, Optional

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

from transformers import SiglipVisionConfig

logger = logging.get_logger(__name__)


class LLMjpVLConfig(PretrainedConfig):
    model_type = "llmjpvl"
    is_composition = True

    def __init__(
        self,
        vision_config: Optional[Dict[str, Any]] = None,
        llm_config: Optional[Dict[str, Any]] = None,
        select_layer=-1,
        force_image_size=None,
        downsample_ratio=0.5,
        template=None,
        dynamic_image_size=False,
        use_thumbnail=False,
        ps_version="v2",
        min_dynamic_patch=1,
        max_dynamic_patch=6,
        img_context_token_id=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if vision_config is None:
            vision_config = {}
        if llm_config is None:
            llm_config = {"architectures": ["Qwen2ForCausalLM"]}
        if isinstance(llm_config, dict):
            assert "architectures" in llm_config, (
                "Should specify architecture in llm_config"
            )

        if isinstance(vision_config, dict):
            self.vision_config = SiglipVisionConfig(**vision_config)
        else:
            self.vision_config = vision_config

        if isinstance(llm_config, dict):
            architecture: str = llm_config["architectures"][0]
            if architecture == "LlamaForCausalLM":
                from transformers import LlamaConfig

                self.llm_config = LlamaConfig(**llm_config)
            elif architecture == "Qwen2ForCausalLM":
                from transformers import Qwen2Config

                self.llm_config = Qwen2Config(**llm_config)
            elif architecture == "Qwen3MoeForCausalLM":
                from transformers import Qwen3MoeConfig

                self.llm_config = Qwen3MoeConfig(**llm_config)
            elif architecture == "Qwen3ForCausalLM":
                from transformers import Qwen3Config

                self.llm_config = Qwen3Config(**llm_config)
            elif architecture == "GptOssForCausalLM":
                from transformers import GptOssConfig

                self.llm_config = GptOssConfig(**llm_config)
            else:
                raise ValueError("Unsupported architecture: {}".format(architecture))
        else:
            self.llm_config = llm_config

        self.select_layer = select_layer
        self.force_image_size = force_image_size
        self.downsample_ratio = downsample_ratio
        self.template = template
        self.dynamic_image_size = dynamic_image_size
        self.use_thumbnail = use_thumbnail
        self.ps_version = ps_version  # pixel shuffle version
        self.min_dynamic_patch = min_dynamic_patch
        self.max_dynamic_patch = max_dynamic_patch
        self.img_context_token_id = img_context_token_id
        self.tie_word_embeddings = self.llm_config.tie_word_embeddings

        logger.info(f"vision_select_layer: {self.select_layer}")
        logger.info(f"ps_version: {self.ps_version}")
        logger.info(f"min_dynamic_patch: {self.min_dynamic_patch}")
        logger.info(f"max_dynamic_patch: {self.max_dynamic_patch}")

    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["model_type"] = self.__class__.model_type
        output["select_layer"] = self.select_layer
        output["force_image_size"] = self.force_image_size
        output["downsample_ratio"] = self.downsample_ratio
        output["template"] = self.template
        output["dynamic_image_size"] = self.dynamic_image_size
        output["use_thumbnail"] = self.use_thumbnail
        output["ps_version"] = self.ps_version
        output["min_dynamic_patch"] = self.min_dynamic_patch
        output["max_dynamic_patch"] = self.max_dynamic_patch
        output["img_context_token_id"] = self.img_context_token_id

        return output