# --------------------------------------------------------
# Eagle2
# Copyright (c) 2025 NVIDIA
# Licensed under The Apache License [see LICENSE for details]
# --------------------------------------------------------

import copy

from transformers import AutoConfig, LlamaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from .configuration_siglip import SiglipVisionConfig
from .configuration_qwen2 import Qwen2Config
from .configuration_multi_backbone_channel_concatentation_model import MultiBackboneChannelConcatenationVisionModelConfig
logger = logging.get_logger(__name__)


class Eagle2ChatConfig(PretrainedConfig):
    model_type = 'eagle_chat'
    is_composition = True

    def __init__(
            self,
            vision_config=None,
            llm_config=None,
            use_backbone_lora=0,
            use_llm_lora=0,
            select_layer=-1,
            force_image_size=None,
            downsample_ratio=0.5,
            template=None,
            dynamic_image_size=False,
            use_thumbnail=False,
            min_dynamic_patch=1,
            max_dynamic_patch=6,
            mlp_checkpoint=True,
            pre_feature_reduction=False,
            keep_aspect_ratio=False,
            **kwargs):
        super().__init__(**kwargs)

        if vision_config is None:
            vision_config = {}
            logger.info('vision_config is None. Initializing Vision Encoders with default values.')

        if llm_config is None:
            llm_config = {}
            logger.info('llm_config is None. Initializing the LLM config with default values')

        if vision_config['model_type'] == 'siglip_vision_model':
            self.vision_config = SiglipVisionConfig(**vision_config)
        elif vision_config['model_type'].startswith("MOB"):
            self.vision_config = MultiBackboneChannelConcatenationVisionModelConfig(**vision_config)
        else:
            raise ValueError('Unsupported model_type: {}'.format(vision_config['model_type']))

        if llm_config['architectures'][0] == 'LlamaForCausalLM':
            self.llm_config = LlamaConfig(**llm_config)
        elif llm_config['architectures'][0] == 'Qwen2ForCausalLM':
            self.llm_config = Qwen2Config(**llm_config)
        else:
            raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
        self.use_backbone_lora = use_backbone_lora
        self.use_llm_lora = use_llm_lora
        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.min_dynamic_patch = min_dynamic_patch
        self.max_dynamic_patch = max_dynamic_patch
        self.mlp_checkpoint = mlp_checkpoint
        self.pre_feature_reduction = pre_feature_reduction
        self.keep_aspect_ratio = keep_aspect_ratio
        logger.info(f'keep_aspect_ratio: {self.keep_aspect_ratio}')
        logger.info(f'vision_select_layer: {self.select_layer}')
        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['use_backbone_lora'] = self.use_backbone_lora
        output['use_llm_lora'] = self.use_llm_lora
        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['min_dynamic_patch'] = self.min_dynamic_patch
        output['max_dynamic_patch'] = self.max_dynamic_patch
        output['keep_aspect_ratio'] = self.keep_aspect_ratio

        return output