import copy
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers import AutoConfig
from transformers.models.auto import CONFIG_MAPPING

logger = logging.get_logger(__name__)

class H2OVLChatConfig(PretrainedConfig):
    model_type = 'h2ovl_chat'
    is_composition = True

    def __init__(
            self,
            vision_config=None,
            llm_config=None,
            use_backbone_lora=0,
            use_llm_lora=0,
            pad2square=False,
            select_layer=-4,
            force_image_size=None,
            downsample_ratio=0.5,
            template=None,
            dynamic_image_size=False,
            use_thumbnail=False,
            ps_version='v1',
            min_dynamic_patch=1,
            max_dynamic_patch=6,
            use_msac=False,
            **kwargs):
        super().__init__(**kwargs)
        
        if vision_config["model_type"] in CONFIG_MAPPING:
            self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        else:
            self.vision_config = AutoConfig.from_pretrained(vision_config["_name_or_path"], trust_remote_code=True)
        self.vision_config.update(vision_config)

        if llm_config["model_type"] in CONFIG_MAPPING:
            self.llm_config = CONFIG_MAPPING[llm_config["model_type"]](**llm_config)
        else:
            self.llm_config = AutoConfig.from_pretrained(llm_config["_name_or_path"], trust_remote_code=True)
        self.llm_config.update(llm_config)

        self.use_backbone_lora = use_backbone_lora
        self.use_llm_lora = use_llm_lora
        self.pad2square = pad2square
        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.use_msac = use_msac

        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['use_backbone_lora'] = self.use_backbone_lora
        output['use_llm_lora'] = self.use_llm_lora
        output['pad2square'] = self.pad2square
        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['use_msac'] = self.use_msac

        return output