from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.models.auto import AutoConfig


class Qwen3VLVisionConfig(PretrainedConfig):
    model_type = "qwen3_vl"
    base_config_key = "vision_config"

    def __init__(
        self,
        depth=27,
        hidden_size=1152,
        hidden_act="gelu_pytorch_tanh",
        intermediate_size=4304,
        num_heads=16,
        in_channels=3,
        patch_size=16,
        spatial_merge_size=2,
        temporal_patch_size=2,
        out_hidden_size=3584,
        num_position_embeddings=2304,
        deepstack_visual_indexes=None,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.depth = depth
        self.hidden_size = hidden_size
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.num_heads = num_heads
        self.in_channels = in_channels
        self.patch_size = patch_size
        self.spatial_merge_size = spatial_merge_size
        self.temporal_patch_size = temporal_patch_size
        self.out_hidden_size = out_hidden_size
        self.num_position_embeddings = num_position_embeddings
        self.deepstack_visual_indexes = deepstack_visual_indexes if deepstack_visual_indexes is not None else [8, 16, 24]
        self.initializer_range = initializer_range


class Qwen3VLTextConfig(PretrainedConfig):
    model_type = "qwen3_vl_text"
    base_config_key = "text_config"

    def __init__(
        self,
        vocab_size=151936,
        hidden_size=4096,
        intermediate_size=22016,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=32,
        head_dim=128,
        hidden_act="silu",
        max_position_embeddings=128000,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=5000000.0,
        rope_scaling=None,
        rope_parameters=None,
        attention_bias=False,
        attention_dropout=0.0,
        **kwargs,
    ):
        if rope_scaling is None and rope_parameters is not None:
            rope_scaling = dict(rope_parameters)
            rope_theta = rope_scaling.pop("rope_theta", rope_theta)
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads or num_attention_heads
        self.head_dim = head_dim
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"})
        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)


class Qwen3VLConfig(PretrainedConfig):
    model_type = "qwen3_vl"
    sub_configs = {"vision_config": Qwen3VLVisionConfig, "text_config": Qwen3VLTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        image_token_id=151655,
        video_token_id=151656,
        vision_start_token_id=151652,
        vision_end_token_id=151653,
        tie_word_embeddings=False,
        **kwargs,
    ):
        self.vision_config = self._make_sub_config(vision_config, Qwen3VLVisionConfig)
        self.text_config = self._make_sub_config(text_config, Qwen3VLTextConfig)
        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.vision_start_token_id = vision_start_token_id
        self.vision_end_token_id = vision_end_token_id
        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)

    @staticmethod
    def _make_sub_config(config, config_class):
        if isinstance(config, config_class):
            return config
        if isinstance(config, dict):
            return config_class(**config)
        return config_class()


def register_qwen3_vl_config():
    for model_type, config_class in (("qwen3_vl", Qwen3VLConfig), ("qwen3_vl_text", Qwen3VLTextConfig)):
        try:
            AutoConfig.for_model(model_type)
        except ValueError:
            AutoConfig.register(model_type, config_class)


register_qwen3_vl_config()


__all__ = ["Qwen3VLConfig", "Qwen3VLTextConfig", "Qwen3VLVisionConfig", "register_qwen3_vl_config"]