gemma.py

from __future__ import annotations

import json
import re

from typing import Callable, Iterable, TYPE_CHECKING, Sequence

import torch

if TYPE_CHECKING:
    from torch import Tensor

from .base import MmprojModel, ModelBase, TextModel, gguf, logger
from .qwen import DFlashModel


@ModelBase.register("GemmaForCausalLM")
# [TAG_HF_EXAMPLE_GATED] google/gemma-2b is gated
@ModelBase.example("trl-internal-testing/tiny-GemmaForCausalLM")
class GemmaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        # TODO: these special tokens should be exported only for the CodeGemma family
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
        special_vocab._set_special_token("prefix", 67)
        special_vocab._set_special_token("suffix", 69)
        special_vocab._set_special_token("middle", 68)
        special_vocab._set_special_token("fsep",   70)
        special_vocab._set_special_token("eot",    107)
        special_vocab.chat_template = None  # do not add it twice
        special_vocab.add_to_gguf(self.gguf_writer)

        self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
        self.gguf_writer.add_key_length(hparams["head_dim"])
        self.gguf_writer.add_value_length(hparams["head_dim"])
        self.gguf_writer.add_file_type(self.ftype)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
        # To prevent errors, skip loading lm_head.weight.
        if name == "lm_head.weight":
            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma2ForCausalLM")
# [TAG_HF_EXAMPLE_GATED] google/gemma-2-9b-it is gated
@ModelBase.example("trl-internal-testing/tiny-Gemma2ForCausalLM")
class Gemma2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA2

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
        self.gguf_writer.add_key_length(hparams["head_dim"])
        self.gguf_writer.add_value_length(hparams["head_dim"])
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_attn_logit_softcapping(
            self.hparams["attn_logit_softcapping"]
        )
        self.gguf_writer.add_final_logit_softcapping(
            self.hparams["final_logit_softcapping"]
        )
        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
        # To prevent errors, skip loading lm_head.weight.
        if name == "lm_head.weight":
            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
# [TAG_HF_EXAMPLE_GATED] google/gemma-3-4b-it is gated
@ModelBase.example("trl-internal-testing/tiny-Gemma3ForConditionalGeneration", "hf-tiny-v2/tiny-random-Gemma3ForCausalLM")
class Gemma3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA3

    def norm_shift(self, name: str) -> float:
        return 1.0 if name.endswith("norm.weight") else 0.0  # Gemma3RMSNorm adds 1.0 to the norm value

    def set_vocab(self):
        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
            self.gguf_writer.add_add_space_prefix(False)
        else:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams

        # some default values are not specified in the hparams
        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
        self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
        self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
        self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers
        # attn_logit_softcapping is removed in Gemma3
        assert hparams.get("attn_logit_softcapping") is None
        if (final_logit_softcap := hparams.get("final_logit_softcapping")):
            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
        if hparams.get("sliding_window_pattern") != 1:
            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
        self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # remove OOV (out-of-vocabulary) rows in token_embd
        if "embed_tokens.weight" in name:
            n_vocab_real = -1
            if (self.dir_model / "tokenizer.model").is_file():
                tokens = self._create_vocab_sentencepiece()[0]
                n_vocab_real = len(tokens)
            else:
                with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
                    tokenizer_json = json.load(f)
                    n_vocab_real = len(tokenizer_json["model"]["vocab"]) + len(tokenizer_json["added_tokens"])
            data_torch = data_torch[:n_vocab_real]

        # ref code in Gemma3RMSNorm
        # output = output * (1.0 + self.weight.float())
        # note: this is not the case on gemma3n
        f_shift = self.norm_shift(name)
        if f_shift != 0.0:
            data_torch = data_torch + f_shift

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma3TextModel")
# [TAG_HF_EXAMPLE_GATED] google/embeddinggemma-300m is gated
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3TextModel")
class EmbeddingGemma(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
    module_paths = []
    dense_features_dims = {}

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.sentence_transformers_dense_modules:
            # read modules.json to determine if model has Dense layers
            modules_file = self.dir_model / "modules.json"
            if modules_file.is_file():
                with open(modules_file, encoding="utf-8") as modules_json_file:
                    mods = json.load(modules_json_file)
                for mod in mods:
                    if mod["type"].endswith("Dense"):
                        mod_path = mod["path"]
                        # check if model.safetensors file for Dense layer exists
                        model_tensors_file = self.dir_model / mod_path / "model.safetensors"
                        if model_tensors_file.is_file():
                            self.module_paths.append(mod_path)
                            # read config.json of the Dense layer to get in/out features
                            mod_conf_file = self.dir_model / mod_path / "config.json"
                            if mod_conf_file.is_file():
                                with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file:
                                    mod_conf = json.load(mod_conf_json_file)
                                    # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights
                                    prefix = self._get_dense_prefix(mod_path)
                                    if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None:
                                        self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"])

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        from safetensors.torch import load_file
        module_paths = list(self.module_paths)
        for i, module_path in enumerate(module_paths):
            tensors_file = self.dir_model / module_path / "model.safetensors"
            local_tensors = load_file(tensors_file)
            tensor_name = self._get_dense_prefix(module_path)
            for name, local_tensor in local_tensors.items():
                if not name.endswith(".weight"):
                    continue
                orig_name = name.replace("linear", tensor_name)
                name = self.map_tensor_name(orig_name)
                yield name, local_tensor.clone()

    @staticmethod
    def _get_dense_prefix(module_path) -> str:
        """Get the tensor name prefix for the Dense layer from module path."""
        tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3"
        return tensor_name

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # Override the sliding window size as it gets adjusted by the Gemma3TextConfig
        # constructor. We want to use the value from the original model's config.json.
        # ref: https://github.com/huggingface/transformers/pull/40700
        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
            config = json.load(f)
            orig_sliding_window = config.get("sliding_window")
            if orig_sliding_window is None:
                raise ValueError("sliding_window not found in model config - this is required for the model")

            logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
                        f"instead of {self.hparams['sliding_window']}")
            self.gguf_writer.add_sliding_window(orig_sliding_window)
        if self.sentence_transformers_dense_modules:
            for dense, dims in self.dense_features_dims.items():
                logger.info(f"Setting dense layer {dense} in/out features to {dims}")
                self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1])

        self._try_set_pooling_type()


@ModelBase.register("Gemma3ForConditionalGeneration")
# [TAG_HF_EXAMPLE_GATED] google/gemma-3-4b-it is gated
@ModelBase.example("trl-internal-testing/tiny-Gemma3ForConditionalGeneration")
class Gemma3VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3)
        # default values below are taken from HF transformers code
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
        self.gguf_writer.add_vision_use_gelu(True)
        # calculate proj_scale_factor (used by tinygemma3 test model)
        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
        n_per_side = int(image_seq_length ** 0.5)
        image_size = self.hparams["image_size"]
        patch_size = self.hparams["patch_size"]
        proj_scale_factor = (image_size // patch_size) // n_per_side
        if proj_scale_factor > 0 and proj_scale_factor != 4:
            # we only need to write this if it's not the default value
            # in this case, we are converting a test model
            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # related to https://github.com/ggml-org/llama.cpp/issues/13025
        if "input_projection" in name:
            return gguf.GGMLQuantizationType.F16
        if ".embeddings." in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "vision_model.head." in name:
            # skip redundant tensors for tinygemma3
            return None

        if not name.startswith(("multi_modal_projector.", "vision_tower.", "multimodal_projector.", "vision_model.")):
            return None

        name = name.replace("_weight", ".weight")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # correct norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
        # the other norm values are part of SigLIP model, and they are already correct
        # ref code: Gemma3RMSNorm
        if "soft_emb_norm.weight" in name:
            logger.info(f"Correcting norm value for '{name}'")
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


class ConformerAudioModel(MmprojModel):
    _batch_norm_tensors: list[dict[str, Tensor]] | None = None

    @staticmethod
    def is_audio_tensor(name: str):
        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ConformerAudioModel.is_audio_tensor(name):
            if ".conv" in name or "_conv" in name and ".weight" in name:
                return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # fold running_mean, running_var and eps into weight and bias for batch_norm
        if "batch_norm" in name:
            if self._batch_norm_tensors is None:
                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
            assert bid is not None
            self._batch_norm_tensors[bid][name] = data_torch

            if len(self._batch_norm_tensors[bid]) < 5:
                return

            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
            eps = 1e-5 # default value

            a = weight / torch.sqrt(running_var + eps)
            b = bias - running_mean * a
            yield from super().modify_tensors(a, f"conformer.layers.{bid}.conv.batch_norm.weight", bid)
            yield from super().modify_tensors(b, f"conformer.layers.{bid}.conv.batch_norm.bias", bid)
            return

        # reshape conv weights
        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
            data_torch = data_torch[:, None, None]
        if "conv.depthwise_conv" in name and name.endswith(".weight"):
            assert data_torch.shape[1] == 1
            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
        if "conv.pointwise_conv" in name and name.endswith(".weight"):
            assert data_torch.shape[2] == 1
            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])

        mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
        yield (mapped_name, data_torch)


@ModelBase.register("Gemma3nForConditionalGeneration")
# [TAG_HF_EXAMPLE_GATED] google/gemma-3n-E2B-it is gated
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3nForConditionalGeneration")
class Gemma3nVisionAudioModel(ConformerAudioModel):
    has_audio_encoder = True
    has_vision_encoder = True

    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
    block_tensor_mapping = {
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
    }

    def __init__(self, *args, **kwargs):
        # Parent init will call find_hparam which now returns 0 for empty keys
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)

        # MobileNetV5 does not use image_mean/std
        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
        self.hparams_vision["image_size"] = self.preprocessor_config.get(
            "size", {"height": 768, "width": 768}
        )["height"]

        # Image sequence length (256 tokens = 16x16 for Gemma3n)
        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
        image_size = self.hparams_vision["image_size"]
        self.hparams_vision["patch_size"] = image_size // image_seq_length

        # remap audio hparams
        assert self.hparams_audio is not None
        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # vision params
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))

        # audio params
        assert self.hparams_audio is not None
        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # Force quantization settings for specific tensor types
        if "input_projection" in name or "input_proj" in name:
            return gguf.GGMLQuantizationType.F16
        if ".embeddings." in name or "stem" in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def custom_map(self, name: str) -> str:
        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
        parts = name.split(".")
        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
        if len(parts) >= 7:
            bid, sid = parts[4], parts[5]
            suffix = ".".join(parts[6:])
            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
            if template in self.block_tensor_mapping:
                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)

        raise ValueError(f"Unknown name: {name}")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if (ConformerAudioModel.is_audio_tensor(name)):
            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
            yield from super().modify_tensors(data_torch, name, bid)

        # Gemma3n uses
        # - model.embed_vision.* for projection layers
        # - model.vision_tower.* for vision encoder
        # Skip non-vision tensors
        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
            return

        if name.startswith("model.vision_tower.timm_model.blocks."):
            # Double-indexed block tensors through custom logic
            yield (self.custom_map(name), data_torch)
            return
        else:
            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
            new_name = self.map_tensor_name(name)

        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]

        yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)


@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
# [TAG_HF_EXAMPLE_GATED] google/gemma-3n-E2B-it is gated
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3nForConditionalGeneration")
class Gemma3NModel(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA3N

    _altup_proj: list[Tensor] = []
    _altup_unembd: list[Tensor] = []

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams["altup_num_inputs"] == 4, "Current conversion only supports 4 altup inputs"
        self._altup_proj = [
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
        ]
        self._altup_unembd = [
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
        ]

    def norm_shift(self, name: str) -> float:
        del name
        return 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code

    def set_vocab(self):
        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
        # which includes special tokens from 262144-262399 for vision/audio.
        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
        # Temporarily override the hparams lookup order to prioritize vocab_size.

        # Store original vocab_size_per_layer_input if it exists
        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")

        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
        if vocab_size_per_layer_input is not None:
            del self.hparams["vocab_size_per_layer_input"]

        # Call parent set_vocab which will now use vocab_size (262400)
        super().set_vocab()

        # Restore vocab_size_per_layer_input for later use
        if vocab_size_per_layer_input is not None:
            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
        self.gguf_writer.add_altup_num_inputs(self.hparams["altup_num_inputs"])
        self.gguf_writer.add_embedding_length_per_layer_input(self.hparams["hidden_size_per_layer_input"])
        self.gguf_writer.add_shared_kv_layers(self.hparams["num_kv_shared_layers"])

        activation_sparsity_scale = []
        for s in self.hparams["activation_sparsity_pattern"]:
            normal_dist = torch.distributions.normal.Normal(0, 1)
            std_multiplier = normal_dist.icdf(torch.tensor(s, dtype=torch.float32))
            activation_sparsity_scale.append(std_multiplier.item())
        self.gguf_writer.add_activation_sparsity_scale(activation_sparsity_scale)

        sliding_window_pattern = []
        for t in self.hparams["layer_types"]:
            sliding_window_pattern.append(t == "sliding_attention")
        self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)

    def _stack_matrices(self, matrices: list[Tensor]) -> Tensor | None:
        has_all = all(m.numel() > 0 for m in matrices)
        if not has_all:
            return None
        else:
            return torch.stack(matrices, dim=0)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith("_scale"):
            name = name + ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # TODO: implement self.prediction_coefs.weight.clamp_(...)

        # Pad token embeddings for vision/audio special tokens (262144-262399)
        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
            # Move to CPU to avoid meta device issues during padding
            data_torch = data_torch.to(device="cpu")

            vocab_size = self.hparams.get("vocab_size", 262400)
            current_size = data_torch.shape[0]  # First dimension is vocab_size

            if current_size < vocab_size:
                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
                padding_size = vocab_size - current_size
                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")

                # Create padding with zeros (vision tokens won't use these embeddings)
                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
                data_torch = torch.cat([data_torch, padding], dim=0)

            # Continue with normal processing
            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
            return

        if "altup_unembed_projections" in name:
            data_torch = data_torch.to(device="cpu")
            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
            # They should NOT be padded
            if ".0." in name:
                self._altup_unembd[0] = data_torch
            elif ".1." in name:
                self._altup_unembd[1] = data_torch
            elif ".2." in name:
                self._altup_unembd[2] = data_torch
            else:
                raise ValueError(f"Unknown name: {name}")
            out = self._stack_matrices(self._altup_unembd)
            if out is not None:
                yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid)
                return
            else:
                return

        if "altup_projections" in name:
            data_torch = data_torch.to(device="cpu")
            if ".0." in name:
                self._altup_proj[0] = data_torch
            elif ".1." in name:
                self._altup_proj[1] = data_torch
            elif ".2." in name:
                self._altup_proj[2] = data_torch
            else:
                raise ValueError(f"Unknown name: {name}")
            out = self._stack_matrices(self._altup_proj)
            if out is not None:
                yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma4ForConditionalGeneration", "Gemma4ForCausalLM")
@ModelBase.example("google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it", "google/gemma-4-E2B-it")
class Gemma4Model(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4

    def norm_shift(self, name: str) -> float:
        del name # unused
        return 0.0

    def set_vocab(self):
        vocab = gguf.LlamaHfVocab(self.dir_model)
        tokens = []
        scores = []
        toktypes = []
        visible_tokens = {"<|channel>", "<channel|>", "<|tool_call>", "<tool_call|>", "<|tool_response>", "<tool_response|>", "<|\"|>"}

        for text, score, toktype in vocab.all_tokens():
            tokens.append(text)
            scores.append(score)
            text_str = text.decode()
            if text_str in visible_tokens:
                # always render these tokens, so that the chat parser can read them
                toktypes.append(gguf.TokenType.USER_DEFINED)
                logger.info(f"Token '{text_str}' is set to USER_DEFINED")
            else:
                toktypes.append(toktype)

        assert len(tokens) == vocab.vocab_size

        self.gguf_writer.add_tokenizer_model("gemma4")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)
        self.gguf_writer.add_add_space_prefix(False)
        self.gguf_writer.add_add_bos_token(True)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        num_kv_shared_layers = self.hparams["num_kv_shared_layers"]
        self.gguf_writer.add_shared_kv_layers(num_kv_shared_layers)

        # per-layer embedding is optional
        n_pl_embd = self.hparams.get("hidden_size_per_layer_input") or 0
        self.gguf_writer.add_embedding_length_per_layer_input(n_pl_embd)

        swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
        self.gguf_writer.add_sliding_window_pattern(swa_layers)

        per_layer_config = self.hparams.get("per_layer_config")
        layer_types = self.hparams.get("layer_types", [])
        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
            for layer_idx, layer_config in per_layer_config.items():
                layer_idx = int(layer_idx)
                if layer_idx < len(layer_types):
                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
                        head_dim_full = layer_config["head_dim"]
                        break

        assert head_dim_full is not None

        head_dim_swa = self.hparams["head_dim"]
        # correct the head dim for global/swa layers
        self.gguf_writer.add_key_length(head_dim_full)
        self.gguf_writer.add_value_length(head_dim_full)
        self.gguf_writer.add_key_length_swa(head_dim_swa)
        self.gguf_writer.add_value_length_swa(head_dim_swa)

        expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
        if expert_intermediate_size is not None:
            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)

        # if use_double_wide_mlp is set, we need to adjust the value for kv shared layers
        use_double_wide_mlp = self.hparams.get("use_double_wide_mlp", False)
        first_kv_shared_layer_idx = self.block_count - num_kv_shared_layers
        if use_double_wide_mlp:
            n_ff = self.hparams["intermediate_size"]
            n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
            self.gguf_writer.add_feed_forward_length(n_ff_arr)

        if (num_key_value_heads_full := self.hparams.get("num_global_key_value_heads")) is None and per_layer_config is not None:
            for layer_idx, layer_config in per_layer_config.items():
                layer_idx = int(layer_idx)
                if layer_idx < len(layer_types):
                    if layer_types[layer_idx] == "full_attention" and "num_key_value_heads" in layer_config:
                        num_key_value_heads_full = layer_config["num_key_value_heads"]
                        break

        num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
        if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
            value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
            self.gguf_writer.add_head_count_kv(value_arr)

        # handle n_rot differently for global vs swa layers
        partial_rotary_factor_swa = self.rope_parameters.get("partial_rotary_factor", 1.0)
        n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
        n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
        self.gguf_writer.add_rope_dimension_count(n_rot_full)
        self.gguf_writer.add_rope_dimension_count_swa(n_rot_swa)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        # full layer uses "proportional" rope with partial_rotary_factor=0.25
        # the expected ordering is cc000000ss000000 (c = cos, s = sin, 0 = unrotated),
        # but ggml neox only supports ccss000000000000, and we cannot rearrange the head because that will break use_alternative_attention
        # solution is to set specific freq_factors for the unrotated dims

        # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
        rope_params_full = self.hparams["rope_parameters"]["full_attention"]
        assert rope_params_full["rope_type"] == "proportional"

        per_layer_config = self.hparams.get("per_layer_config")
        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
            layer_types = self.hparams.get("layer_types", [])
            for layer_idx, layer_config in per_layer_config.items():
                layer_idx = int(layer_idx)
                if layer_idx < len(layer_types):
                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
                        head_dim_full = layer_config["head_dim"]
                        break

        assert head_dim_full is not None

        partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
        n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
        n_unrot_full = int(head_dim_full / 2) - n_rot_full
        values = [1.0] * n_rot_full + [1e30] * n_unrot_full
        rope_freqs_full = torch.tensor(values, dtype=torch.float32)
        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), rope_freqs_full)

    def _generate_nvfp4_tensors(self):
        # Gemma-4 stores a per-layer router.per_expert_scale ([n_expert]) that scales
        # each expert's contribution. It's mathematically equivalent to a per-expert
        # scalar on the down_proj output, which is exactly where ffn_down_exps_s is
        # applied at inference. Fold it into each expert's NVFP4 weight_scale_2 so the
        # existing NVFP4 path produces the right scales.
        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
        for name in [n for n in self.model_tensors if n.endswith(".router.per_expert_scale")]:
            bid_match = re.search(r"\.layers\.(\d+)\.", name)
            if bid_match is None:
                continue
            bid = bid_match.group(1)
            prefix = name[: name.index(f".layers.{bid}.") + len(f".layers.{bid}.")]
            w2_targets = [f"{prefix}experts.{e}.down_proj.weight_scale_2" for e in range(n_experts)]
            present = [w2 in self.model_tensors for w2 in w2_targets]
            if not any(present):
                continue
            assert all(present), f"layer {bid}: partial NVFP4 quantization across experts"
            r = self.model_tensors.pop(name)
            for e, w2 in enumerate(w2_targets):
                s = self.model_tensors[w2]
                self.model_tensors[w2] = lambda s=s, r=r, i=e: s() * r()[i]
        super()._generate_nvfp4_tensors()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith("per_dim_scale") or name.endswith("layer_scalar"):
            name = name + ".weight"
        if ".experts." in name and not name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")):
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith("router.scale"):
            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_INP, bid, ".scale")
            yield (name, data_torch)
            return
        if ".per_expert_scale" in name:
            # convert per-expert scale to FFN down scale
            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid, ".scale")
            yield (name, data_torch)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma4DSparkModel")
class Gemma4DSparkModel(DFlashModel):
    model_arch = gguf.MODEL_ARCH.DFLASH

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if not self.hparams.get("attention_k_eq_v", False):
            raise ValueError("Gemma4 DSpark currently requires attention_k_eq_v")
        if self.hparams.get("layer_types") != ["full_attention"] * self.block_count:
            raise ValueError("Gemma4 DSpark currently requires uniform full_attention layer types")
        if self.hparams.get("hidden_activation", "gelu_pytorch_tanh") != "gelu_pytorch_tanh":
            raise ValueError("Gemma4 DSpark currently requires hidden_activation=gelu_pytorch_tanh")
        if self.hparams.get("attention_bias", False) or self.hparams.get("enable_moe_block", False):
            raise ValueError("Gemma4 DSpark attention bias and MoE are not supported")
        if (self.hparams.get("draft_vocab_size") or self.hparams["vocab_size"]) != self.hparams["vocab_size"]:
            raise ValueError("Gemma4 DSpark currently requires a full draft vocabulary")
        if "model.lm_head.weight" not in self.model_tensors and self.hparams.get("tie_word_embeddings") is not True:
            raise ValueError("Gemma4 DSpark requires lm_head.weight unless tie_word_embeddings is true")

        self.dflash_config = self.hparams.get("dflash_config", {})
        markov_type = self.dflash_config.get("markov_head_type", self.hparams.get("markov_head_type", "vanilla"))
        if markov_type != "vanilla":
            raise ValueError("Gemma4 DSpark currently requires a vanilla Markov head")

        # Gemma4TextConfig supplies these defaults when rope_parameters is absent.
        rope = self.hparams.get("rope_parameters") or {
            "full_attention": {"rope_type": "proportional", "partial_rotary_factor": 0.25, "rope_theta": 1000000.0},
        }
        self.rope_parameters = rope.get("full_attention", rope)
        if self.rope_parameters.get("rope_type") not in ("default", "proportional"):
            raise ValueError("Gemma4 DSpark requires default or proportional RoPE")

    def set_vocab(self):
        super().set_vocab()
        mask_id = self.dflash_config.get("mask_token_id", self.hparams.get("mask_token_id"))
        if mask_id is None:
            raise ValueError("Gemma4 DSpark requires mask_token_id")
        if "mask_token_id" not in self.dflash_config:
            self.gguf_writer.add_mask_token_id(mask_id)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        head_dim = int(self.hparams["global_head_dim"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_global_key_value_heads"])
        self.gguf_writer.add_key_length(head_dim)
        self.gguf_writer.add_value_length(head_dim)
        self.gguf_writer.add_rope_dimension_count(head_dim)
        self.gguf_writer.add_embedding_scale(self.hparams["hidden_size"] ** 0.5)
        self.gguf_writer.add_attention_scale(1.0)
        self.gguf_writer.add_hidden_act("gelu_pytorch_tanh")

        self.gguf_writer.add_sample_from_anchor(self.hparams.get("sample_from_anchor", True))
        target_layers = self.dflash_config.get("target_layer_ids", self.hparams.get("target_layer_ids"))
        if not target_layers:
            raise ValueError("Gemma4 DSpark requires target_layer_ids")
        self.gguf_writer.add_has_confidence_head(any("confidence_head.proj" in name for name in self.model_tensors))

        if self.hparams.get("final_logit_softcapping"):
            raise ValueError("Gemma4 DSpark logit softcapping is not supported")
        # The top-level sliding_window is inert unless the draft enables SWA.
        if self.dflash_config.get("use_swa", False):
            window = self.dflash_config["swa_window_size"]
            if window <= 0:
                raise ValueError("Gemma4 DSpark swa_window_size must be positive")

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if not name.startswith("model."):
            name = "model." + name
        if name.endswith(".layer_scalar"):
            name += ".weight"
        name = name.replace("model.confidence_proj.", "model.confidence_head.proj.")
        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # The shared DFlash map assigns this name to Qwen's pre-FFN norm.
        if name.endswith(".post_attention_layernorm.weight"):
            name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_POST_NORM, bid)
        elif name.endswith(".pre_feedforward_layernorm.weight"):
            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_NORM, bid)
        yield from super().modify_tensors(data_torch, name, bid)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if self.rope_parameters["rope_type"] == "proportional":
            # Keep the unrotated dimensions in place, as in the Gemma4 converter.
            head_dim = int(self.hparams["global_head_dim"])
            fraction_value = self.rope_parameters.get("partial_rotary_factor", 0.25)
            if not isinstance(fraction_value, (int, float)):
                raise ValueError("Gemma4 DSpark partial_rotary_factor must be numeric")
            fraction = float(fraction_value)
            n_rot = int(head_dim * fraction / 2)
            if not 0 < fraction <= 1 or head_dim * fraction != 2 * n_rot:
                raise ValueError("Gemma4 DSpark rotary dimension count must be positive and even")
            factors = torch.tensor([1.0] * n_rot + [1e30] * (head_dim // 2 - n_rot), dtype=torch.float32)
            yield self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), factors


@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
class Gemma4UnifiedModel(Gemma4Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4

    def _get_suppress_tokens(self) -> Sequence[int] | None:
        gen_cfg_path = self.dir_model / "generation_config.json"
        if gen_cfg_path.is_file():
            with open(gen_cfg_path, encoding="utf-8") as f:
                gen_cfg = json.load(f)
                return gen_cfg.get("suppress_tokens")
        return None

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        suppress_tokens = self._get_suppress_tokens()
        if suppress_tokens is not None:
            self.gguf_writer.add_suppress_tokens(suppress_tokens)


@ModelBase.register("Gemma4AssistantForCausalLM", "Gemma4UnifiedAssistantForCausalLM")
@ModelBase.example("google/gemma-4-31B-it-assistant", "google/gemma-4-26B-A4B-it-assistant", "google/gemma-4-E2B-it-assistant")
class Gemma4AssistantModel(Gemma4Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4_ASSISTANT

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "masked_embedding" in name:
            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
            return None

        return super().filter_tensors(item)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_embedding_length_out(self.hparams["backbone_hidden_size"])
        self.gguf_writer.add_nextn_predict_layers(self.block_count)


@ModelBase.register("Gemma4ForConditionalGeneration")
@ModelBase.example("google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it", "google/gemma-4-E2B-it")
class Gemma4VisionAudioModel(MmprojModel):
    has_audio_encoder = True
    has_vision_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = 224 # unused, but set to avoid error

        # remap audio hparams
        if self.hparams_audio:
            self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
            if "hidden_size" in self.hparams_audio:
                self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
        else:
            self.has_audio_encoder = False

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # vision params
        assert self.hparams_vision is not None
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))

        # audio params
        if self.has_audio_encoder:
            assert self.hparams_audio is not None
            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
            self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-6))

    def is_audio_tensor(self, name: str) -> bool:
        return "audio_tower" in name or "embed_audio" in name

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if self.is_audio_tensor(name):
            if ".conv" in name or "_conv" in name and ".weight" in name:
                return gguf.GGMLQuantizationType.F32
        if "position_embedding_table" in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid # unused

        if len(data_torch.shape) == 0:
            # convert scalar tensors (input/output_mix/max) to 1D tensors
            data_torch = data_torch.unsqueeze(0)

        if self.is_audio_tensor(name):
            assert self.hparams_audio is not None
            name = name.replace("model.audio_tower.", "conformer.")
            name = name.replace(".linear.", ".")
            if name.endswith("per_dim_key_scale") or name.endswith("per_dim_scale"):
                name = name + ".weight"
                data_torch = torch.nn.functional.softplus(data_torch)
            if "lconv1d.depthwise_conv1d" in name and name.endswith(".weight"):
                assert data_torch.shape[1] == 1
                data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
            yield (mapped_name, data_torch)

        else:
            name = name.replace("model.vision_tower.encoder.", "vision_model.model.")
            name = name.replace(".linear.weight", ".weight")
            if name.endswith("layer_scalar") or name.endswith("position_embedding_table"):
                name = name + ".weight"
            if name.endswith("patch_embedder.input_proj.weight"):
                n_embd, ksize_sq_c = data_torch.shape
                patch_size = int((ksize_sq_c // 3) ** 0.5)
                data_torch = data_torch.reshape(n_embd, patch_size, patch_size, 3)
                data_torch = data_torch.permute(0, 3, 1, 2).contiguous()
            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
            yield (mapped_name, data_torch)


@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
class Gemma4UnifiedVisionAudioModel(Gemma4VisionAudioModel):
    has_audio_encoder = True
    has_vision_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        assert self.hparams_audio is not None
        text_embd_dim = self.hparams_vision["mm_embed_dim"]
        self.hparams_vision["hidden_size"] = text_embd_dim
        self.hparams_audio["hidden_size"] = self.hparams_audio["audio_embed_dim"]
        # this is a transformer-less vision tower, the params below are redundant but set to avoid error
        self.hparams_vision["intermediate_size"] = 0
        self.hparams_vision["num_layers"] = 0
        self.hparams_vision["num_attention_heads"] = 0
        self.hparams_audio["intermediate_size"] = 0
        self.hparams_audio["num_layers"] = 0
        self.hparams_audio["num_attention_heads"] = 0

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4UV)
        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4UA)

    def modify_tensors(self, data_torch, name, bid):
        if name.endswith("pos_embedding"):
            name += ".weight"
            data_torch = data_torch.permute(1, 0, 2)
        elif ".pos_norm." in name:
            # rename to patch_ln3 to reuse the tensor name scheme
            name = name.replace(".pos_norm.", ".patch_ln3.")
        elif "patch_dense.weight" in name:
            # ggml im2col outputs in RR..GG..BB.. (CHW) order, but weight expects RGBRGB.. (HWC).
            # Permute columns so column i aligns with CHW input position i.
            assert self.hparams_vision is not None
            if "model_patch_size" in self.hparams_vision:
                p = self.hparams_vision["model_patch_size"]
            else:
                p = self.hparams_vision["patch_size"] * self.hparams_vision["pooling_kernel_size"]
            i = torch.arange(p * p * 3)
            ch  = i // (p * p)
            row = (i % (p * p)) // p
            col = i % p
            # perm[i] = HWC column index for CHW position i
            perm = row * p * 3 + col * 3 + ch
            data_torch = data_torch[:, perm]
        elif "patch_ln1.weight" in name or "patch_ln1.bias" in name:
            # same permutation for patch_ln1 as patch_dense to align with CHW input order
            assert self.hparams_vision is not None
            if "model_patch_size" in self.hparams_vision:
                p = self.hparams_vision["model_patch_size"]
            else:
                p = self.hparams_vision["patch_size"] * self.hparams_vision["pooling_kernel_size"]
            i = torch.arange(p * p * 3)
            ch  = i // (p * p)
            row = (i % (p * p)) // p
            col = i % p
            # perm[i] = HWC index for CHW position i
            perm = row * p * 3 + col * 3 + ch
            data_torch = data_torch[perm]
        return super().modify_tensors(data_torch, name, bid)