#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/gemma3n/modular_gemma3n.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_gemma3n.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any

from ...configuration_utils import PreTrainedConfig, layer_type_validation
from ...modeling_rope_utils import RopeParameters
from ...utils import is_timm_available, logging, requires_backends


if is_timm_available():
    from timm.data import ImageNetInfo, infer_imagenet_subset


logger = logging.get_logger(__name__)


class Gemma3nTextConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Gemma3nTextModel`]. It is used to instantiate an
    Gemma3nTextModel model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B, e.g.
    [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).

    Configuration objects that inherit from [`Gemma3nTextConfig`] and can be used to control the model outputs. Read
    the documentation from [`Gemma3nTextConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 262400):
            Vocabulary size of the Gemma3nText model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`Gemma3nTextModel`]
        vocab_size_per_layer_input (`int`, *optional*, defaults to 262144):
            Vocabulary size of the per-layer text embeddings that augment the standard embeddings.
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        hidden_size_per_layer_input (`int`, *optional*, defaults to 256):
            Dimension of the hidden representations for per-layer emebeddings.
        intermediate_size (`int` or `Sequence[int]`, *optional*, defaults to 16384):
            Dimension of the MLP representations. MatFormer configurations may wish to provide a sequence of integers
            to account for variable intermediate_size values across layers. In such cases,
            `len(intermediate_size) == num_hidden_layers`.
        num_hidden_layers (`int`, *optional*, defaults to 35):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 2):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout this
            [paper](https://huggingface.co/papers/2305.13245). If not specified, will default to `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 256):
            The attention head dimension.
        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            The non-linear activation function (function or string) in the decoder. Will default to
            `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"`
            activation function.
        max_position_embeddings (`int`, *optional*, defaults to 32768):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 1):
            End of stream token id.
        bos_token_id (`int`, *optional*, defaults to 2):
            Beginning of stream token id.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        sliding_window (`int`, *optional*, defaults to 512):
            This is the size of the sliding window used by local attention layers.
        layer_types (`Optional`, *optional*):
            A sequence of strings defining the attention type for that layer as either "sliding_attention" or
            "full_attention". If not provided, `layer_types` will de inferred from `num_hidden_layers` using a pattern
            of four "sliding_attention" layers followed one "full_attention". The last layer in the model should always
            be a "full_attention" layer.
        final_logit_softcapping (`float`, *optional*, defaults to 30.0):
            Scaling factor when applying tanh softcapping on the logits.
        altup_active_idx (`int`, *optional*, defaults to 0):
            The index of the prediction from which AltUp will compute additional predictions or correct
        altup_coef_clip (`float`, *optional*, defaults to 120.0):
            The maximum amplitude of an AltUp prediction or correction coefficient weight.
        altup_correct_scale (`bool`, *optional*, defaults to `True`):
            If True, apply the `AltUp.correct_output_scale` to the corrected prediction at `altup_active_idx`.
        altup_num_inputs (`int`, *optional*, defaults to 4):
            The number of predictions that AltUp should be make given the input sequence.
        num_kv_shared_layers (`int`, *optional*, defaults to 15):
            The number of layer that share KV cache values. During the forward pass, the last `num_kv_shared_layers`
            layers in the model "share" the KV values in that each local and global layer in this range uses the KV
            cache values computed for the last local or global layer, respectively, before entering this range. The
            value should be a multiple of the attention pattern size (see `layer_types` parameter).
        laurel_rank (int, *optional*, defaults to 64):
            The intermediate size for the linear projections in the Learned Augmented Residual Layer.
        activation_sparsity_pattern (Sequence[float], *optional*):
            The sparsity factor used to extract the top-k activations for a given layer. The provided Sequence must
            explicitly provide a sparsity value for each layer in the model. By default, the first 10 layers are
            sparse with a sparsity factor of 0.95 and the rest are dense.

    ```python
    >>> from transformers import Gemma3nTextModel, Gemma3nTextConfig

    >>> # Initializing a Gemma3nText gemma3n_text-E4B style configuration
    >>> configuration = Gemma3nTextConfig()

    >>> # Initializing a model from the gemma3n_text-E4B style configuration
    >>> model = Gemma3nTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "gemma3n_text"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    default_theta = {"global": 1_000_000.0, "local": 10_000.0}

    def __init__(
        self,
        vocab_size: int = 262_400,
        vocab_size_per_layer_input: int = 262_144,
        hidden_size: int = 2048,
        hidden_size_per_layer_input: int = 256,
        intermediate_size: int | Sequence[int] = 16_384,
        num_hidden_layers: int = 35,
        num_attention_heads: int = 8,
        num_key_value_heads: int = 2,
        head_dim: int = 256,
        hidden_activation: str = "gelu_pytorch_tanh",
        max_position_embeddings: int = 32_768,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-6,
        use_cache: bool = True,
        pad_token_id: int = 0,
        eos_token_id: int = 1,
        bos_token_id: int = 2,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        attention_bias: bool = False,
        attention_dropout: float = 0.0,
        sliding_window: int = 512,
        layer_types: Sequence[str] | None = None,
        final_logit_softcapping: float = 30.0,
        altup_active_idx: int = 0,
        altup_coef_clip: float = 120.0,
        altup_correct_scale: bool = True,
        altup_num_inputs: int = 4,
        num_kv_shared_layers: int = 15,
        laurel_rank: int = 64,
        activation_sparsity_pattern: float | Sequence[float] | None = None,
        **kwargs,
    ):
        if isinstance(intermediate_size, Sequence) and (intsize_len := len(intermediate_size)) != num_hidden_layers:
            raise ValueError(
                "intermediate_size must have an explicit intermediate size for every layer or one for all layers. "
                f"Expected {num_hidden_layers} values but got {intsize_len}."
            )
        elif not isinstance(intermediate_size, Sequence):
            intermediate_size = [intermediate_size] * num_hidden_layers

        self.vocab_size = vocab_size
        self.vocab_size_per_layer_input = vocab_size_per_layer_input
        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.head_dim = head_dim
        self.num_key_value_heads = num_key_value_heads
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.hidden_activation = hidden_activation
        self.sliding_window = sliding_window
        self.final_logit_softcapping = final_logit_softcapping
        self.layer_types = layer_types

        if layer_types is None:
            self.layer_types = [
                "full_attention" if (i + 1) % 5 == 0 else "sliding_attention" for i in range(self.num_hidden_layers)
            ]
        else:
            self.layer_types = layer_types

        layer_type_validation(self.layer_types, self.num_hidden_layers)

        self.hidden_size_per_layer_input = hidden_size_per_layer_input
        self.num_kv_shared_layers = num_kv_shared_layers

        self.altup_active_idx = altup_active_idx
        self.altup_coef_clip = altup_coef_clip
        self.altup_correct_scale = altup_correct_scale
        self.altup_num_inputs = altup_num_inputs

        self.laurel_rank = laurel_rank

        if activation_sparsity_pattern is None:
            num_sparse_layers = 10 if num_hidden_layers > 10 else 0
            activation_sparsity_pattern = [0.95] * num_sparse_layers + [0.0] * (num_hidden_layers - num_sparse_layers)

        if (len_asp := len(activation_sparsity_pattern)) != num_hidden_layers:
            raise ValueError(
                "activation_sparsity_pattern must have an explicit activation sparsity value for every layer."
                f"Expected {num_hidden_layers} values but got {len_asp}."
            )
        self.activation_sparsity_pattern = activation_sparsity_pattern
        self.rope_parameters = rope_parameters
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )

    def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)

        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters`
        # as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format
        default_rope_params = {
            "sliding_attention": {"rope_type": "default"},
            "full_attention": {"rope_type": "default"},
        }
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params
        if rope_scaling is not None:
            self.rope_parameters["full_attention"].update(rope_scaling)
        self.rope_parameters["full_attention"].setdefault(
            "rope_theta", kwargs.pop("rope_theta", self.default_theta["global"])
        )
        self.rope_parameters["sliding_attention"].setdefault(
            "rope_theta", kwargs.pop("rope_local_base_freq", self.default_theta["local"])
        )

        # Standardize and validate the correctness of rotary position embeddings parameters
        self.standardize_rope_params()
        self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
        return kwargs


class Gemma3nAudioConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Gemma3nAudioEncoder`]. It is used to instantiate
    an `Gemma3nAudioEncoder` model according to the specified arguments, defining the model architecture. Instantiating
    a configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B, e.g.,
    [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).

    Configuration objects that inherit from [`Gemma3nAudioConfig`] and can be used to control the model outputs. Read
    the documentation from [`Gemma3nAudioConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 128):
            Vocabulary size of the additional hard-token embeddings for audio model. These augment the embeddings
            included in the `Gemma3nTextModel` to provide, e.g., the end of audio and audio soft token placeholder
            tokens when converting `input_ids` to embeddings in the `Gemma3nForConditionalGeneration` model.
        vocab_offset (`int`, *optional*, defaults to 262272):
            Offset between the tokenizer vocab index for the token ids embedded by `Gemma3nMultimodalEmbedder` and the
            0-indexed `Gemma3nMultimodalEmbedder.embedding` table.
        input_feat_size (`int`, *optional*, defaults to 128):
            The number of channels in each mel-spectrogram frame.
        hidden_size (`int`, *optional*, defaults to 1536):
            Dimension of the hidden representations.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        gradient_clipping (`float`, *optional*, defaults to 10000000000.0):
            Clipping value used to stabilize extremely large gradient values.
        conf_attention_chunk_size (`int`, *optional*, defaults to 12):
            The sub-sequence size for local attention processing inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_attention_context_left (`int`, *optional*, defaults to 13):
            The left context size of the local attention inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_attention_context_right (`int`, *optional*, defaults to 0):
            The right context size of the local attention inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_attention_logit_cap (`float`, *optional*, defaults to 50.0):
            Logit cap applied during local attention inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_num_attention_heads (`int`, *optional*, defaults to 8):
            The number of attention heads in local attention inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_num_hidden_layers (`int`, *optional*, defaults to 12):
            The number of layers that use local attention inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_conv_kernel_size (`int`, *optional*, defaults to 5):
            Convolution kernel size for the conformer block inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_reduction_factor (`int`, *optional*, defaults to 4):
            Reduction factor used in the conformer block inside the Conformer ("conf") section of the
            Universal Speech Model.
        conf_residual_weight (`float`, *optional*, defaults to 0.5):
            Residual connection weight inside the Conformer ("conf") section of the
            Universal Speech Model.
        sscp_conv_channel_size (`tuple(int, int)`, *optional*, defaults to `(128, 32)`):
            The channel sizes for the first and second convolutional layers in the Sub-sample Convolution Projection
            ("sscp") section of the Universal Speech Model.
        sscp_conv_group_norm_eps (`float`, *optional*, defaults to 0.001):
            Epsilon used in group normalization in the subsample convolution projection in the Sub-sample Convolution
            Projection ("sscp") section of the Universal Speech Model.
        sscp_conv_kernel_size (`tuple(tuple(int, int), tuple(int, int))`, *optional*, defaults to `((3, 3), (3, 3))`):
            Kernel sizes of the two convolutional layers in the subsample convolution projection  in the Sub-sample
            Convolution Projection ("sscp") section of the Universal Speech Model. The kernel sizes are specified as a
            tuple of height and width for each layer, where the height corresponds to the time dimension and the width
            corresponds to the frequency dimension.
        sscp_conv_stride_size (`tuple(tuple(int, int), tuple(int, int))`, *optional*, defaults to `((2, 2), (2, 2))`):
            Stride sizes of the two convolutional layers in the subsample convolution projection in the Sub-sample
            Convolution Projection ("sscp") section of the Universal Speech Model. The stride sizes are specified as a
            tuple of height and width for each layer, where the height corresponds to the time dimension and the width
            corresponds to the frequency dimension.

    Example:

    ```python
    >>> from transformers import Gemma3nAudioConfig, Gemma3nAudioEncoder

    >>> # Initializing a Gemma3nAudioEncoder gemma3n_audio-E4B-style configuration
    >>> configuration = Gemma3nAudioConfig()

    >>> # Initializing a model from the gemma3n_audio-E4B style configuration
    >>> model = Gemma3nAudioEncoder(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "gemma3n_audio"

    def __init__(
        self,
        vocab_size: int = 128,
        vocab_offset: int = 262_144 + 128,  # text vocab size + vision vocab size
        input_feat_size: int = 128,
        hidden_size: int = 1536,
        rms_norm_eps: float = 1e-6,
        gradient_clipping: float = 10_000_000_000.0,
        conf_attention_chunk_size: int = 12,
        conf_attention_context_left: int = 13,
        conf_attention_context_right: int = 0,
        conf_attention_logit_cap: float = 50.0,
        conf_num_attention_heads: int = 8,
        conf_num_hidden_layers: int = 12,
        conf_conv_kernel_size: int = 5,
        conf_reduction_factor: int = 4,
        conf_residual_weight: float = 0.5,
        sscp_conv_channel_size: tuple[int, int] = (128, 32),
        sscp_conv_group_norm_eps: float = 1e-3,
        sscp_conv_kernel_size: tuple[tuple[int, int], tuple[int, int]] = (
            (3, 3),
            (3, 3),
        ),
        sscp_conv_stride_size: tuple[tuple[int, int], tuple[int, int]] = (
            (2, 2),
            (2, 2),
        ),
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.input_feat_size = input_feat_size
        self.hidden_size = hidden_size
        self.rms_norm_eps = rms_norm_eps
        self.vocab_size = vocab_size
        self.vocab_offset = vocab_offset
        self.gradient_clipping = gradient_clipping
        self.conf_attention_chunk_size = conf_attention_chunk_size
        self.conf_attention_context_left = conf_attention_context_left
        self.conf_attention_context_right = conf_attention_context_right
        self.conf_attention_logit_cap = conf_attention_logit_cap
        self.conf_num_attention_heads = conf_num_attention_heads
        self.conf_num_hidden_layers = conf_num_hidden_layers
        self.conf_conv_kernel_size = conf_conv_kernel_size
        self.conf_reduction_factor = conf_reduction_factor
        self.conf_residual_weight = conf_residual_weight
        self.sscp_conv_channel_size = sscp_conv_channel_size
        self.sscp_conv_group_norm_eps = sscp_conv_group_norm_eps
        self.sscp_conv_kernel_size = sscp_conv_kernel_size
        self.sscp_conv_stride_size = sscp_conv_stride_size


class Gemma3nVisionConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration for a timm backbone [`TimmWrapper`]. It is used to
    instantiate an timm model model according to the specified arguments, defining the model architecture.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B
    vision tower, e.g. [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).

    Configuration objects inherit from [`Gemma3nVisionConfig`] and can be used to control the model outputs. Read the
    documentation from [`Gemma3nVisionConfig`] for more information.

    Config loads imagenet label descriptions and stores them in `id2label` attribute, `label2id` attribute for default
    imagenet models is set to `None` due to occlusions in the label descriptions.

    Args:
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        do_pooling (`bool`, *optional*, defaults to `False`):
            Whether to do pooling for the last_hidden_state in `TimmWrapper` or not.
        architecture (`str`, *optional*, defaults to `"mobilenetv5_300m_enc"`):
            Determines vision architecture for TimmWrapper.
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        vocab_size (`int`, *optional*, defaults to 128):
            Vocabulary size of the additional hard-token embeddings for vision model.
        vocab_offset (`int`, *optional*, defaults to 262144):
            Offset between the tokenizer vocab index for the token ids embedded by `Gemma3nMultimodalEmbedder` and the
            0-indexed `Gemma3nMultimodalEmbedder.embedding` table.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.

    Example:
    ```python
    >>> from transformers import Gemma3nVisionConfig, TimmWrapper

    >>> # Initializing a TimmWrapper gemma3n_vision-E4B-style configuration
    >>> configuration = Gemma3nVisionConfig()

    >>> # Initializing a gemma3n_vision-E4B-style TimmWrapper from the configuration
    >>> model = TimmWrapper(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "gemma3n_vision"

    def __init__(
        self,
        initializer_range: float = 0.02,
        do_pooling: bool = False,
        architecture: str = "mobilenetv5_300m_enc",
        hidden_size: int = 2048,
        vocab_size: int = 128,
        vocab_offset: int = 262_144,
        rms_norm_eps: float = 1e-06,
        model_args: dict | None = None,
        **kwargs,
    ):
        self.architecture = architecture
        self.initializer_range = initializer_range
        self.do_pooling = do_pooling
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.vocab_offset = vocab_offset
        self.rms_norm_eps = rms_norm_eps
        self.architecture = architecture
        self.initializer_range = initializer_range
        self.do_pooling = do_pooling
        self.model_args = model_args  # named "model_args" for BC with timm
        super().__init__(**kwargs)

    @classmethod
    def from_dict(cls, config_dict: dict[str, Any], **kwargs):
        # Create a copy to avoid mutating the original dict
        config_dict = config_dict.copy()

        label_names = config_dict.get("label_names")
        is_custom_model = "num_labels" in kwargs or "id2label" in kwargs

        # if no labels added to config, use imagenet labeller in timm
        if label_names is None and not is_custom_model:
            requires_backends(cls, ["timm"])
            imagenet_subset = infer_imagenet_subset(config_dict)
            if imagenet_subset:
                dataset_info = ImageNetInfo(imagenet_subset)
                synsets = dataset_info.label_names()
                label_descriptions = dataset_info.label_descriptions(as_dict=True)
                label_names = [label_descriptions[synset] for synset in synsets]

        if label_names is not None and not is_custom_model:
            kwargs["id2label"] = dict(enumerate(label_names))

            # if all label names are unique, create label2id mapping as well
            if len(set(label_names)) == len(label_names):
                kwargs["label2id"] = {name: i for i, name in enumerate(label_names)}
            else:
                kwargs["label2id"] = None

        # timm config stores the `num_classes` attribute in both the root of config and in the "pretrained_cfg" dict.
        # We are removing these attributes in order to have the native `transformers` num_labels attribute in config
        # and to avoid duplicate attributes
        num_labels_in_kwargs = kwargs.pop("num_labels", None)
        num_labels_in_dict = config_dict.pop("num_classes", None)

        # passed num_labels has priority over num_classes in config_dict
        kwargs["num_labels"] = num_labels_in_kwargs or num_labels_in_dict

        # pop num_classes from "pretrained_cfg",
        # it is not necessary to have it, only root one is used in timm
        if "pretrained_cfg" in config_dict and "num_classes" in config_dict["pretrained_cfg"]:
            config_dict["pretrained_cfg"].pop("num_classes", None)

        return super().from_dict(config_dict, **kwargs)

    def to_dict(self) -> dict[str, Any]:
        output = super().to_dict()
        output.setdefault("num_classes", self.num_labels)
        output.setdefault("label_names", list(self.id2label.values()))
        output.pop("id2label", None)
        output.pop("label2id", None)
        return output


class Gemma3nConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Gemma3nForConditionalGeneration`]. It is used to
    instantiate a Gemma3nForConditionalGeneration according to the specified arguments, defining the model
    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
    Gemma3n-E4B.

    e.g. [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B)

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        text_config (`Union[Gemma3nTextConfig, dict]`, *optional*):
            The config object of the text backbone.
        vision_config (`Union[AutoConfig, dict]`,  *optional*):
            Custom vision config or dict.
        audio_config (`Union[AutoConfig, dict]`,  *optional*):
            Custom audio config or dict.
        audio_soft_tokens_per_image (`int`, *optional*, defaults to 188):
            The number of soft tokens per audio clip.
        vision_soft_tokens_per_image (`int`, *optional*, defaults to 256):
            The number of soft tokens per image.
        boi_token_id (`int`, *optional*, defaults to 255999):
            The begin-of-image token index to wrap the image prompt.
        eoi_token_id (`int`, *optional*, defaults to 262144):
            The end-of-image token index to wrap the image prompt.
        image_token_id (`int`, *optional*, defaults to 262145):
            The image token index to encode the image prompt.
        boa_token_id (`int`, *optional*, defaults to 256000):
            The begin-of-audio token index to wrap the audio prompt.
        eoa_token_id (`int`, *optional*, defaults to 262272):
            The end-of-audio token index to wrap the audio prompt.
        audio_token_id (`int`, *optional*, defaults to 262273):
            The audio token index to encode the audio prompt.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.


    Example:

    ```python
    >>> from transformers import Gemma3nForConditionalGeneration, Gemma3nConfig, Gemma3nTextConfig

    >>> # Initializing a MobileNet vision config, which is loaded from TIMM
    >>> vision_config = Gemma3nVisionConfig()

    >>> # Initializing a Gemma3n Audio config
    >>> audio_config = Gemma3nAudioConfig()

    >>> # Initializing a Gemma3n Text config
    >>> text_config = Gemma3nTextConfig()

    >>> # Initializing a Gemma3n gemma-3-4b style configuration
    >>> configuration = Gemma3nConfig(text_config, vision_config, audio_config)

    >>> # Initializing a model from the gemma-3-4b style configuration
    >>> model = Gemma3nTextConfig(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "gemma3n"
    sub_configs = {
        "text_config": Gemma3nTextConfig,
        "vision_config": Gemma3nVisionConfig,
        "audio_config": Gemma3nAudioConfig,
    }

    def __init__(
        self,
        text_config: Gemma3nTextConfig | dict[str, Any] | None = None,
        vision_config: Gemma3nVisionConfig | dict[str, Any] | None = None,
        audio_config: Gemma3nAudioConfig | dict[str, Any] | None = None,
        audio_soft_tokens_per_image: int = 188,
        vision_soft_tokens_per_image: int = 256,
        boi_token_id: int = 255_999,
        eoi_token_id: int = 262_144,
        image_token_id: int = 262_145,
        boa_token_id: int = 256_000,
        eoa_token_id: int = 262_272,
        audio_token_id: int = 262_273,
        initializer_range: float = 0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if isinstance(text_config, dict):
            text_config = Gemma3nTextConfig(**text_config)
        elif text_config is None:
            text_config = Gemma3nTextConfig()
            logger.info("text_config is None. Using default Gemma3nTextConfig.")

        if isinstance(vision_config, dict):
            vision_config = Gemma3nVisionConfig(**vision_config)
        elif vision_config is None:
            vision_config = Gemma3nVisionConfig()
            logger.info("vision_config is None. Using default Gemma3nVisionConfig.")

        if isinstance(audio_config, dict):
            audio_config = Gemma3nAudioConfig(**audio_config)
        elif audio_config is None:
            audio_config = Gemma3nAudioConfig()
            logger.info("audio_config is None. Using default Gemma3nAudioConfig.")

        self.text_config = text_config
        self.vision_config = vision_config
        self.audio_config = audio_config

        self.audio_soft_tokens_per_image = audio_soft_tokens_per_image
        self.vision_soft_tokens_per_image = vision_soft_tokens_per_image
        self.boi_token_id = boi_token_id
        self.eoi_token_id = eoi_token_id
        self.image_token_id = image_token_id
        self.boa_token_id = boa_token_id
        self.eoa_token_id = eoa_token_id
        self.audio_token_id = audio_token_id
        self.initializer_range = initializer_range


__all__ = ["Gemma3nAudioConfig", "Gemma3nConfig", "Gemma3nTextConfig", "Gemma3nVisionConfig"]
