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# 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 typing import Any

from ...configuration_utils import PreTrainedConfig, layer_type_validation
from ...modeling_rope_utils import RopeParameters
from ...utils import logging
from ..siglip import SiglipVisionConfig


logger = logging.get_logger(__name__)


class T5Gemma2TextConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`T5Gemma2TextModel`]. It is used to instantiate the encoder's
    text model portion of the T5Gemma2 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 T5Gemma2Text-7B.
    e.g. [google/t5gemma2_text-7b](https://huggingface.co/google/t5gemma2_text-7b)
    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 262208):
            Vocabulary size of the T5Gemma2Text model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`T5Gemma2TextModel`]
        hidden_size (`int`, *optional*, defaults to 2304):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 9216):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 26):
            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 4):
            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, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is 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 131072):
            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.
        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.
        query_pre_attn_scalar (`float`, *optional*, defaults to 256):
            Scaling factor used on the attention scores
        sliding_window (`int`, *optional*, defaults to 4096):
            In T5Gemma2Text, every other layer uses sliding window attention. This is the size of the sliding window.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        final_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the logits.
        attn_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the attention scores.
        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`.
    """

    model_type = "t5gemma2_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.q_norm": "replicated_with_grad_allreduce",
        "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
        "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 | None = 262_208,
        hidden_size: int | None = 2304,
        intermediate_size: int | None = 9216,
        num_hidden_layers: int | None = 26,
        num_attention_heads: int | None = 8,
        num_key_value_heads: int | None = 4,
        head_dim: int | None = 256,
        hidden_activation: str | None = "gelu_pytorch_tanh",
        max_position_embeddings: int | None = 131_072,
        initializer_range: float | None = 0.02,
        rms_norm_eps: int | None = 1e-6,
        use_cache: bool | None = True,
        pad_token_id: int | None = 0,
        eos_token_id: int | None = 1,
        bos_token_id: int | None = 2,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        query_pre_attn_scalar: int | None = 256,
        sliding_window: int | None = 4096,
        layer_types: list[str] | None = None,
        final_logit_softcapping: float | None = None,
        attn_logit_softcapping: float | None = None,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        **kwargs,
    ):
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        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.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.query_pre_attn_scalar = query_pre_attn_scalar
        self.sliding_window = sliding_window
        self.final_logit_softcapping = final_logit_softcapping
        self.attn_logit_softcapping = attn_logit_softcapping
        self.layer_types = layer_types

        # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
        self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6)

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

        self.rope_parameters = rope_parameters
        super().__init__(**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)

        # Set default values if not present
        if self.rope_parameters.get("full_attention") is None:
            self.rope_parameters["full_attention"] = {"rope_type": "default"}
        self.rope_parameters["full_attention"].setdefault(
            "rope_theta", kwargs.pop("rope_theta", self.default_theta["global"])
        )
        if self.rope_parameters.get("sliding_attention") is None:
            self.rope_parameters["sliding_attention"] = {"rope_type": "default"}
        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 T5Gemma2EncoderConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`T5Gemma2EncoderForConditionalGeneration`]. It is used to instantiate an
    T5Gemma2EncoderForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the PaliGemma-2B.

    e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b)

    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[T5Gemma2EncoderTextConfig, dict]`, *optional*):
            The config object of the text backbone.
        vision_config (`Union[AutoConfig, dict]`,  *optional*):
            Custom vision config or dict.
        mm_tokens_per_image (`int`, *optional*, defaults to 256):
            The number of tokens per image embedding.
        boi_token_index (`int`, *optional*, defaults to 255999):
            The begin-of-image token index to wrap the image prompt.
        eoi_token_index (`int`, *optional*, defaults to 256000):
            The end-of-image token index to wrap the image prompt.
        image_token_index (`int`, *optional*, defaults to 262144):
            The image token index to encode the image prompt.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
            Whether to tie weight embeddings

    Example:

    ```python
    >>> from transformers import T5Gemma2EncoderForConditionalGeneration, T5Gemma2EncoderConfig, SiglipVisionConfig, T5Gemma2EncoderTextConfig

    >>> # Initializing a Siglip-like vision config
    >>> vision_config = SiglipVisionConfig()

    >>> # Initializing a T5Gemma2Encoder Text config
    >>> text_config = T5Gemma2EncoderTextConfig()

    >>> # Initializing a T5Gemma2Encoder gemma-3-4b style configuration
    >>> configuration = T5Gemma2EncoderConfig(vision_config, text_config)

    >>> # Initializing a model from the gemma-3-4b style configuration
    >>> model = T5Gemma2EncoderTextConfig(configuration)

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

    model_type = "t5gemma2_encoder"
    attribute_map = {
        "image_token_id": "image_token_index",
        "boi_token_id": "boi_token_index",
        "eoi_token_id": "eoi_token_index",
    }

    sub_configs = {
        "text_config": T5Gemma2TextConfig,
        "vision_config": SiglipVisionConfig,
    }

    def __init__(
        self,
        text_config: T5Gemma2TextConfig | dict[str, Any] | None = None,
        vision_config: SiglipVisionConfig | dict[str, Any] | None = None,
        mm_tokens_per_image: int | None = 256,
        boi_token_index: int | None = 255_999,
        eoi_token_index: int | None = 256_000,
        image_token_index: int | None = 262_144,
        initializer_range: float | None = 0.02,
        tie_word_embeddings: bool | None = True,
        **kwargs,
    ):
        if text_config is None:
            text_config = T5Gemma2TextConfig()
            logger.info("text_config is None, using default T5Gemma2EncoderTextConfig text config.")
        elif isinstance(text_config, dict):
            text_config = T5Gemma2TextConfig(**text_config)

        if isinstance(vision_config, dict):
            vision_config = SiglipVisionConfig(**vision_config)
        elif vision_config is None:
            vision_config = SiglipVisionConfig()
            logger.info("vision_config is None, using default SiglipVisionConfig vision config.")

        self.text_config = text_config
        self.vision_config = vision_config
        self.mm_tokens_per_image = mm_tokens_per_image
        self.boi_token_index = boi_token_index
        self.eoi_token_index = eoi_token_index
        self.image_token_index = image_token_index
        self.initializer_range = initializer_range
        self.tie_word_embeddings = tie_word_embeddings

        super().__init__(**kwargs)


class T5Gemma2DecoderConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`T5Gemma2DecoderModel`]. It is used to instantiate the decoder
    text model portion of the T5Gemma2 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 T5Gemma2Decoder-7B.
    e.g. [google/t5gemma2_text-7b](https://huggingface.co/google/t5gemma2_text-7b)
    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 262208):
            Vocabulary size of the T5Gemma2Decoder model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`T5Gemma2DecoderModel`]
        hidden_size (`int`, *optional*, defaults to 2304):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 9216):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 26):
            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 4):
            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, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is 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 131072):
            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.
        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.
        query_pre_attn_scalar (`float`, *optional*, defaults to 256):
            Scaling factor used on the attention scores
        sliding_window (`int`, *optional*, defaults to 4096):
            In T5Gemma2Decoder, every other layer uses sliding window attention. This is the size of the sliding window.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        final_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the logits.
        attn_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the attention scores.
        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`.
    """

    model_type = "t5gemma2_decoder"
    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.q_norm": "replicated_with_grad_allreduce",
        "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
        "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 | None = 262_208,
        hidden_size: int | None = 2304,
        intermediate_size: int | None = 9216,
        num_hidden_layers: int | None = 26,
        num_attention_heads: int | None = 8,
        num_key_value_heads: int | None = 4,
        head_dim: int | None = 256,
        hidden_activation: str | None = "gelu_pytorch_tanh",
        max_position_embeddings: int | None = 131_072,
        initializer_range: float | None = 0.02,
        rms_norm_eps: int | None = 1e-6,
        use_cache: bool | None = True,
        pad_token_id: int | None = 0,
        eos_token_id: int | None = 1,
        bos_token_id: int | None = 2,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        query_pre_attn_scalar: int | None = 256,
        sliding_window: int | None = 4096,
        layer_types: list[str] | None = None,
        final_logit_softcapping: float | None = None,
        attn_logit_softcapping: float | None = None,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        **kwargs,
    ):
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        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.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.query_pre_attn_scalar = query_pre_attn_scalar
        self.sliding_window = sliding_window
        self.final_logit_softcapping = final_logit_softcapping
        self.attn_logit_softcapping = attn_logit_softcapping
        self.layer_types = layer_types

        # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
        self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6)

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

        self.rope_parameters = rope_parameters
        super().__init__(**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)

        # Set default values if not present
        if self.rope_parameters.get("full_attention") is None:
            self.rope_parameters["full_attention"] = {"rope_type": "default"}
        self.rope_parameters["full_attention"].setdefault(
            "rope_theta", kwargs.pop("rope_theta", self.default_theta["global"])
        )
        if self.rope_parameters.get("sliding_attention") is None:
            self.rope_parameters["sliding_attention"] = {"rope_type": "default"}
        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 T5Gemma2Config(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`T5Gemma2Model`]. It is used to instantiate an T5Gemma2
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to a hypothetical balanced Gemma3 encoder-decoder model.
    e.g. [google/t5gemma-2-270m-270m](https://huggingface.co/google/t5gemma-2-270m-270m)
    Configuration objects inherit from [PreTrainedConfig] and can be used to control the model outputs. Read the
    documentation from [PreTrainedConfig] for more information.

    Args:
        encoder (`Union[T5Gemma2EncoderConfig, dict]`, optional, *optional*):
            Configuration for the encoder.
        decoder (`Union[T5Gemma2DecoderConfig, dict]`, optional, *optional*):
            Configuration for the decoder.
        is_encoder_decoder (bool, optional, *optional*, defaults to `True`):
            Whether the model is used as an encoder/decoder or not.
        dropout_rate (`float`, *optional*, defaults to 0.0):
            The ratio for all dropout layers (following T5).
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for attention.
        classifier_dropout_rate (`float`, *optional*, defaults to 0.0):
            The dropout ratio for classifier (following T5).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        image_token_index (`int`, *optional*, defaults to 256001):
            The image token index to encode the image prompt. Defaults to 256001, which is right after the eoi_token_index.
            Note this is different from Gemma 3.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
            Whether to tie weight embeddings

    ```python
    >>> from transformers import T5Gemma2Config, T5Gemma2Model
    >>> t5gemma2_config = T5Gemma2Config.from_pretrained("google/t5gemma-270m-270m")
    >>> model = T5Gemma2Model(t5gemma2_config)
    ```
    """

    model_type = "t5gemma2"
    keys_to_ignore_at_inference = ["past_key_values"]

    sub_configs = {
        "encoder": T5Gemma2EncoderConfig,
        "decoder": T5Gemma2DecoderConfig,
    }

    attribute_map = {
        "image_token_id": "image_token_index",
        "eoi_token_id": "eoi_token_index",
    }

    def __init__(
        self,
        encoder: T5Gemma2EncoderConfig | dict[str, Any] | None = None,
        decoder: T5Gemma2DecoderConfig | dict[str, Any] | None = None,
        is_encoder_decoder: bool = True,
        dropout_rate: float = 0.0,
        attention_dropout: float = 0.0,
        classifier_dropout_rate: float = 0.0,
        initializer_range: float = 0.02,
        image_token_index: int = 256_001,
        tie_word_embeddings: bool | None = True,
        **kwargs,
    ):
        if isinstance(encoder, dict):
            encoder = T5Gemma2EncoderConfig(**encoder)
        elif encoder is None:
            encoder = T5Gemma2EncoderConfig()
            logger.info("encoder is None, using default T5Gemma2EncoderConfig encoder config.")
        else:
            if not isinstance(encoder, T5Gemma2EncoderConfig):
                raise ValueError(f"{type(encoder)} is not supported.")

        if isinstance(decoder, dict):
            decoder = T5Gemma2DecoderConfig(**decoder)
        elif decoder is None:
            decoder = T5Gemma2DecoderConfig()
            logger.info("decoder is None, using default T5Gemma2DecoderConfig decoder config.")
        else:
            if not isinstance(decoder, T5Gemma2DecoderConfig):
                raise ValueError(f"{type(decoder)} is not supported.")

        if encoder.text_config.hidden_size != decoder.hidden_size:
            raise ValueError(
                "Imbalanced encoder-decoder is not supported in T5Gemma2: "
                f"encoder ({encoder.text_config.hidden_size}) vs decoder ({decoder.hidden_size})."
            )

        if not is_encoder_decoder:
            raise ValueError("T5Gemma2Model only support encoder-decoder modeling.")

        if encoder.text_config.vocab_size != decoder.vocab_size:
            raise ValueError(
                "Imbalanced encoder-decoder vocabulary size is not supported in T5Gemma2: "
                f"encoder ({encoder.text_config.vocab_size}) vs decoder ({decoder.vocab_size})."
            )

        # Encoder.
        encoder.text_config.dropout_rate = dropout_rate
        encoder.text_config.attention_dropout = attention_dropout
        encoder.vision_config.attention_dropout = attention_dropout
        encoder.image_token_index = image_token_index
        self.encoder = encoder

        # Decoder.
        decoder.dropout_rate = dropout_rate
        decoder.attention_dropout = attention_dropout
        self.decoder = decoder

        for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id", "vocab_size"]:
            if special_token_key not in kwargs:
                kwargs[special_token_key] = getattr(decoder, special_token_key)

        self.classifier_dropout_rate = classifier_dropout_rate
        self.initializer_range = initializer_range
        self.eoi_token_index = encoder.eoi_token_index
        self.image_token_index = image_token_index
        self.tie_word_embeddings = tie_word_embeddings

        super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)


__all__ = ["T5Gemma2Config", "T5Gemma2TextConfig", "T5Gemma2EncoderConfig", "T5Gemma2DecoderConfig"]
