# coding=utf-8
# 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig


logger = logging.get_logger(__name__)


class ShieldGemma2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ShieldGemma2ForImageClassification`]. It is used to instantiate an
    ShieldGemma2ForImageClassification according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the shieldgemma-2-4b-it.

    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[ShieldGemma2TextConfig, 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.


    Example:

    ```python
    >>> from transformers import ShieldGemma2ForConditionalGeneration, ShieldGemma2Config, SiglipVisionConfig, ShieldGemma2TextConfig

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

    >>> # Initializing a ShieldGemma2 Text config
    >>> text_config = ShieldGemma2TextConfig()

    >>> # Initializing a ShieldGemma2 gemma-3-4b style configuration
    >>> configuration = ShieldGemma2Config(vision_config, text_config)

    >>> # Initializing a model from the gemma-3-4b style configuration
    >>> model = ShieldGemma2TextConfig(configuration)

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

    model_type = "shieldgemma2"
    attribute_map = {
        "image_token_id": "image_token_index",
        "boi_token_id": "boi_token_index",
        "eoi_token_id": "eoi_token_index",
    }
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        mm_tokens_per_image: int = 256,
        boi_token_index: int = 255_999,
        eoi_token_index: int = 256_000,
        image_token_index: int = 262_144,
        initializer_range: float = 0.02,
        **kwargs,
    ):
        if isinstance(vision_config, dict):
            vision_config["model_type"] = (
                vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model"
            )
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        elif vision_config is None:
            vision_config = CONFIG_MAPPING["siglip_vision_model"]()

        self.vision_config = vision_config

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gemma3_text"
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["gemma3_text"]()

        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

        super().__init__(**kwargs)


__all__ = ["ShieldGemma2Config"]

# coding=utf-8
# 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig


logger = logging.get_logger(__name__)


class ShieldGemma2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ShieldGemma2ForImageClassification`]. It is used to instantiate an
    ShieldGemma2ForImageClassification according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the shieldgemma-2-4b-it.

    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[ShieldGemma2TextConfig, 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.


    Example:

    ```python
    >>> from transformers import ShieldGemma2ForConditionalGeneration, ShieldGemma2Config, SiglipVisionConfig, ShieldGemma2TextConfig

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

    >>> # Initializing a ShieldGemma2 Text config
    >>> text_config = ShieldGemma2TextConfig()

    >>> # Initializing a ShieldGemma2 gemma-3-4b style configuration
    >>> configuration = ShieldGemma2Config(vision_config, text_config)

    >>> # Initializing a model from the gemma-3-4b style configuration
    >>> model = ShieldGemma2TextConfig(configuration)

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

    model_type = "shieldgemma2"
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        mm_tokens_per_image: int = 256,
        boi_token_index: int = 255_999,
        eoi_token_index: int = 256_000,
        image_token_index: int = 262_144,
        initializer_range: float = 0.02,
        **kwargs,
    ):
        if isinstance(vision_config, dict):
            vision_config["model_type"] = (
                vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model"
            )
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        elif vision_config is None:
            vision_config = CONFIG_MAPPING["siglip_vision_model"]()

        self.vision_config = vision_config

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gemma3_text"
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["gemma3_text"]()

        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

        super().__init__(**kwargs)


__all__ = ["ShieldGemma2Config"]
