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# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
# Written by Orr Zohar
#
# 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 SmolVLMVisionConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SmolVLMVisionModel`]. It is used to instantiate a
    SmolVLM vision encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
    [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) used in SmolVLM
    [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct).

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

    Args:
        hidden_size (`int`, *optional*, defaults to 1152):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input images.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 32):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        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.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer
    >>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig

    >>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration
    >>> configuration = SmolVLMVisionConfig()

    >>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration
    >>> model = SmolVLMVisionTransformer(configuration)

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

    model_type = "smolvlm_vision"
    base_config_key = "vision_config"

    def __init__(
        self,
        hidden_size=1152,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=16,
        num_channels=3,
        image_size=224,
        patch_size=32,
        hidden_act="gelu_pytorch_tanh",
        layer_norm_eps=1e-6,
        attention_dropout=0.0,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.image_size = image_size
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range


class SmolVLMConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
    SmolVLM 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 model of the SmolVLM
    [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.

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

    Args:
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should cache the key/value pairs of the attention mechanism. Only
            relevant if `config.is_decoder=True`.
        image_token_id (`int`, *optional*, defaults to 128257):
            The id of the "image" token.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether or not to tie the word embeddings with the token embeddings.
        vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
            Custom vision config or dict for the vision tower
        text_config (`PreTrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
            Custom text config or dict for the text model
        scale_factor (`int`, *optional*, defaults to 2):
            The scale factor for the image encoder.
        pad_token_id (`int`, *optional*, defaults to 128002):
            The id of the padding token.

    Example:
    ```python
    >>> from transformers import SmolVLMModel, SmolVLMConfig
    >>> # Initializing configuration
    >>> configuration = SmolVLMConfig()
    >>> # Initializing a model from the configuration
    >>> model = SmolVLMModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "smolvlm"
    sub_configs = {"text_config": AutoConfig, "vision_config": SmolVLMVisionConfig}

    def __init__(
        self,
        use_cache=True,
        image_token_id=128257,
        tie_word_embeddings=False,
        vision_config=None,
        text_config=None,
        scale_factor=2,
        pad_token_id=128_002,
        **kwargs,
    ):
        self.image_token_id = image_token_id
        self.use_cache = use_cache
        self.tie_word_embeddings = tie_word_embeddings

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

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config.get("model_type", "llama")
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            logger.info("text_config is None, using default text config")
            text_config = CONFIG_MAPPING["llama"](
                rms_norm_eps=1e-5,
                pad_token_id=pad_token_id,
                tie_word_embeddings=False,
            )

        self.text_config = text_config
        self.scale_factor = scale_factor

        super().__init__(**kwargs, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings)


__all__ = ["SmolVLMVisionConfig", "SmolVLMConfig"]
