Lightstar/venv/Lib/site-packages/transformers/models/idefics3/configuration_idefics3.py

# coding=utf-8
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"""Idefics3 model configuration"""

import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)


class Idefics3VisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a
    Idefics3 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-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics3 model
    [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3).

    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.
        intializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation for initializing all weight matrices in the model.

    Example:

    ```python
    >>> from transformers.models.idefics3.modeling_idefics3 import Idefics3VisionTransformer
    >>> from transformers.models.idefics3.configuration_idefics3 import Idefics3VisionConfig

    >>> # Initializing a Idefics3VisionConfig with google/siglip-base-patch16-224 style configuration
    >>> configuration = Idefics3VisionConfig()

    >>> # Initializing a Idefics3VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration
    >>> model = Idefics3VisionTransformer(configuration)

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

    model_type = "idefics3"

    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

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        # get the vision config dict if we are loading from Idefics3Config
        if config_dict.get("model_type") == "idefics3":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class Idefics3Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a
    Idefics3 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 Idefics3
    [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) 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 Idefics3Model, Idefics3Config
    >>> # Initializing configuration
    >>> configuration = Idefics3Config()
    >>> # Initializing a model from the configuration
    >>> model = Idefics3Model(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "idefics3"
    is_composition = True

    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 = Idefics3VisionConfig()
            logger.info("vision_config is None, using default vision config")
        elif isinstance(vision_config, dict):
            self.vision_config = Idefics3VisionConfig(**vision_config)
        elif isinstance(vision_config, Idefics3VisionConfig):
            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 "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, tie_word_embeddings=tie_word_embeddings)