Lightstar/venv/Lib/site-packages/transformers/models/align/configuration_align.py

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

import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
    pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class AlignTextConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a
    ALIGN text 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 text encoder of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are
    copied from BERT.

    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 30522):
            Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`AlignTextModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`AlignTextModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        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`.

    Example:

    ```python
    >>> from transformers import AlignTextConfig, AlignTextModel

    >>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignTextConfig()

    >>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignTextModel(configuration)

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

    model_type = "align_text_model"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.pad_token_id = pad_token_id

    @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 text config dict if we are loading from AlignConfig
        if config_dict.get("model_type") == "align":
            config_dict = config_dict["text_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 AlignVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a
    ALIGN 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 vision encoder of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied
    from EfficientNet (efficientnet-b7)

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

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 600):
            The input image size.
        width_coefficient (`float`, *optional*, defaults to 2.0):
            Scaling coefficient for network width at each stage.
        depth_coefficient (`float`, *optional*, defaults to 3.1):
            Scaling coefficient for network depth at each stage.
        depth_divisor `int`, *optional*, defaults to 8):
            A unit of network width.
        kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
            List of kernel sizes to be used in each block.
        in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
            List of input channel sizes to be used in each block for convolutional layers.
        out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
            List of output channel sizes to be used in each block for convolutional layers.
        depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
            List of block indices with square padding.
        strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
            List of stride sizes to be used in each block for convolutional layers.
        num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
            List of the number of times each block is to repeated.
        expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
            List of scaling coefficient of each block.
        squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
            Squeeze expansion ratio.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
        hidden_dim (`int`, *optional*, defaults to 1280):
            The hidden dimension of the layer before the classification head.
        pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
            Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
            `"max"`]
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        batch_norm_eps (`float`, *optional*, defaults to 1e-3):
            The epsilon used by the batch normalization layers.
        batch_norm_momentum (`float`, *optional*, defaults to 0.99):
            The momentum used by the batch normalization layers.
        drop_connect_rate (`float`, *optional*, defaults to 0.2):
            The drop rate for skip connections.

    Example:

    ```python
    >>> from transformers import AlignVisionConfig, AlignVisionModel

    >>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignVisionConfig()

    >>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignVisionModel(configuration)

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

    model_type = "align_vision_model"

    def __init__(
        self,
        num_channels: int = 3,
        image_size: int = 600,
        width_coefficient: float = 2.0,
        depth_coefficient: float = 3.1,
        depth_divisor: int = 8,
        kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
        in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
        out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
        depthwise_padding: List[int] = [],
        strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
        num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
        expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
        squeeze_expansion_ratio: float = 0.25,
        hidden_act: str = "swish",
        hidden_dim: int = 2560,
        pooling_type: str = "mean",
        initializer_range: float = 0.02,
        batch_norm_eps: float = 0.001,
        batch_norm_momentum: float = 0.99,
        drop_connect_rate: float = 0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.width_coefficient = width_coefficient
        self.depth_coefficient = depth_coefficient
        self.depth_divisor = depth_divisor
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.depthwise_padding = depthwise_padding
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.squeeze_expansion_ratio = squeeze_expansion_ratio
        self.hidden_act = hidden_act
        self.hidden_dim = hidden_dim
        self.pooling_type = pooling_type
        self.initializer_range = initializer_range
        self.batch_norm_eps = batch_norm_eps
        self.batch_norm_momentum = batch_norm_momentum
        self.drop_connect_rate = drop_connect_rate
        self.num_hidden_layers = sum(num_block_repeats) * 4

    @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 AlignConfig
        if config_dict.get("model_type") == "align":
            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 AlignConfig(PretrainedConfig):
    r"""
    [`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to
    instantiate a ALIGN model according to the specified arguments, defining the text model and vision model configs.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture.

    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 (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`AlignTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`AlignVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 640):
            Dimensionality of text and vision projection layers.
        temperature_init_value (`float`, *optional*, defaults to 1.0):
            The initial value of the *temperature* parameter. Default is used as per the original ALIGN implementation.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import AlignConfig, AlignModel

    >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignConfig()

    >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignModel(configuration)

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

    >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
    >>> from transformers import AlignTextConfig, AlignVisionConfig

    >>> # Initializing ALIGN Text and Vision configurations
    >>> config_text = AlignTextConfig()
    >>> config_vision = AlignVisionConfig()

    >>> config = AlignConfig.from_text_vision_configs(config_text, config_vision)
    ```"""

    model_type = "align"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=640,
        temperature_init_value=1.0,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if text_config is None:
            text_config = {}
            logger.info("text_config is None. Initializing the AlignTextConfig with default values.")

        if vision_config is None:
            vision_config = {}
            logger.info("vision_config is None. Initializing the AlignVisionConfig with default values.")

        self.text_config = AlignTextConfig(**text_config)
        self.vision_config = AlignVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.temperature_init_value = temperature_init_value
        self.initializer_range = initializer_range

    @classmethod
    def from_text_vision_configs(cls, text_config: AlignTextConfig, vision_config: AlignVisionConfig, **kwargs):
        r"""
        Instantiate a [`AlignConfig`] (or a derived class) from align text model configuration and align vision model
        configuration.

        Returns:
            [`AlignConfig`]: An instance of a configuration object
        """

        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)


__all__ = ["AlignTextConfig", "AlignVisionConfig", "AlignConfig"]