Lightstar/venv/Lib/site-packages/transformers/models/deprecated/van/configuration_van.py

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

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


logger = logging.get_logger(__name__)


class VanConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`VanModel`]. It is used to instantiate a VAN 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 VAN
    [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-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:
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`):
            Patch size to use in each stage's embedding layer.
        strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
            Stride size to use in each stage's embedding layer to downsample the input.
        hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to `[3, 3, 12, 3]`):
            Depth (number of layers) for each stage.
        mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
            The expansion ratio for mlp layer at each stage.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in each layer. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        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-06):
            The epsilon used by the layer normalization layers.
        layer_scale_init_value (`float`, *optional*, defaults to 0.01):
            The initial value for layer scaling.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The dropout probability for stochastic depth.
        dropout_rate (`float`, *optional*, defaults to 0.0):
            The dropout probability for dropout.

    Example:
    ```python
    >>> from transformers import VanModel, VanConfig

    >>> # Initializing a VAN van-base style configuration
    >>> configuration = VanConfig()
    >>> # Initializing a model from the van-base style configuration
    >>> model = VanModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "van"

    def __init__(
        self,
        image_size=224,
        num_channels=3,
        patch_sizes=[7, 3, 3, 3],
        strides=[4, 2, 2, 2],
        hidden_sizes=[64, 128, 320, 512],
        depths=[3, 3, 12, 3],
        mlp_ratios=[8, 8, 4, 4],
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-6,
        layer_scale_init_value=1e-2,
        drop_path_rate=0.0,
        dropout_rate=0.0,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.image_size = image_size
        self.num_channels = num_channels
        self.patch_sizes = patch_sizes
        self.strides = strides
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.mlp_ratios = mlp_ratios
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.layer_scale_init_value = layer_scale_init_value
        self.drop_path_rate = drop_path_rate
        self.dropout_rate = dropout_rate