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

from ....configuration_utils import PreTrainedConfig
from ....utils import logging


logger = logging.get_logger(__name__)


class EfficientFormerConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`EfficientFormerModel`]. It is used to
    instantiate an EfficientFormer 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 EfficientFormer
    [snap-research/efficientformer-l1](https://huggingface.co/snap-research/efficientformer-l1) architecture.

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

    Args:
        depths (`List(int)`, *optional*, defaults to `[3, 2, 6, 4]`)
            Depth of each stage.
        hidden_sizes (`List(int)`, *optional*, defaults to `[48, 96, 224, 448]`)
            Dimensionality of each stage.
        downsamples (`List(bool)`, *optional*, defaults to `[True, True, True, True]`)
            Whether or not to downsample inputs between two stages.
        dim (`int`, *optional*, defaults to 448):
            Number of channels in Meta3D layers
        key_dim (`int`, *optional*, defaults to 32):
            The size of the key in meta3D block.
        attention_ratio (`int`, *optional*, defaults to 4):
            Ratio of the dimension of the query and value to the dimension of the key in MSHA block
        resolution (`int`, *optional*, defaults to 7)
            Size of each patch
        num_hidden_layers (`int`, *optional*, defaults to 5):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the 3D MetaBlock.
        mlp_expansion_ratio (`int`, *optional*, defaults to 4):
            Ratio of size of the hidden dimensionality of an MLP to the dimensionality of its input.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        pool_size (`int`, *optional*, defaults to 3):
            Kernel size of pooling layers.
        downsample_patch_size (`int`, *optional*, defaults to 3):
            The size of patches in downsampling layers.
        downsample_stride (`int`, *optional*, defaults to 2):
            The stride of convolution kernels in downsampling layers.
        downsample_pad (`int`, *optional*, defaults to 1):
            Padding in downsampling layers.
        drop_path_rate (`int`, *optional*, defaults to 0):
            Rate at which to increase dropout probability in DropPath.
        num_meta3d_blocks (`int`, *optional*, defaults to 1):
            The number of 3D MetaBlocks in the last stage.
        distillation (`bool`, *optional*, defaults to `True`):
            Whether to add a distillation head.
        use_layer_scale (`bool`, *optional*, defaults to `True`):
            Whether to scale outputs from token mixers.
        layer_scale_init_value (`float`, *optional*, defaults to 1e-5):
            Factor by which outputs from token mixers are scaled.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. 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-12):
            The epsilon used by the layer normalization layers.
        image_size (`int`, *optional*, defaults to `224`):
            The size (resolution) of each image.

    Example:

    ```python
    >>> from transformers import EfficientFormerConfig, EfficientFormerModel

    >>> # Initializing a EfficientFormer efficientformer-l1 style configuration
    >>> configuration = EfficientFormerConfig()

    >>> # Initializing a EfficientFormerModel (with random weights) from the efficientformer-l3 style configuration
    >>> model = EfficientFormerModel(configuration)

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

    model_type = "efficientformer"

    def __init__(
        self,
        depths: list[int] = [3, 2, 6, 4],
        hidden_sizes: list[int] = [48, 96, 224, 448],
        downsamples: list[bool] = [True, True, True, True],
        dim: int = 448,
        key_dim: int = 32,
        attention_ratio: int = 4,
        resolution: int = 7,
        num_hidden_layers: int = 5,
        num_attention_heads: int = 8,
        mlp_expansion_ratio: int = 4,
        hidden_dropout_prob: float = 0.0,
        patch_size: int = 16,
        num_channels: int = 3,
        pool_size: int = 3,
        downsample_patch_size: int = 3,
        downsample_stride: int = 2,
        downsample_pad: int = 1,
        drop_path_rate: float = 0.0,
        num_meta3d_blocks: int = 1,
        distillation: bool = True,
        use_layer_scale: bool = True,
        layer_scale_init_value: float = 1e-5,
        hidden_act: str = "gelu",
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        image_size: int = 224,
        batch_norm_eps: float = 1e-05,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.hidden_sizes = hidden_sizes
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.depths = depths
        self.mlp_expansion_ratio = mlp_expansion_ratio
        self.downsamples = downsamples
        self.dim = dim
        self.key_dim = key_dim
        self.attention_ratio = attention_ratio
        self.resolution = resolution
        self.pool_size = pool_size
        self.downsample_patch_size = downsample_patch_size
        self.downsample_stride = downsample_stride
        self.downsample_pad = downsample_pad
        self.drop_path_rate = drop_path_rate
        self.num_meta3d_blocks = num_meta3d_blocks
        self.distillation = distillation
        self.use_layer_scale = use_layer_scale
        self.layer_scale_init_value = layer_scale_init_value
        self.image_size = image_size
        self.batch_norm_eps = batch_norm_eps


__all__ = [
    "EfficientFormerConfig",
]
