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""" Neighborhood Attention Transformer model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


logger = logging.get_logger(__name__)

NAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class NatConfig(BackboneConfigMixin, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat 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 Nat
    [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) architecture.

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

    Args:
        patch_size (`int`, *optional*, defaults to 4):
            The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embed_dim (`int`, *optional*, defaults to 64):
            Dimensionality of patch embedding.
        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
            Number of layers in each level of the encoder.
        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
            Number of attention heads in each layer of the Transformer encoder.
        kernel_size (`int`, *optional*, defaults to 7):
            Neighborhood Attention kernel size.
        mlp_ratio (`float`, *optional*, defaults to 3.0):
            Ratio of MLP hidden dimensionality to embedding dimensionality.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not a learnable bias should be added to the queries, keys and values.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder. 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.
        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
            The initial value for the layer scale. Disabled if <=0.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage.

    Example:

    ```python
    >>> from transformers import NatConfig, NatModel

    >>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
    >>> configuration = NatConfig()

    >>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
    >>> model = NatModel(configuration)

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

    attribute_map = {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(
        self,
        patch_size=4,
        num_channels=3,
        embed_dim=64,
        depths=[3, 4, 6, 5],
        num_heads=[2, 4, 8, 16],
        kernel_size=7,
        mlp_ratio=3.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        layer_scale_init_value=0.0,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_layers = len(depths)
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
        self.layer_scale_init_value = layer_scale_init_value
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
        )
