from dataclasses import dataclass
from typing import Tuple

from transformers import PretrainedConfig


@dataclass
class sCTConfig(PretrainedConfig):  # noqa: N801
    model_type = "sCT"

    def __init__(self, **kwargs):  # type: ignore
        self.alphabet_size = kwargs.get("alphabet_size", 7)
        self.pad_token_id = kwargs.get("pad_token_id", 5)
        self.mask_token_id = kwargs.get("mask_token_id", 6)
        self.cell_len = kwargs.get("cell_len", 19968)

        self.num_downsamples = kwargs.get("num_downsamples", 8)
        self.attention_heads = kwargs.get("attention_heads", 16)
        self.key_size = kwargs.get("key_size", None)
        self.token_embed_dim = kwargs.get("token_embed_dim", 16)

        self.embed_dim = kwargs.get("embed_dim", 1024)
        self.ffn_embed_dim = kwargs.get("ffn_embed_dim", 2048)
        self.num_layers = kwargs.get("num_layers", 4)
        self.layer_norm_eps = kwargs.get("layer_norm_eps", 1e-5)
        self.interpolation_method = kwargs.get("interpolation_method", "nearest")

        # bad hack to satisfy cellnt_celltype_annotation.py:312
        self.max_positions: int = kwargs.get("max_positions", 20480)
        self.num_cells: int = kwargs.get("num_cells", 50)
        self.num_hidden_layers_head: int = kwargs.get("num_hidden_layers_head", 1)

        self.use_skip_connection: bool = kwargs.get("use_skip_connection", True)

        # logging
        self.use_gradient_checkpointing: bool = False

        # return
        self.embeddings_layers_to_save: Tuple[int, ...] = kwargs.get(
            "embeddings_layers_to_save", ()
        )
        self.attention_maps_to_save: list[tuple[int, int]] = kwargs.get(
            "attention_maps_to_save", []
        )

        # Spatial info configuration
        self.use_spatial_information: bool = kwargs.get(
            "use_spatial_information", False
        )
        self.num_scales: int = kwargs.get("num_scales", 10)
        self.sigma_min: float = kwargs.get("sigma_min", 1.0)
        self.sigma_max: float = kwargs.get("sigma_max", 10.0)

        super().__init__(**kwargs)

        def __post_init__(self) -> None:  # type: ignore # noqa: N807
            """
            Checks that the given values are compatible.
            """
            if self.key_size is None:
                if not self.embed_dim % self.attention_heads == 0:
                    raise ValueError(
                        f"When no key size is provided, the embedding dimension"
                        f"should be divisible by the number of heads, however "
                        f"provided embedding dimension is {self.embed_dim} and "
                        f"the number of heads is {self.attention_heads}."
                    )
                self.key_size = self.embed_dim // self.attention_heads