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import math
import warnings

from transformers.configuration_utils import PretrainedConfig


class Mamba2Config(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
    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 MAMBA2
    [state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture.

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


    Args:
        head_dim (`int`, *optional*, defaults to 64):
            Dimension of each head.
        vocab_size (`int`, *optional*, defaults to 32768):
            Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Mamba2Model`].
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the embeddings and hidden states.
        state_size (`int`, *optional*, defaults to 128): shape of the state space latents.
        num_hidden_layers (`int`, *optional*, defaults to 48):
            Number of hidden layers in the model.
        norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon to use in the layer normalization layers.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the beginning of sentence token in the vocabulary.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the end of sentence token in the vocabulary.
        expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
        conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
        n_groups (`int`, *optional*, defaults to 1):
            Number of groups for the evolution matrices of mamba 2.
        use_bias (`bool`, *optional*, defaults to `False`):
            Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
        use_conv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the convolution layer of the mixer block.
        hidden_act (`str`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        residual_in_fp32 (`bool`, *optional*, defaults to `True`):
            Whether or not residuals should be in `float32`.
            If set to `False` residuals will keep the same `dtype` as the rest of the model
        time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
            Rank of the discretization projection matrix.
            `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
        time_step_min (`float`, *optional*, defaults to 0.001):
            Minimum `time_step` used to bound `dt_proj.bias`.
        time_step_max (`float`, *optional*, defaults to 0.1):
            Maximum `time_step` used to bound `dt_proj.bias`.
        time_step_floor (`float`, *optional*, defaults to 0.0001):
            Minimum clamping value of the `dt_proj.bias` layer initialization.
        time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
            Accepted range of time step values.
        rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
            Whether or not to rescale `out_proj` weights when initializing.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the cache should be used.
        rms_norm (`bool`, *optional*, defaults to `True`):
            Whether to use RMS norm or not.
        chunk_size (`int`, *optional*, defaults to 256):
            Size of the chunks that will comprise the sequence.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie word embeddings or not.
    """

    model_type = "mamba2"

    def __init__(
        self,
        head_dim: int = 64,
        vocab_size: int = 32000,
        hidden_size: int = 2048,
        state_size: int = 128,
        num_hidden_layers: int = 48,
        norm_eps: float = 1e-5,
        pad_token_id: int = 0,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        expand: int = 2,
        conv_kernel: int = 4,
        n_groups: int = 1,
        use_bias: bool = False,
        use_conv_bias: bool = True,
        hidden_act: str = "silu",
        initializer_range: float = 0.02,
        residual_in_fp32: bool = True,
        time_step_rank: str = "auto",
        time_step_min: float = 0.001,
        time_step_max: float = 0.1,
        time_step_floor: float = 1e-4,
        time_step_limit=(0.0, float("inf")),
        rescale_prenorm_residual: bool = True,
        use_cache: bool = True,
        rms_norm: bool = True,
        chunk_size: int = 256,
        fuse_norm: bool = True,
        fuse_cross_entropy: bool = True,
        fuse_linear_cross_entropy: bool = False,
        use_l2warp: bool = False,
        tie_word_embeddings: bool = False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.state_size = state_size
        self.num_hidden_layers = num_hidden_layers
        self.norm_eps = norm_eps
        self.conv_kernel = conv_kernel
        self.expand = expand

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.use_bias = use_bias
        self.use_conv_bias = use_conv_bias
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.time_step_rank = (
            math.ceil(self.hidden_size / 16)
            if time_step_rank == "auto"
            else time_step_rank
        )
        self.time_step_min = time_step_min
        self.time_step_max = time_step_max
        self.time_step_floor = time_step_floor
        self.rescale_prenorm_residual = rescale_prenorm_residual
        self.residual_in_fp32 = residual_in_fp32
        self.use_cache = use_cache
        self.n_groups = n_groups
        self.head_dim = head_dim
        self.num_heads = int(self.expand * self.hidden_size / self.head_dim)
        self.rms_norm = rms_norm
        self.state_size = state_size
        self.chunk_size = chunk_size
        self.time_step_limit = time_step_limit
        self.fuse_norm = fuse_norm
        self.fuse_cross_entropy = fuse_cross_entropy
        self.fuse_linear_cross_entropy = fuse_linear_cross_entropy
        self.use_l2warp = use_l2warp
        self.tie_word_embeddings = tie_word_embeddings

        if fuse_cross_entropy and fuse_linear_cross_entropy:
            raise ValueError(
                "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.",
            )
        if fuse_linear_cross_entropy:
            warnings.warn(
                "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency "
                "at the potential cost of reduced precision. "
                "If you observe issues like loss divergence, consider disabling this setting.",
            )

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
