from dataclasses import dataclass
from typing import Any

from transformers.configuration_utils import PretrainedConfig

from sglang.srt.configs.mamba_utils import (
    Mamba2CacheParams,
    Mamba2StateShape,
    mamba2_state_dtype,
)
from sglang.srt.runtime_context import get_parallel


@dataclass
class JetBlockConfig:
    mode: str
    expand_v: float
    num_heads: int
    head_dim: int
    norm_eps: str
    conv_size: int
    dconv_generator_reduction: int
    dconv_implementation: str


class JetNemotronConfig(PretrainedConfig):
    model_type: str = "jet_nemotron"

    efficient_attention_config: dict[str, dict[str, Any]] = None
    hidden_act: str = None
    hidden_size: int = None
    initializer_range: float = None
    intermediate_size: int = None
    layer_types: list[str] = None
    max_position_embeddings: int = None
    num_attention_heads: int = None
    num_key_value_heads: int = None
    rms_norm_eps: float = None
    rope_scaling: None = None
    rope_theta: float = None

    @property
    def full_attention_layer_ids(self) -> list[int]:
        return [
            idx
            for idx, layer_type in enumerate(self.layer_types)
            if layer_type in ("attn", "swa")
        ]

    @property
    def linear_layer_ids(self) -> list[int]:
        return [
            idx
            for idx, layer_type in enumerate(self.layer_types)
            if layer_type == "jet"
        ]

    @property
    def mamba2_cache_params(self) -> Mamba2CacheParams:

        jet_block_config = JetBlockConfig(**self.efficient_attention_config["jet"])

        num_heads = jet_block_config.num_heads
        head_k_dim = jet_block_config.head_dim
        head_v_dim = int(head_k_dim * jet_block_config.expand_v)
        total_v_dim = num_heads * head_v_dim

        shape = Mamba2StateShape.create(
            tp_world_size=get_parallel().attn_tp_size,
            intermediate_size=total_v_dim,
            n_groups=num_heads,
            num_heads=num_heads,
            head_dim=head_v_dim,
            state_size=head_k_dim,
            conv_kernel=jet_block_config.conv_size,
        )

        return Mamba2CacheParams(
            shape=shape, layers=self.linear_layer_ids, dtype=mamba2_state_dtype(self)
        )