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from __future__ import annotations

from dataclasses import dataclass

import torch

from tokenspeed.runtime.configs.model_config import ModelConfig
from tokenspeed.runtime.layers.attention.configs.base import (
    BaseAttnConfig,
    resolve_dtype,
)
from tokenspeed.runtime.layers.attention.kv_cache.base import BaseTokenToKVPool
from tokenspeed.runtime.utils.server_args import ServerArgs


@dataclass
class MLAConfig(BaseAttnConfig):
    kv_lora_rank: int
    qk_nope_head_dim: int
    qk_rope_head_dim: int
    v_head_dim: int
    scaling: float
    kv_cache_dim: int

    @classmethod
    def generate(
        cls, server_args: ServerArgs, model_config: ModelConfig, is_draft: bool = False
    ):
        kwargs = {}
        if server_args.speculative_algorithm is not None:
            kwargs.update(
                speculative_num_steps=server_args.speculative_num_steps,
                speculative_num_draft_tokens=server_args.speculative_num_draft_tokens,
            )
        return cls(
            device=server_args.device,
            context_len=model_config.context_len,
            backend_name=(
                server_args.attention_backend
                if not is_draft
                else server_args.drafter_attention_backend
            ),
            num_attention_heads=model_config.num_attention_heads,
            num_kv_heads=model_config.num_key_value_heads,
            head_dim=model_config.head_dim,
            attn_tp_size=server_args.attn_tp_size or server_args.mapping.attn.tp_size,
            dtype=model_config.dtype,
            kv_cache_dtype=resolve_dtype(server_args.kv_cache_dtype),
            page_size=server_args.block_size,
            max_graph_bs=server_args.max_cudagraph_capture_size,
            max_bs=server_args.max_num_seqs
            // (server_args.data_parallel_size or server_args.mapping.attn.dp_size),
            kv_cache_quant_method=server_args.kv_cache_quant_method,
            is_draft=is_draft,
            kv_lora_rank=model_config.kv_lora_rank,
            qk_nope_head_dim=model_config.qk_nope_head_dim,
            qk_rope_head_dim=model_config.qk_rope_head_dim,
            v_head_dim=model_config.v_head_dim,
            scaling=model_config.scaling,
            kv_cache_dim=model_config.kv_lora_rank + model_config.qk_rope_head_dim,
            **kwargs,
        )

    def cache_cell_size(self) -> int:
        if self.kv_cache_quant_method == "per_token_head":
            cell_size = (
                self.kv_lora_rank * torch._utils._element_size(self.kv_cache_dtype)
                + self.qk_rope_head_dim * torch._utils._element_size(self.dtype)
                + 1 * torch._utils._element_size(torch.float32)
            )
        else:
            cell_size = (
                self.kv_lora_rank + self.qk_rope_head_dim
            ) * torch._utils._element_size(self.kv_cache_dtype)
        return cell_size

    def create_pool(
        self,
        num_layers: int,
        max_total_num_tokens: int,
        rank: int,
        enable_memory_saver: bool,
    ) -> BaseTokenToKVPool:
        from tokenspeed.runtime.layers.attention.kv_cache.mla import MLATokenToKVPool

        return MLATokenToKVPool(
            size=max_total_num_tokens,
            dtype=self.kv_cache_dtype,
            model_dtype=self.dtype,
            quant_method=self.kv_cache_quant_method,
            kv_lora_rank=self.kv_lora_rank,
            qk_rope_head_dim=self.qk_rope_head_dim,
            layer_num=num_layers,
            device=self.device,
            enable_memory_saver=enable_memory_saver,
            max_batch_size=self.max_bs,
            max_context_len=self.context_len,
            page_size=self.page_size,
            rank=rank,
        )