# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import os

from typing import Any, Dict, List, Optional, Type
from .triton_config import get_nearest_config, get_attention_mla_configs, get_config, get_attention_mla_configs_json

import torch

from vllm.attention.backends.abstract import (AttentionType,
                                              is_quantized_kv_cache)
from vllm.attention.backends.mla.common import (MLACommonBackend,
                                                MLACommonImpl,
                                                MLACommonMetadata)
from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
import vllm.envs as envs

from vllm.logger import init_logger
logger = init_logger(__name__)


class TritonMLABackend(MLACommonBackend):

    @staticmethod
    def get_name() -> str:
        return "TRITON_MLA"

    @staticmethod
    def get_impl_cls() -> Type["TritonMLAImpl"]:
        return TritonMLAImpl


class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):

    def __init__(
            self,
            num_heads: int,
            head_size: int,
            scale: float,
            num_kv_heads: int,
            alibi_slopes: Optional[List[float]],
            sliding_window: Optional[int],
            kv_cache_dtype: str,
            blocksparse_params: Optional[Dict[str, Any]],
            logits_soft_cap: Optional[float],
            attn_type: str,
            kv_sharing_target_layer_name: Optional[str],
            # MLA Specific Arguments
            **mla_args) -> None:
        super().__init__(num_heads, head_size, scale, num_kv_heads,
                         alibi_slopes, sliding_window, kv_cache_dtype,
                         blocksparse_params, logits_soft_cap, attn_type,
                         kv_sharing_target_layer_name, **mla_args)

        unsupported_features = [
            alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
        ]
        if any(unsupported_features):
            raise NotImplementedError(
                "TritonMLAImpl does not support one of the following: "
                "alibi_slopes, sliding_window, blocksparse_params, "
                "logits_soft_cap")

        if attn_type != AttentionType.DECODER:
            raise NotImplementedError("Encoder self-attention and "
                                      "encoder/decoder cross-attention "
                                      "are not implemented for "
                                      "TritonMLAImpl")
            
        if envs.VLLM_USE_TRITON_OPT_MLA:
            self.attn_configs = get_attention_mla_configs_json(self.num_heads, 1, self.kv_lora_rank + self.qk_rope_head_dim, self.kv_lora_rank, "fp16")

        if is_quantized_kv_cache(self.kv_cache_dtype):
            raise NotImplementedError(
                "TritonMLA with FP8 KV cache not yet supported")

    def _forward_decode(
        self,
        q_nope: torch.Tensor,
        q_pe: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
    ) -> torch.Tensor:
        assert kv_c_and_k_pe_cache.numel() > 0

        decode_meta = attn_metadata.decode_metadata
        assert decode_meta is not None
        B = q_nope.shape[0]

        q = torch.cat([q_nope, q_pe], dim=-1)
        o = torch.zeros(B,
                        self.num_heads,
                        self.kv_lora_rank,
                        dtype=q.dtype,
                        device=q.device)

        num_kv_splits = 4  # TODO: heuristic

        # TODO(lucas) Allocate ahead of time
        attn_logits = torch.empty(
            (
                B,
                self.num_heads,
                num_kv_splits,
                # NOTE(lucas) idk why the +1 is here but sglang has it so we
                # just mirror that
                self.kv_lora_rank + 1,
            ),
            dtype=torch.float32,
            device=q.device,
        )

        # Add a head dim of 1
        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
        kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
        PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
        
        # TODO
        max_seq_len = torch.max(decode_meta.seq_lens_tensor).item()
        if os.environ.get('PA_MATCH_USE_MEAN_SEQ') == '1':
            match_seq_len = int((decode_meta.seq_lens_tensor.sum()/ max(1, B)).item())
        else:
            match_seq_len = max_seq_len
        
        if envs.VLLM_USE_TRITON_OPT_MLA:
            best_config = self.attn_configs[min(self.attn_configs.keys(), key=lambda x: abs(int(x) - match_seq_len))]
                
        # Run MQA
        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
                             decode_meta.block_tables,
                             decode_meta.seq_lens_tensor, attn_logits,
                             num_kv_splits, self.scale, best_config, PAGE_SIZE)

        return self._v_up_proj(o)