# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from typing import Any

import torch
import torch.nn as nn

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.tasks import GenerationTask
from vllm.v1.core.sched.output import NewRequestData
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.worker.gpu.input_batch import InputBatch
from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
from vllm.v1.worker.gpu.states import RequestState
from vllm.v1.worker.utils import AttentionGroup


class ModelSpecificAttnMetadata:
    """Base class for model-specific attention metadata."""

    def get_extra_common_attn_kwargs(
        self,
        kv_cache_group_id: int,
        num_reqs: int,
    ) -> dict[str, Any]:
        return {}

    def get_extra_attn_kwargs(
        self,
        attn_metadata_builder: Any,
        num_reqs: int,
    ) -> dict[str, Any]:
        return {}


class ModelState(ABC):
    @abstractmethod
    def __init__(
        self,
        vllm_config: VllmConfig,
        model: nn.Module,
        encoder_cache: EncoderCache | None,
        device: torch.device,
    ) -> None:
        raise NotImplementedError

    @abstractmethod
    def get_supported_generation_tasks(self) -> tuple[GenerationTask, ...]:
        raise NotImplementedError

    def add_request(self, req_index: int, new_req_data: NewRequestData) -> None:
        return None

    def apply_staged_writes(self) -> None:
        return None

    def postprocess_state(
        self,
        input_batch: InputBatch,
        num_sampled: torch.Tensor,
    ) -> None:
        return None

    @abstractmethod
    def get_mm_embeddings(
        self,
        scheduled_encoder_inputs: dict[str, list[int]],
        input_batch: InputBatch,
        req_states: RequestState,
    ) -> torch.Tensor | None:
        raise NotImplementedError

    @abstractmethod
    def prepare_inputs(
        self, input_batch: InputBatch, req_states: RequestState
    ) -> dict[str, Any]:
        raise NotImplementedError

    @abstractmethod
    def prepare_dummy_inputs(self, num_reqs: int, num_tokens: int) -> dict[str, Any]:
        raise NotImplementedError

    @abstractmethod
    def prepare_attn(
        self,
        input_batch: InputBatch,
        cudagraph_mode: CUDAGraphMode,
        block_tables: tuple[torch.Tensor, ...],
        slot_mappings: torch.Tensor,
        attn_groups: list[list[AttentionGroup]],
        kv_cache_config: KVCacheConfig,
        for_capture: bool = False,
    ) -> dict[str, Any]:
        raise NotImplementedError