from dependency_injector import containers, providers

from config import Config
from services.model.mlx_model import MlxModelService
from services.model.mlx_chat_model import MlxChatModel
from services.chat.history_service import HistoryService
from services.chat.chat_service import ChatService
from services.chat.compact_service import CompactService
from services.db.mysql_service import DatabaseService
from services.db.conversation_repository import ConversationRepository
from services.db.user_profile_repository import UserProfileRepository
from services.db.feedback_repository import FeedbackRepository
from services.ui.cli_service import CliUiService
from services.events.event_bus import EventBus
from services.events.handlers import StreamTokenHandler, StreamEndHandler
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_qdrant import FastEmbedSparse
from services.rag.ingestion_service import IngestionService
from services.rag.rerank_service import RerankService
from services.rag.retriever_service import RetrieverService
from services.agent.agent_service import AgentService


class Container(containers.DeclarativeContainer):
    config = providers.Singleton(Config)

    event_bus = providers.Singleton(EventBus)

    model_service = providers.Singleton(
        MlxModelService,
        model_id=providers.Callable(lambda c: c.model_id, config),
    )

    # LangGraph 에이전트용 BaseChatModel (Phase 1)
    chat_model = providers.Singleton(
        MlxChatModel,
        model_id=providers.Callable(lambda c: c.model_id, config),
        max_tokens=providers.Callable(lambda c: c.max_tokens, config),
        enable_thinking=providers.Callable(lambda c: c.enable_thinking, config),
    )

    compact_service = providers.Singleton(
        CompactService,
        model=model_service,
    )

    db_service = providers.Singleton(
        DatabaseService,
        host=providers.Callable(lambda c: c.db_host, config),
        port=providers.Callable(lambda c: c.db_port, config),
        db=providers.Callable(lambda c: c.db_name, config),
        user=providers.Callable(lambda c: c.db_user, config),
        password=providers.Callable(lambda c: c.db_password, config),
    )

    conversation_repository = providers.Singleton(
        ConversationRepository,
        db=db_service,
    )

    user_profile_repository = providers.Singleton(
        UserProfileRepository,
        db=db_service,
    )

    feedback_repository = providers.Singleton(
        FeedbackRepository,
        db=db_service,
    )

    history_service = providers.Factory(
        HistoryService,
        system_prompt=providers.Callable(lambda c: c.system_prompt, config),
        max_turns=providers.Callable(lambda c: c.max_history_turns, config),
        compact_threshold=providers.Callable(lambda c: c.compact_threshold, config),
        repository=conversation_repository,
        compact_service=compact_service,
    )

    chat_service = providers.Factory(
        ChatService,
        model=model_service,
        history=history_service,
        event_bus=event_bus,
        max_tokens=providers.Callable(lambda c: c.max_tokens, config),
    )

    ui_service = providers.Singleton(CliUiService)

    stream_token_handler = providers.Singleton(StreamTokenHandler)
    stream_end_handler = providers.Singleton(StreamEndHandler)

    # Phase 2 — RAG 파이프라인
    embeddings = providers.Singleton(
        HuggingFaceEmbeddings,
        model_name=providers.Callable(lambda c: c.embedding_model_id, config),
        model_kwargs=providers.Callable(lambda c: {"device": c.embedding_device}, config),
    )

    reranker = providers.Callable(
        lambda c: RerankService(c.reranker_model_id) if c.reranker_enabled else None,
        config,
    )

    sparse_embeddings = providers.Singleton(
        lambda c: FastEmbedSparse(model_name=c.sparse_model_id) if c.hybrid_search_enabled else None,
        config,
    )

    ingestion_service = providers.Singleton(
        IngestionService,
        embeddings=embeddings,
        qdrant_url=providers.Callable(lambda c: c.qdrant_url, config),
        collection_name=providers.Callable(lambda c: c.qdrant_collection, config),
        breakpoint_threshold_type=providers.Callable(
            lambda c: c.semantic_breakpoint_threshold_type, config
        ),
        buffer_size=providers.Callable(lambda c: c.semantic_buffer_size, config),
        sparse_embeddings=sparse_embeddings,
    )

    retriever_service = providers.Singleton(
        RetrieverService,
        embeddings=embeddings,
        qdrant_url=providers.Callable(lambda c: c.qdrant_url, config),
        collection_name=providers.Callable(lambda c: c.qdrant_collection, config),
        top_k=providers.Callable(lambda c: c.rag_top_k, config),
        reranker=reranker,
        rerank_fetch_k=providers.Callable(lambda c: c.reranker_fetch_k, config),
        sparse_embeddings=sparse_embeddings,
    )

    # Phase 3 — LangGraph Agent
    agent_service = providers.Singleton(
        AgentService,
        chat_model=chat_model,
        retriever_service=retriever_service,
        system_prompt=providers.Callable(lambda c: c.system_prompt, config),
        rag_verbose=providers.Callable(lambda c: c.rag_verbose, config),
        rag_show_sources=providers.Callable(lambda c: c.rag_show_sources, config),
        langgraph_verbose=providers.Callable(lambda c: c.langgraph_verbose, config),
        think_verbose=providers.Callable(lambda c: c.think_verbose, config),
        user_profile_repository=user_profile_repository,
        conversation_repository=conversation_repository,
    )