"""
Typed configuration schema for the unified application configuration.

This module defines the normalized in-memory structure used by all services.
"""

from __future__ import annotations

from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple


@dataclass(frozen=True)
class IndexConfig:
    """Deprecated compatibility shape for legacy diagnostics/tests."""

    name: str
    label: str
    fields: List[str]
    boost: float = 1.0
    example: Optional[str] = None


@dataclass(frozen=True)
class QueryConfig:
    """Configuration for query processing."""

    supported_languages: List[str] = field(default_factory=lambda: ["zh", "en"])
    default_language: str = "en"
    enable_text_embedding: bool = True
    enable_query_rewrite: bool = True
    rewrite_dictionary: Dict[str, str] = field(default_factory=dict)
    text_embedding_field: Optional[str] = "title_embedding"
    image_embedding_field: Optional[str] = None
    source_fields: Optional[List[str]] = None
    # 文本向量 KNN 与多模态（图片）向量 KNN 各自 boost；未在 YAML 中写时由 loader 用 legacy knn_boost 回填
    knn_text_boost: float = 20.0
    knn_image_boost: float = 20.0
    knn_text_k: int = 120
    knn_text_num_candidates: int = 400
    knn_text_k_long: int = 160
    knn_text_num_candidates_long: int = 500
    knn_image_k: int = 120
    knn_image_num_candidates: int = 400
    multilingual_fields: List[str] = field(
        default_factory=lambda: []
    )
    shared_fields: List[str] = field(
        default_factory=lambda: []
    )
    core_multilingual_fields: List[str] = field(
        default_factory=lambda: []
    )
    base_minimum_should_match: str = "70%"
    translation_minimum_should_match: str = "70%"
    translation_boost: float = 0.4
    tie_breaker_base_query: float = 0.9
    best_fields: Dict[str, float] = field(default_factory=dict)
    best_fields_boost: float = 2.0
    phrase_fields: Dict[str, float] = field(default_factory=dict)
    phrase_match_boost: float = 3.0
    zh_to_en_model: str = "opus-mt-zh-en"
    en_to_zh_model: str = "opus-mt-en-zh"
    default_translation_model: str = "nllb-200-distilled-600m"
    # 检测语种不在租户 index_languages（无可直接命中的多语字段）时使用；None 表示与上一组同模型。
    zh_to_en_model_source_not_in_index: Optional[str] = None
    en_to_zh_model_source_not_in_index: Optional[str] = None
    default_translation_model_source_not_in_index: Optional[str] = None
    # 查询阶段：翻译与向量生成并发提交后，共用同一等待预算（毫秒）。
    # 检测语言已在租户 index_languages 内：偏快返回，预算较短。
    # 检测语言不在 index_languages 内：翻译对召回更关键，预算较长。
    translation_embedding_wait_budget_ms_source_in_index: int = 80
    translation_embedding_wait_budget_ms_source_not_in_index: int = 200
    style_intent_enabled: bool = True
    style_intent_selected_sku_boost: float = 1.2
    style_intent_terms: Dict[str, List[Dict[str, List[str]]]] = field(default_factory=dict)
    style_intent_dimension_aliases: Dict[str, List[str]] = field(default_factory=dict)
    product_title_exclusion_enabled: bool = True
    product_title_exclusion_rules: List[Dict[str, List[str]]] = field(default_factory=list)


@dataclass(frozen=True)
class SPUConfig:
    """SPU aggregation/search configuration."""

    enabled: bool = False
    spu_field: Optional[str] = None
    inner_hits_size: int = 3
    searchable_option_dimensions: List[str] = field(
        default_factory=lambda: ["option1", "option2", "option3"]
    )


@dataclass(frozen=True)
class FunctionScoreConfig:
    """Function score configuration."""

    score_mode: str = "sum"
    boost_mode: str = "multiply"
    functions: List[Dict[str, Any]] = field(default_factory=list)


@dataclass(frozen=True)
class RerankFusionConfig:
    """
    Multiplicative fusion: fused = Π (max(score_i, 0) + bias_i) ** exponent_i
    for es / rerank / fine / text / knn terms respectively.
    """

    es_bias: float = 0.1
    es_exponent: float = 0.0
    rerank_bias: float = 0.00001
    rerank_exponent: float = 1.0
    text_bias: float = 0.1
    text_exponent: float = 0.35
    knn_text_weight: float = 1.0
    knn_image_weight: float = 1.0
    knn_tie_breaker: float = 0.0
    knn_bias: float = 0.6
    knn_exponent: float = 0.2
    #: Optional additive floor for the weighted text KNN term.
    #: Falls back to knn_bias when omitted in config loading.
    knn_text_bias: float = 0.6
    #: Optional extra multiplicative term on weighted text KNN.
    #: Uses knn_text_bias as the additive floor.
    knn_text_exponent: float = 0.0
    #: Optional additive floor for the weighted image KNN term.
    #: Falls back to knn_bias when omitted in config loading.
    knn_image_bias: float = 0.6
    #: Optional extra multiplicative term on weighted image KNN.
    #: Uses knn_image_bias as the additive floor.
    knn_image_exponent: float = 0.0
    fine_bias: float = 0.00001
    fine_exponent: float = 1.0
    #: 翻译子句 named query 分数相对原文 base_query 的权重（加权后再与原文做 dismax 融合）
    text_translation_weight: float = 0.8


@dataclass(frozen=True)
class CoarseRankFusionConfig:
    """
    Multiplicative fusion without model score:
    fused = (max(es, 0) + es_bias) ** es_exponent
            * (max(text, 0) + text_bias) ** text_exponent
            * (max(knn, 0) + knn_bias) ** knn_exponent
    """

    es_bias: float = 0.1
    es_exponent: float = 0.0
    text_bias: float = 0.1
    text_exponent: float = 0.35
    knn_text_weight: float = 1.0
    knn_image_weight: float = 1.0
    knn_tie_breaker: float = 0.0
    knn_bias: float = 0.6
    knn_exponent: float = 0.2
    #: Optional additive floor for the weighted text KNN term.
    #: Falls back to knn_bias when omitted in config loading.
    knn_text_bias: float = 0.6
    #: Optional extra multiplicative term on weighted text KNN.
    #: Uses knn_text_bias as the additive floor.
    knn_text_exponent: float = 0.0
    #: Optional additive floor for the weighted image KNN term.
    #: Falls back to knn_bias when omitted in config loading.
    knn_image_bias: float = 0.6
    #: Optional extra multiplicative term on weighted image KNN.
    #: Uses knn_image_bias as the additive floor.
    knn_image_exponent: float = 0.0
    #: 翻译子句 named query 分数相对原文 base_query 的权重（加权后再与原文做 dismax 融合）
    text_translation_weight: float = 0.8


@dataclass(frozen=True)
class CoarseRankConfig:
    """Search-time coarse ranking configuration."""

    enabled: bool = True
    input_window: int = 700
    output_window: int = 240
    fusion: CoarseRankFusionConfig = field(default_factory=CoarseRankFusionConfig)


@dataclass(frozen=True)
class FineRankConfig:
    """Search-time lightweight rerank configuration."""

    enabled: bool = True
    input_window: int = 240
    output_window: int = 80
    timeout_sec: float = 10.0
    rerank_query_template: str = "{query}"
    rerank_doc_template: str = "{title}"
    service_profile: Optional[str] = "fine"


@dataclass(frozen=True)
class RerankConfig:
    """Search-time rerank configuration."""

    enabled: bool = True
    rerank_window: int = 384
    exact_knn_rescore_enabled: bool = False
    #: topN exact vector scoring window; <=0 means "follow rerank_window"
    exact_knn_rescore_window: int = 0
    timeout_sec: float = 15.0
    weight_es: float = 0.4
    weight_ai: float = 0.6
    rerank_query_template: str = "{query}"
    rerank_doc_template: str = "{title}"
    service_profile: Optional[str] = None
    fusion: RerankFusionConfig = field(default_factory=RerankFusionConfig)


@dataclass(frozen=True)
class SearchConfig:
    """Search behavior configuration shared by backend and indexer."""

    field_boosts: Dict[str, float]
    indexes: List[IndexConfig] = field(default_factory=list)
    query_config: QueryConfig = field(default_factory=QueryConfig)
    function_score: FunctionScoreConfig = field(default_factory=FunctionScoreConfig)
    coarse_rank: CoarseRankConfig = field(default_factory=CoarseRankConfig)
    fine_rank: FineRankConfig = field(default_factory=FineRankConfig)
    rerank: RerankConfig = field(default_factory=RerankConfig)
    spu_config: SPUConfig = field(default_factory=SPUConfig)
    es_index_name: str = "search_products"
    es_settings: Dict[str, Any] = field(default_factory=dict)


@dataclass(frozen=True)
class TranslationServiceConfig:
    """Translator service configuration."""

    endpoint: str
    timeout_sec: float
    default_model: str
    default_scene: str
    cache: Dict[str, Any]
    capabilities: Dict[str, Dict[str, Any]]

    def as_dict(self) -> Dict[str, Any]:
        return {
            "service_url": self.endpoint,
            "timeout_sec": self.timeout_sec,
            "default_model": self.default_model,
            "default_scene": self.default_scene,
            "cache": self.cache,
            "capabilities": self.capabilities,
        }


@dataclass(frozen=True)
class EmbeddingServiceConfig:
    """Embedding service configuration."""

    provider: str
    providers: Dict[str, Any]
    backend: str
    backends: Dict[str, Dict[str, Any]]
    image_backend: str
    image_backends: Dict[str, Dict[str, Any]]

    def get_provider_config(self) -> Dict[str, Any]:
        return dict(self.providers.get(self.provider, {}) or {})

    def get_backend_config(self) -> Dict[str, Any]:
        return dict(self.backends.get(self.backend, {}) or {})

    def get_image_backend_config(self) -> Dict[str, Any]:
        return dict(self.image_backends.get(self.image_backend, {}) or {})


@dataclass(frozen=True)
class RerankServiceInstanceConfig:
    """One named reranker service instance."""

    host: str = "0.0.0.0"
    port: int = 6007
    backend: str = "qwen3_vllm_score"
    runtime_dir: Optional[str] = None
    base_url: Optional[str] = None
    service_url: Optional[str] = None


@dataclass(frozen=True)
class RerankServiceConfig:
    """Reranker service configuration."""

    provider: str
    providers: Dict[str, Any]
    default_instance: str
    instances: Dict[str, RerankServiceInstanceConfig]
    backends: Dict[str, Dict[str, Any]]
    request: Dict[str, Any]

    def get_provider_config(self) -> Dict[str, Any]:
        return dict(self.providers.get(self.provider, {}) or {})

    def get_instance(self, name: Optional[str] = None) -> RerankServiceInstanceConfig:
        instance_name = str(name or self.default_instance).strip() or self.default_instance
        instance = self.instances.get(instance_name)
        if instance is None:
            raise KeyError(f"Unknown rerank service instance: {instance_name!r}")
        return instance

    def get_backend_config(self, name: Optional[str] = None) -> Dict[str, Any]:
        instance = self.get_instance(name)
        return dict(self.backends.get(instance.backend, {}) or {})


@dataclass(frozen=True)
class ServicesConfig:
    """All service-level configuration."""

    translation: TranslationServiceConfig
    embedding: EmbeddingServiceConfig
    rerank: RerankServiceConfig


@dataclass(frozen=True)
class TenantCatalogConfig:
    """Tenant catalog configuration."""

    default: Dict[str, Any]
    tenants: Dict[str, Dict[str, Any]]

    def get_raw(self) -> Dict[str, Any]:
        return {
            "default": dict(self.default),
            "tenants": {str(key): dict(value) for key, value in self.tenants.items()},
        }


@dataclass(frozen=True)
class ElasticsearchSettings:
    host: str = "http://localhost:9200"
    username: Optional[str] = None
    password: Optional[str] = None


@dataclass(frozen=True)
class RedisSettings:
    host: str = "localhost"
    port: int = 6479
    snapshot_db: int = 0
    password: Optional[str] = None
    socket_timeout: int = 1
    socket_connect_timeout: int = 1
    retry_on_timeout: bool = False
    cache_expire_days: int = 720
    embedding_cache_prefix: str = "embedding"
    anchor_cache_prefix: str = "product_anchors"
    anchor_cache_expire_days: int = 30


@dataclass(frozen=True)
class DatabaseSettings:
    host: Optional[str] = None
    port: int = 3306
    database: Optional[str] = None
    username: Optional[str] = None
    password: Optional[str] = None


@dataclass(frozen=True)
class SecretsConfig:
    dashscope_api_key: Optional[str] = None
    deepl_auth_key: Optional[str] = None


@dataclass(frozen=True)
class InfrastructureConfig:
    elasticsearch: ElasticsearchSettings
    redis: RedisSettings
    database: DatabaseSettings
    secrets: SecretsConfig


@dataclass(frozen=True)
class ProductEnrichConfig:
    """Configuration for LLM-based product content understanding (enrich-content)."""

    max_workers: int = 40


@dataclass(frozen=True)
class RuntimeConfig:
    environment: str = "prod"
    index_namespace: str = ""
    api_host: str = "0.0.0.0"
    api_port: int = 6002
    indexer_host: str = "0.0.0.0"
    indexer_port: int = 6004
    embedding_host: str = "0.0.0.0"
    embedding_port: int = 6005
    embedding_text_port: int = 6005
    embedding_image_port: int = 6008
    translator_host: str = "0.0.0.0"
    translator_port: int = 6006
    reranker_host: str = "0.0.0.0"
    reranker_port: int = 6007


@dataclass(frozen=True)
class AssetsConfig:
    query_rewrite_dictionary_path: Path


@dataclass(frozen=True)
class SearchEvaluationDatasetConfig:
    """Named query-set definition for the search evaluation framework."""

    dataset_id: str
    display_name: str
    description: str
    query_file: Path
    tenant_id: str
    language: str
    enabled: bool = True


@dataclass(frozen=True)
class SearchEvaluationConfig:
    """Offline / web UI search evaluation (YAML: ``search_evaluation``)."""

    artifact_root: Path
    queries_file: Path
    default_dataset_id: str
    datasets: Tuple[SearchEvaluationDatasetConfig, ...]
    eval_log_dir: Path
    default_tenant_id: str
    search_base_url: str
    web_host: str
    web_port: int
    judge_model: str
    judge_enable_thinking: bool
    judge_dashscope_batch: bool
    intent_model: str
    intent_enable_thinking: bool
    judge_batch_completion_window: str
    judge_batch_poll_interval_sec: float
    build_search_depth: int
    build_rerank_depth: int
    annotate_search_top_k: int
    annotate_rerank_top_k: int
    batch_top_k: int
    audit_top_k: int
    audit_limit_suspicious: int
    default_language: str
    search_recall_top_k: int
    rerank_high_threshold: float
    rerank_high_skip_count: int
    rebuild_llm_batch_size: int
    rebuild_min_llm_batches: int
    rebuild_max_llm_batches: int
    rebuild_irrelevant_stop_ratio: float
    rebuild_irrel_low_combined_stop_ratio: float
    rebuild_irrelevant_stop_streak: int


@dataclass(frozen=True)
class ConfigMetadata:
    loaded_files: Tuple[str, ...]
    config_hash: str
    deprecated_keys: Tuple[str, ...] = field(default_factory=tuple)


@dataclass(frozen=True)
class AppConfig:
    """Root application configuration."""

    runtime: RuntimeConfig
    infrastructure: InfrastructureConfig
    product_enrich: ProductEnrichConfig
    search: SearchConfig
    services: ServicesConfig
    tenants: TenantCatalogConfig
    assets: AssetsConfig
    search_evaluation: SearchEvaluationConfig
    metadata: ConfigMetadata

    def sanitized_dict(self) -> Dict[str, Any]:
        data = asdict(self)
        data["infrastructure"]["elasticsearch"]["password"] = _mask_secret(
            data["infrastructure"]["elasticsearch"].get("password")
        )
        data["infrastructure"]["database"]["password"] = _mask_secret(
            data["infrastructure"]["database"].get("password")
        )
        data["infrastructure"]["redis"]["password"] = _mask_secret(
            data["infrastructure"]["redis"].get("password")
        )
        data["infrastructure"]["secrets"]["dashscope_api_key"] = _mask_secret(
            data["infrastructure"]["secrets"].get("dashscope_api_key")
        )
        data["infrastructure"]["secrets"]["deepl_auth_key"] = _mask_secret(
            data["infrastructure"]["secrets"].get("deepl_auth_key")
        )
        return data


def _mask_secret(value: Optional[str]) -> Optional[str]:
    if not value:
        return value
    return "***"