from pydantic import field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=".env",
        extra="ignore",
    )

    google_project_id: str
    google_location: str = "us-central1"

    # Runtime de atendimento do product. Mantido separado do runtime de geração
    # de código do admin_app, que usa AdminSettings próprios.
    atendimento_model_name: str | None = None
    atendimento_bundle_model_name: str | None = None
    atendimento_temperature: float = 0
    atendimento_max_output_tokens: int = 768

    # Aliases legados mantidos por compatibilidade enquanto o runtime de
    # atendimento migra para o perfil explícito de atendimento.
    vertex_model_name: str = "gemini-2.5-pro"
    vertex_bundle_model_name: str = "gemini-2.5-pro"

    # Tools database (MySQL)
    db_host: str = "127.0.0.1"
    db_port: int = 3306
    db_user: str = "root"
    db_password: str = ""
    db_name: str = "orquestrador_mock"
    db_cloud_sql_connection_name: str | None = None

    # Mock database (MySQL) for fictitious business data
    mock_db_host: str = "127.0.0.1"
    mock_db_port: int = 3306
    mock_db_user: str = "root"
    mock_db_password: str = ""
    mock_db_name: str = "orquestrador_mock"
    mock_db_cloud_sql_connection_name: str | None = None
    mock_seed_enabled: bool = True
    auto_seed_tools: bool = True
    auto_seed_mock: bool = True

    environment: str = "production"
    debug: bool = False

    # Cloud SQL (legacy Postgres var kept only for backward compatibility in deploy scripts)
    cloud_sql_connection_name: str | None = None

    # Parametros de rede legados (mantidos por compatibilidade)
    run_vpc_connector: str | None = None
    run_vpc_egress: str = "private-ranges-only"

    # Telegram satellite
    telegram_bot_token: str | None = None
    telegram_polling_timeout: int = 30
    telegram_request_timeout: int = 45

    # Conversation state backend
    conversation_state_backend: str = "memory"
    conversation_state_ttl_minutes: int = 60

    # Redis conversation state
    redis_url: str = "redis://127.0.0.1:6379/0"
    redis_key_prefix: str = "orquestrador"
    redis_socket_timeout_seconds: int = 5

    # External integrations
    integrations_enabled: bool = False
    integration_sync_delivery_enabled: bool = True
    brevo_api_key: str | None = None
    brevo_base_url: str = "https://api.brevo.com/v3"
    brevo_sender_email: str | None = None
    brevo_sender_name: str = "Orquestrador"
    brevo_request_timeout_seconds: int = 10

    @field_validator("debug", mode="before")
    @classmethod
    def parse_debug_aliases(cls, value):
        if isinstance(value, str):
            normalized = value.strip().lower()
            if normalized in {"debug", "development", "dev"}:
                return True
            if normalized in {"release", "production", "prod"}:
                return False
        return value

    @field_validator("environment", "conversation_state_backend", mode="before")
    @classmethod
    def normalize_runtime_text_settings(cls, value):
        if isinstance(value, str):
            return value.strip().lower()
        return value

    @field_validator("atendimento_model_name", "atendimento_bundle_model_name", mode="before")
    @classmethod
    def normalize_optional_model_names(cls, value):
        if isinstance(value, str):
            stripped = value.strip()
            return stripped or None
        return value

    @field_validator("vertex_model_name", "vertex_bundle_model_name", mode="before")
    @classmethod
    def normalize_required_model_names(cls, value):
        if isinstance(value, str):
            return value.strip()
        return value

    @field_validator("atendimento_temperature")
    @classmethod
    def validate_atendimento_temperature(cls, value: float) -> float:
        if value < 0 or value > 2:
            raise ValueError("atendimento_temperature must be between 0 and 2")
        return value

    @field_validator("atendimento_max_output_tokens")
    @classmethod
    def validate_atendimento_max_output_tokens(cls, value: int) -> int:
        if value < 128:
            raise ValueError("atendimento_max_output_tokens must be >= 128")
        return value

    def resolve_atendimento_model_name(self) -> str:
        configured = str(self.atendimento_model_name or "").strip()
        if configured:
            return configured
        return str(self.vertex_model_name or "").strip()

    def resolve_atendimento_bundle_model_name(self) -> str:
        configured = str(self.atendimento_bundle_model_name or "").strip()
        if configured:
            return configured
        legacy = str(self.vertex_bundle_model_name or "").strip()
        if legacy:
            return legacy
        return self.resolve_atendimento_model_name()

    def build_atendimento_generation_config(self) -> dict[str, int | float]:
        return {
            "temperature": float(self.atendimento_temperature),
            "max_output_tokens": int(self.atendimento_max_output_tokens),
        }


settings = Settings()