from typing import Dict, List, Optional

from pydantic import BaseModel, Field, ValidationError, field_validator


class SchedulerConfig(BaseModel):
    """Scheduler related configuration."""

    enabled: bool = Field(
        default=True, description="Whether the scheduler is enabled"
    )
    interval_minutes: int = Field(
        default=60, ge=1, description="Scheduler interval in minutes"
    )


class BackupConfig(BaseModel):
    """Configuration for automatic backups of application data."""

    enabled: bool = Field(
        default=False, description="Whether backups are enabled"
    )
    path: Optional[str] = Field(
        default="data/backups", description="Path to store backups"
    )
    keep_days: int = Field(
        default=30, ge=0, description="How many days to keep backups"
    )


class LoggingConfig(BaseModel):
    """Logging configuration with basic validation for level."""

    level: str = Field(
        default="INFO", description="Logging level"
    )
    file: Optional[str] = Field(
        default=None, description="Optional file path for log output"
    )
    max_bytes: Optional[int] = Field(
        default=None, ge=0, description="Max bytes per log file for rotation"
    )
    backup_count: Optional[int] = Field(
        default=3, ge=0, description="Number of rotated log files to keep"
    )

    @field_validator("level")
    @classmethod
    def validate_level(cls, v: str) -> str:
        allowed = {"DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"}
        lvl = (v or "").upper()
        if lvl not in allowed:
            raise ValueError(f"invalid logging level: {v}")
        return lvl


class ValidationResult(BaseModel):
    """Result of a configuration validation attempt."""

    valid: bool = Field(..., description="Whether the configuration is valid")
    errors: List[str] = Field(
        default_factory=lambda: [],
        description="List of validation error messages"
    )


class AppConfig(BaseModel):
    """Top-level application configuration model used by the web layer.

    This model intentionally keeps things small and serializable to JSON.
    """

    name: str = Field(default="Aniworld", description="Application name")
    data_dir: str = Field(default="data", description="Base data directory")
    scheduler: SchedulerConfig = Field(
        default_factory=SchedulerConfig
    )
    logging: LoggingConfig = Field(default_factory=LoggingConfig)
    backup: BackupConfig = Field(default_factory=BackupConfig)
    other: Dict[str, object] = Field(
        default_factory=dict, description="Arbitrary other settings"
    )

    def validate_config(self) -> ValidationResult:
        """Perform light-weight validation and return a ValidationResult.

        This method intentionally avoids performing IO (no filesystem checks)
        so it remains fast and side-effect free for unit tests and API use.
        """
        errors: List[str] = []

        # Pydantic field validators already run on construction; re-run a
        # quick check for common constraints and collect messages.
        try:
            # Reconstruct to ensure nested validators are executed
            AppConfig(**self.model_dump())
        except ValidationError as exc:
            for e in exc.errors():
                loc = ".".join(str(x) for x in e.get("loc", []))
                msg = f"{loc}: {e.get('msg')}"
                errors.append(msg)

        # backup.path must be set when backups are enabled
        backup_data = self.model_dump().get("backup", {})
        if backup_data.get("enabled") and not backup_data.get("path"):
            errors.append(
                "backup.path must be set when backups.enabled is true"
            )

        return ValidationResult(valid=(len(errors) == 0), errors=errors)


class ConfigUpdate(BaseModel):
    scheduler: Optional[SchedulerConfig] = None
    logging: Optional[LoggingConfig] = None
    backup: Optional[BackupConfig] = None
    other: Optional[Dict[str, object]] = None

    def apply_to(self, current: AppConfig) -> AppConfig:
        """Return a new AppConfig with updates applied to the current config.

        Performs a shallow merge for `other`.
        """
        data = current.model_dump()
        if self.scheduler is not None:
            data["scheduler"] = self.scheduler.model_dump()
        if self.logging is not None:
            data["logging"] = self.logging.model_dump()
        if self.backup is not None:
            data["backup"] = self.backup.model_dump()
        if self.other is not None:
            merged = dict(current.other or {})
            merged.update(self.other)
            data["other"] = merged
        return AppConfig(**data)