"""Readability validator to ensure content meets readability standards."""

from typing import Any, Optional

from curriculum_curator.validation.validators.base import BaseValidator


class ReadabilityValidator(BaseValidator):
    """Validator that ensures content meets readability standards.

    Checks various readability metrics including sentence length, Flesch Reading Ease,
    and other readability indices. Helps ensure content is appropriate for the target audience.

    Config options:
        max_avg_sentence_length: Maximum average sentence length
        min_flesch_reading_ease: Minimum Flesch Reading Ease score (0-100, higher is more readable)
    """

    def __init__(self, config: Any):
        """Initialize the readability validator.

        Args:
            config: Configuration for the validator
        """
        super().__init__(config)
        self.max_avg_sentence_length = getattr(config, "max_avg_sentence_length", 25)
        self.min_flesch_reading_ease = getattr(config, "min_flesch_reading_ease", 60.0)

    async def validate(
        self, content: str, _context: Optional[dict[str, Any]] = None
    ) -> dict[str, Any]:
        """Check if content meets readability standards.

        Args:
            content: The content to validate
            _context: Optional context with additional requirements

        Returns:
            dict: Validation result with readability metrics
        """
        # Calculate metrics
        metrics = self._calculate_metrics(content)

        issues = []

        # Check sentence length
        if metrics["avg_sentence_length"] > self.max_avg_sentence_length:
            issues.append(
                {
                    "issue": "sentence_length",
                    "message": f"Average sentence length ({metrics['avg_sentence_length']:.1f}) exceeds maximum ({self.max_avg_sentence_length})",
                    "value": metrics["avg_sentence_length"],
                    "threshold": self.max_avg_sentence_length,
                }
            )

        # Check Flesch reading ease
        if metrics["flesch_reading_ease"] < self.min_flesch_reading_ease:
            issues.append(
                {
                    "issue": "reading_ease",
                    "message": f"Flesch reading ease score ({metrics['flesch_reading_ease']:.1f}) below minimum ({self.min_flesch_reading_ease})",
                    "value": metrics["flesch_reading_ease"],
                    "threshold": self.min_flesch_reading_ease,
                }
            )

        if issues:
            return {
                "valid": False,
                "reason": "Readability issues detected",
                "issues": issues,
                "metrics": metrics,
            }

        return {"valid": True, "metrics": metrics}

    def _calculate_metrics(self, _content: str) -> dict[str, Any]:
        """Calculate readability metrics for the content.

        Args:
            _content: The content to analyze

        Returns:
            dict: Dictionary of readability metrics
        """
        # TODO: Implement with textstat or similar library
        # This is a placeholder implementation

        return {
            "sentence_count": 0,
            "word_count": 0,
            "avg_sentence_length": 0,
            "flesch_reading_ease": 100,  # Default to highest readability
            "gunning_fog": 0,
            "smog_index": 0,
            "automated_readability_index": 0,
        }

    def get_remediation_hints(self, validation_result: dict[str, Any]) -> dict[str, Any]:
        """Get hints for remediation if readability validation fails.

        Args:
            validation_result: The validation result

        Returns:
            dict: Remediation hints
        """
        if validation_result.get("valid", True):
            return {"can_remediate": False, "hints": []}

        hints = []
        issues = validation_result.get("issues", [])

        for issue in issues:
            if issue.get("issue") == "sentence_length":
                hints.extend(
                    [
                        "Break long sentences into shorter ones",
                        "Use simpler sentence structures",
                        "Aim for sentences with 15-20 words on average",
                    ]
                )

            if issue.get("issue") == "reading_ease":
                hints.extend(
                    [
                        "Use simpler vocabulary",
                        "Reduce sentence complexity",
                        "Avoid passive voice",
                        "Use more common words instead of specialized jargon",
                    ]
                )

        return {"can_remediate": True, "hints": hints}
