"""Similarity validator to detect duplicate or highly similar content."""

from typing import Any, Optional

from curriculum_curator.validation.validators.base import BaseValidator


class SimilarityValidator(BaseValidator):
    """Validator that detects if content is too similar to existing content.

    Uses embeddings and cosine similarity to compare content against previously
    seen content. Helps prevent redundant or duplicative information.

    Config options:
        threshold: Similarity threshold (0.0-1.0) above which content is considered duplicate
        model: Optional embedding model to use (defaults to "all-MiniLM-L6-v2")
    """

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

        Args:
            config: Configuration for the validator
        """
        super().__init__(config)
        self.threshold = getattr(config, "threshold", 0.85)
        self.model = getattr(config, "model", "all-MiniLM-L6-v2")
        self.embeddings = {}  # Cache for previously seen content embeddings

    async def validate(
        self, _content: str, _context: Optional[dict[str, Any]] = None
    ) -> dict[str, Any]:
        """Check if content is too similar to existing content.

        Args:
            _content: The content to validate
            _context: Optional context with previous sections to compare against

        Returns:
            dict: Validation result with similarity scores
        """
        # TODO: Implement embedding generation and similarity comparison
        # This would use sentence-transformers or similar library

        # Placeholder implementation
        return {"valid": True, "message": "Similarity validation not yet implemented"}

    def _generate_embedding(self, text: str) -> Any:
        """Generate embedding for text.

        Args:
            text: The text to generate embedding for

        Returns:
            Embedding vector
        """
        # TODO: Implement embedding generation
        # from sentence_transformers import SentenceTransformer
        # model = SentenceTransformer(self.model)
        # return model.encode(text)
        pass

    def _calculate_similarity(self, _embedding1: Any, _embedding2: Any) -> float:
        """Calculate cosine similarity between two embeddings.

        Args:
            _embedding1: First embedding
            _embedding2: Second embedding

        Returns:
            float: Cosine similarity score (0.0-1.0)
        """
        # TODO: Implement cosine similarity calculation
        # from sklearn.metrics.pairwise import cosine_similarity
        # import numpy as np
        # return cosine_similarity(
        #     np.array(embedding1).reshape(1, -1),
        #     np.array(embedding2).reshape(1, -1)
        # )[0][0]
        return 0.0

    def get_remediation_hints(self, validation_result: dict[str, Any]) -> dict[str, Any]:
        """Get hints for remediation if content is too similar.

        Args:
            validation_result: The validation result

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

        return {
            "can_remediate": True,
            "hints": [
                "Rewrite the content to be more distinct",
                "Add more specific details or examples",
                "Change the structure or organization",
                f"Reduce similarity below {self.threshold} threshold",
            ],
        }
