from sentence_transformers import SentenceTransformer
from dataclasses import dataclass

from binary_shield.comparison import compute_similarity, hamming_distance
from binary_shield.embedding import extract_embedding
from binary_shield.privacy import apply_randomized_response
from binary_shield.quantization import BinaryPackedEmbedding, binary_quantize


@dataclass
class BinaryFingerprint:
    fingerprint: BinaryPackedEmbedding
    epsilon: float | None


@dataclass
class ComparisonResult:
    hamming_distance: int
    similarity: float
    is_match: bool


class BinaryShield:
    def __init__(
        self,
        model_name: str = "all-MiniLM-L6-v2",
        epsilon: float | None = None,
    ) -> None:
        self.model = SentenceTransformer(model_name)
        self.epsilon = epsilon

    def generate_fingerprint(self, text: str) -> BinaryFingerprint:
        embedding = extract_embedding(text, self.model)
        bin_embedding = binary_quantize(embedding)
        if self.epsilon is not None:
            bin_embedding = apply_randomized_response(bin_embedding, self.epsilon)
        return BinaryFingerprint(
            fingerprint=bin_embedding,
            epsilon=self.epsilon,
        )

    @staticmethod
    def compare(
        fp1: BinaryFingerprint,
        fp2: BinaryFingerprint,
        threshold: float = 0.8,
    ) -> ComparisonResult:
        dist = hamming_distance(fp1.fingerprint, fp2.fingerprint)
        sim = compute_similarity(fp1.fingerprint, fp2.fingerprint)
        return ComparisonResult(
            hamming_distance=dist,
            similarity=sim,
            is_match=sim >= threshold,
        )