"""GreenLeaf Law Embed — quantization modules.

Provides native int8 and binary quantization for embedding vectors,
enabling efficient storage and fast similarity search without
requiring post-hoc compression.

Quantizers use straight-through estimation: the forward pass applies
hard quantization while gradients flow through the soft (continuous)
approximation during training.
"""

import torch
import numpy as np
from typing import Literal
from sentence_transformers.models import Module


class BaseQuantizer(torch.nn.Module):
    """Base quantizer with straight-through gradient estimation.

    During inference (hard=True), applies discrete quantization.
    During training, gradients bypass the discretization step so
    the model can learn through the quantization bottleneck.
    """

    def __init__(self, hard: bool = True):
        """
        Args:
            hard: If True, output is discretized. If False, output is
                  the soft continuous approximation.
        """
        super().__init__()
        self._hard = hard

    def _hard_quantize(self, x, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError

    def _soft_quantize(self, x, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError

    def forward(self, x, *args, **kwargs) -> torch.Tensor:
        soft = self._soft_quantize(x, *args, **kwargs)

        if not self._hard:
            return soft

        # Straight-through estimator: forward uses hard quantization,
        # backward passes gradients through the soft path unchanged.
        return (
            self._hard_quantize(x, *args, **kwargs).detach()
            + soft - soft.detach()
        )


class Int8EmbeddingQuantizer(BaseQuantizer):
    """Quantizes embeddings to signed 8-bit integers via tanh scaling.

    The soft path applies tanh to squash values into [-1, 1], then
    the hard path scales to [-128, 127] and rounds to integers.
    """

    def __init__(self, hard: bool = True):
        super().__init__(hard=hard)
        self.qmin = -128
        self.qmax = 127

    def _soft_quantize(self, x, *args, **kwargs):
        return torch.tanh(x)

    def _hard_quantize(self, x, *args, **kwargs):
        soft = self._soft_quantize(x)
        int_x = torch.round(soft * self.qmax)
        return torch.clamp(int_x, self.qmin, self.qmax)


class BinaryEmbeddingQuantizer(BaseQuantizer):
    """Quantizes embeddings to {-1, +1} via sign function.

    The soft path uses scaled tanh as a differentiable approximation
    to the sign function. The hard path applies the actual sign.
    """

    def __init__(self, hard: bool = True, scale: float = 1.0):
        super().__init__(hard)
        self._scale = scale

    def _soft_quantize(self, x, *args, **kwargs):
        return torch.tanh(self._scale * x)

    def _hard_quantize(self, x, *args, **kwargs):
        return torch.where(x >= 0, 1.0, -1.0)


class PackedBinaryEncoder:
    """Packs binary {-1, +1} embeddings into uint8 bit-arrays.

    Each embedding dimension maps to 1 bit, reducing storage by 32x
    compared to float32. Useful for large-scale retrieval with
    Hamming distance.
    """

    def __call__(self, x: torch.Tensor) -> torch.Tensor:
        bits = np.where(x.cpu().numpy() >= 0, True, False)
        packed = np.packbits(bits, axis=-1)
        return torch.from_numpy(packed).to(x.device)


class FlexibleQuantizer(Module):
    """Sentence-transformers module that applies optional quantization.

    Default behavior: pass through raw float embeddings (bf16/fp32).
    Quantization is applied only when explicitly requested.

    Supported modes:
      - None: raw float embeddings (default, no quantization)
      - "int8": signed 8-bit integer embeddings
      - "binary": {-1, +1} binary embeddings
      - "ubinary": packed binary as uint8 bit-arrays
    """

    def __init__(self):
        super().__init__()
        self._int8_quantizer = Int8EmbeddingQuantizer()
        self._binary_quantizer = BinaryEmbeddingQuantizer()
        self._packed_binary_encoder = PackedBinaryEncoder()

    def forward(
        self,
        features: dict[str, torch.Tensor],
        quantization: Literal["int8", "binary", "ubinary"] | None = None,
        **kwargs,
    ) -> dict[str, torch.Tensor]:
        if quantization is None:
            # Default: return raw float embeddings, no quantization
            return features
        elif quantization == "int8":
            features["sentence_embedding"] = self._int8_quantizer(
                features["sentence_embedding"]
            )
        elif quantization == "binary":
            features["sentence_embedding"] = self._binary_quantizer(
                features["sentence_embedding"]
            )
        elif quantization == "ubinary":
            features["sentence_embedding"] = self._packed_binary_encoder(
                features["sentence_embedding"]
            )
        else:
            raise ValueError(
                f"Unknown quantization mode: '{quantization}'. "
                f"Supported modes: 'int8', 'binary', 'ubinary'."
            )
        return features

    @classmethod
    def load(
        cls,
        model_name_or_path: str,
        subfolder: str = "",
        token: bool | str | None = None,
        cache_folder: str | None = None,
        revision: str | None = None,
        local_files_only: bool = False,
        **kwargs,
    ):
        return cls()

    def save(self, output_path: str, *args, **kwargs) -> None:
        return