from __future__ import annotations

import json
import os

import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn


class LayerNorm(nn.Module):
    def __init__(self, dimension: int):
        super().__init__()
        self.dimension = dimension
        self.norm = nn.LayerNorm(dimension)

    def forward(self, features: dict[str, Tensor]):
        features["sentence_embedding"] = self.norm(features["sentence_embedding"])
        return features

    def get_sentence_embedding_dimension(self):
        return self.dimension

    def save(self, output_path, safe_serialization: bool = True) -> None:
        with open(os.path.join(output_path, "config.json"), "w") as fOut:
            json.dump({"dimension": self.dimension}, fOut, indent=2)

        if safe_serialization:
            save_safetensors_model(self, os.path.join(output_path, "model.safetensors"))
        else:
            torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin"))

    @staticmethod
    def load(input_path):
        with open(os.path.join(input_path, "config.json")) as fIn:
            config = json.load(fIn)

        model = LayerNorm(**config)
        if os.path.exists(os.path.join(input_path, "model.safetensors")):
            load_safetensors_model(model, os.path.join(input_path, "model.safetensors"))
        else:
            model.load_state_dict(
                torch.load(os.path.join(input_path, "pytorch_model.bin"), map_location=torch.device("cpu"))
            )
        return model

from __future__ import annotations

import json
import os

import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn


class LayerNorm(nn.Module):
    def __init__(self, dimension: int):
        super(LayerNorm, self).__init__()
        self.dimension = dimension
        self.norm = nn.LayerNorm(dimension)

    def forward(self, features: dict[str, Tensor]):
        features["sentence_embedding"] = self.norm(features["sentence_embedding"])
        return features

    def get_sentence_embedding_dimension(self):
        return self.dimension

    def save(self, output_path, safe_serialization: bool = True) -> None:
        with open(os.path.join(output_path, "config.json"), "w") as fOut:
            json.dump({"dimension": self.dimension}, fOut, indent=2)

        if safe_serialization:
            save_safetensors_model(self, os.path.join(output_path, "model.safetensors"))
        else:
            torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin"))

    @staticmethod
    def load(input_path):
        with open(os.path.join(input_path, "config.json")) as fIn:
            config = json.load(fIn)

        model = LayerNorm(**config)
        if os.path.exists(os.path.join(input_path, "model.safetensors")):
            load_safetensors_model(model, os.path.join(input_path, "model.safetensors"))
        else:
            model.load_state_dict(
                torch.load(os.path.join(input_path, "pytorch_model.bin"), map_location=torch.device("cpu"))
            )
        return model
