import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""


import nobuco
from nobuco import ChannelOrder, ChannelOrderingStrategy
from nobuco.layers.weight import WeightLayer

import torch
from torch import nn

import tensorflow as tf
from tensorflow.lite.python.lite import TFLiteConverter
import keras


class DummyModel(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, qkv, w, b):
        x = torch.nn.functional._in_projection_packed(qkv, qkv, qkv, w, b)
        return x


model = DummyModel()

qkv = torch.randn(50, 1, 1024)
w = torch.randn(3072, 1024)
b = torch.randn(3072)

keras_model = nobuco.pytorch_to_keras(
    model,
    args=[qkv, w, b],
    # inputs_channel_order=ChannelOrder.PYTORCH,
)

model_path = 'projection'
keras_model.save(model_path + '.h5')
print('Model saved')

custom_objects = {'WeightLayer': WeightLayer}

keras_model_restored = keras.models.load_model(model_path + '.h5', custom_objects=custom_objects)
print('Model loaded')

converter = TFLiteConverter.from_keras_model_file(model_path + '.h5', custom_objects=custom_objects)
converter.target_ops = [tf.lite.OpsSet.SELECT_TF_OPS, tf.lite.OpsSet.TFLITE_BUILTINS]
tflite_model = converter.convert()
with open(model_path + '.tflite', 'wb') as f:
    f.write(tflite_model)
