# to run this script, you need torch 1.13.1 and torchvision 0.14.1

import numpy as np
import onnx
import torch
import torchvision
import os

from models.spoter_embedding_model import SPOTER_EMBEDDINGS

# set parameters of the model
model_name = 'fingerspelling_embedding_model'

# load PyTorch model from .pth file

device = torch.device("cpu")
# if torch.cuda.is_available():
#     device = torch.device("cuda")

CHECKPOINT_PATH = "checkpoints/fingerspelling_checkpoint.pth"
checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)

model = SPOTER_EMBEDDINGS(
    features=checkpoint["config_args"].vector_length,
    hidden_dim=checkpoint["config_args"].hidden_dim,
    norm_emb=checkpoint["config_args"].normalize_embeddings,
).to(device)
model.load_state_dict(checkpoint["state_dict"])
# set model to evaluation mode
model.eval()

dummy_input = torch.randn(1, 10, 54, 2)

# check if models folder exists
if not os.path.exists('out-models'):
    os.makedirs('out-models')

for model_export in ["onnx", "coreml"]:
    if model_export == "coreml":
        # set device for dummy input
        dummy_input = dummy_input.to(device)
        traced_model = torch.jit.trace(model, dummy_input)

        out = traced_model(dummy_input)
        import coremltools as ct

        # Convert to Core ML
        coreml_model = ct.convert(
            traced_model,
            inputs=[ct.TensorType(name="input", shape=dummy_input.shape)],
        )

        # Save Core ML model
        coreml_model.save("out-models/" + model_name + ".mlmodel")
    else:
        # set device for dummy input
        dummy_input = dummy_input.to(device)

        torch.onnx.export(model,                                # model being run
                        dummy_input,                          # model input (or a tuple for multiple inputs)
                        'out-models/' + model_name + '.onnx',     # where to save the model (can be a file or file-like object)
                        export_params=True,                   # store the trained parameter weights inside the model file
                        opset_version=9,                      # the ONNX version to export the model to
                        do_constant_folding=True,             # whether to execute constant folding for optimization
                        input_names = ['X'],                  # the model's input names
                        output_names = ['Y']                  # the model's output names
                        )