# SPDX-License-Identifier: Apache-2.0

"""
This example builds a simple model without training.
It is converted into ONNX. Predictions are compared to
the predictions from tensorflow to check there is no
discrepencies. Inferencing time is also compared between
*onnxruntime*, *tensorflow* and *tensorflow.lite*.
"""
import subprocess
import timeit

import numpy as np
from onnxruntime import InferenceSession
from tensorflow import keras
from tensorflow.keras import Input, layers

########################################
# Creates the model.
model = keras.Sequential()
model.add(Input((4, 4)))
model.add(layers.SimpleRNN(8))
model.add(layers.Dense(2))
print(model.summary())
input_names = [n.name for n in model.inputs]
output_names = [n.name for n in model.outputs]
print('inputs:', input_names)
print('outputs:', output_names)

########################################
# Training
# ....
# Skipped.

########################################
# Testing the model.
input = np.random.randn(2, 4, 4).astype(np.float32)
expected = model.predict(input)
print(expected)

########################################
# Saves the model.
model.export("simple_rnn")

########################################
# Run the command line.
proc = subprocess.run('python -m tf2onnx.convert --saved-model simple_rnn '
                      '--output simple_rnn.onnx --opset 12'.split(),
                      capture_output=True)
print(proc.returncode)
print(proc.stdout.decode('ascii'))
print(proc.stderr.decode('ascii'))

########################################
# Runs onnxruntime.
session = InferenceSession("simple_rnn.onnx")
got = session.run(None, {'keras_tensor': input})
print(got[0])

########################################
# Measures the differences.
print(np.abs(got[0] - expected).max())

########################################
# Measures processing time.
print('tf:', timeit.timeit('model.predict(input)',
                           number=100, globals=globals()))
print('ort:', timeit.timeit("session.run(None, {'keras_tensor': input})",
                            number=100, globals=globals()))
