from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import glob
import numpy as np
import onnx.backend.test
import caffe2.python.onnx.backend as c2
import os
from onnx import numpy_helper


def load_tensor_as_numpy_array(f):
    tensor = onnx.TensorProto()
    with open(f, 'rb') as file:
        tensor.ParseFromString(file.read())
    return tensor


def assert_similar(ref, real):
    np.testing.assert_equal(len(ref), len(real))
    for i in range(len(ref)):
        np.testing.assert_allclose(ref[i], real[i], rtol=1e-3)


def run_generated_test(model_file, data_dir, device='CPU'):
    model = onnx.load(model_file)
    input_num = len(glob.glob(os.path.join(data_dir, "input_*.pb")))
    inputs = []
    for i in range(input_num):
        inputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
            os.path.join(data_dir, "input_{}.pb".format(i)))))
    output_num = len(glob.glob(os.path.join(data_dir, "output_*.pb")))
    outputs = []
    for i in range(output_num):
        outputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
            os.path.join(data_dir, "output_{}.pb".format(i)))))
    prepared = c2.prepare(model, device=device)
    c2_outputs = prepared.run(inputs)
    assert_similar(outputs, c2_outputs)