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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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"""Tests of calibrators"""
import inspect
import pytest
import numpy as np

import torch

from pytorch_quantization import enable_onnx_export
from pytorch_quantization import utils as quant_utils
from pytorch_quantization import calib
from pytorch_quantization import nn as quant_nn
import tests.utils as test_utils
from examples.torchvision.models.classification import *
from tests.fixtures import verbose
from tests.fixtures.models import QuantLeNet

np.random.seed(12345)
torch.manual_seed(12345)

# pylint:disable=missing-docstring, no-self-use


class TestExampleModels():

    def test_resnet50(self):
        model = resnet50(pretrained=True, quantize=True)
        model.eval()

        for name, module in model.named_modules():
            if name.endswith('_quantizer'):
                module.amax = 2.50

        model.cuda()
        dummy_input = torch.randn(1, 3, 224, 224, device='cuda')
        with enable_onnx_export():
            if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
                torch.onnx.export(model,
                                  dummy_input,
                                  "/tmp/resnet50.onnx",
                                  verbose=False,
                                  opset_version=13,
                                  enable_onnx_checker=False,
                                  do_constant_folding=True)
            else:
                torch.onnx.export(model,
                                  dummy_input,
                                  "/tmp/resnet50.onnx",
                                  verbose=False,
                                  opset_version=13,
                                  do_constant_folding=True)

    def test_resnet50_cpu(self):
        model = resnet50(pretrained=True, quantize=True)
        model.eval()

        for name, module in model.named_modules():
            if name.endswith('_quantizer'):
                module.amax = 2.50

        dummy_input = torch.randn(1, 3, 224, 224)
        with enable_onnx_export():
            if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
                torch.onnx.export(model,
                                  dummy_input,
                                  "/tmp/resnet50_cpu.onnx",
                                  verbose=False,
                                  opset_version=13,
                                  enable_onnx_checker=False,
                                  do_constant_folding=True)
            else:
                torch.onnx.export(model,
                                  dummy_input,
                                  "/tmp/resnet50.onnx",
                                  verbose=False,
                                  opset_version=13,
                                  do_constant_folding=True)
