# -*- coding: utf-8 -*-

import sys
import os

# torch
import torch
import torch.nn.quantized as nnq
import torch.nn.quantized.dynamic as nnqd
import torch.nn.intrinsic.quantized as nniq

# Testing utils
from torch.testing._internal.common_utils import TestCase
from torch.testing._internal.common_quantized import override_qengines, qengine_is_fbgemm

class TestSerialization(TestCase):
    """ Test backward compatiblity for serialization and numerics
    """
    # Copy and modified from TestCase.assertExpected
    def _test_op(self, qmodule, subname=None, input_size=None, input_quantized=True,
                 generate=False, prec=None, new_zipfile_serialization=False):
        r""" Test quantized modules serialized previously can be loaded
        with current code, make sure we don't break backward compatibility for the
        serialization of quantized modules
        """
        def remove_prefix(text, prefix):
            if text.startswith(prefix):
                return text[len(prefix):]
            return text
        # NB: we take __file__ from the module that defined the test
        # class, so we place the expect directory where the test script
        # lives, NOT where test/common_utils.py lives.
        module_id = self.__class__.__module__
        munged_id = remove_prefix(self.id(), module_id + ".")
        test_file = os.path.realpath(sys.modules[module_id].__file__)
        base_name = os.path.join(os.path.dirname(test_file),
                                 "serialized",
                                 munged_id)

        subname_output = ""
        if subname:
            base_name += "_" + subname
            subname_output = " ({})".format(subname)

        input_file = base_name + ".input.pt"
        state_dict_file = base_name + ".state_dict.pt"
        scripted_module_file = base_name + ".scripted.pt"
        traced_module_file = base_name + ".traced.pt"
        expected_file = base_name + ".expected.pt"

        # only generate once.
        if generate and qengine_is_fbgemm():
            input_tensor = torch.rand(*input_size).float()
            if input_quantized:
                input_tensor = torch.quantize_per_tensor(input_tensor, 0.5, 2, torch.quint8)
            torch.save(input_tensor, input_file)
            # Temporary fix to use _use_new_zipfile_serialization until #38379 lands.
            torch.save(qmodule.state_dict(), state_dict_file, _use_new_zipfile_serialization=new_zipfile_serialization)
            torch.jit.save(torch.jit.script(qmodule), scripted_module_file)
            torch.jit.save(torch.jit.trace(qmodule, input_tensor), traced_module_file)
            torch.save(qmodule(input_tensor), expected_file)

        input_tensor = torch.load(input_file)
        qmodule.load_state_dict(torch.load(state_dict_file))
        qmodule_scripted = torch.jit.load(scripted_module_file)
        qmodule_traced = torch.jit.load(traced_module_file)
        expected = torch.load(expected_file)
        self.assertEqual(qmodule(input_tensor), expected, atol=prec)
        self.assertEqual(qmodule_scripted(input_tensor), expected, atol=prec)
        self.assertEqual(qmodule_traced(input_tensor), expected, atol=prec)

    @override_qengines
    def test_linear(self):
        module = nnq.Linear(3, 1, bias_=True, dtype=torch.qint8)
        self._test_op(module, input_size=[1, 3], generate=False)

    @override_qengines
    def test_linear_relu(self):
        module = nniq.LinearReLU(3, 1, bias=True, dtype=torch.qint8)
        self._test_op(module, input_size=[1, 3], generate=False)

    @override_qengines
    def test_linear_dynamic(self):
        module_qint8 = nnqd.Linear(3, 1, bias_=True, dtype=torch.qint8)
        self._test_op(module_qint8, "qint8", input_size=[1, 3], input_quantized=False, generate=False)
        if qengine_is_fbgemm():
            module_float16 = nnqd.Linear(3, 1, bias_=True, dtype=torch.float16)
            self._test_op(module_float16, "float16", input_size=[1, 3], input_quantized=False, generate=False)

    @override_qengines
    def test_conv2d(self):
        module = nnq.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1,
                            groups=1, bias=True, padding_mode="zeros")
        self._test_op(module, input_size=[1, 3, 6, 6], generate=False)
        # TODO: graph mode quantized conv2d module

    @override_qengines
    def test_conv2d_relu(self):
        module = nniq.ConvReLU2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1,
                                 groups=1, bias=True, padding_mode="zeros")
        self._test_op(module, input_size=[1, 3, 6, 6], generate=False)
        # TODO: graph mode quantized conv2d module

    @override_qengines
    def test_conv3d(self):
        if qengine_is_fbgemm():
            module = nnq.Conv3d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1,
                                groups=1, bias=True, padding_mode="zeros")
            self._test_op(module, input_size=[1, 3, 6, 6, 6], generate=False)
            # TODO: graph mode quantized conv3d module

    @override_qengines
    def test_conv3d_relu(self):
        if qengine_is_fbgemm():
            module = nniq.ConvReLU3d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1,
                                     groups=1, bias=True, padding_mode="zeros")
            self._test_op(module, input_size=[1, 3, 6, 6, 6], generate=False)
            # TODO: graph mode quantized conv3d module

    @override_qengines
    def test_lstm(self):
        class LSTMModule(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.lstm = nnqd.LSTM(input_size=3, hidden_size=7, num_layers=1).to(dtype=torch.float)

            def forward(self, x):
                x = self.lstm(x)
                return x
        if qengine_is_fbgemm():
            mod = LSTMModule()
            self._test_op(mod, input_size=[4, 4, 3], input_quantized=False, generate=False, new_zipfile_serialization=True)
