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"""Model used for tests"""

import pytest
import torch.nn as nn
import torch.nn.functional as F
from pytorch_quantization.nn import QuantConv2d, QuantLinear
from pytorch_quantization.tensor_quant import QuantDescriptor

class LeNet(nn.Module):
    def __init__(self, **kwargs):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5, **kwargs)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5, **kwargs)
        self.fc1 = nn.Linear(320, 50, **kwargs)
        self.fc2 = nn.Linear(50, 10, **kwargs)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


class QuantLeNet(nn.Module):
    def __init__(self, **kwargs):
        super(QuantLeNet, self).__init__()
        self.conv1 = QuantConv2d(1, 10, kernel_size=5, **kwargs)
        self.conv2 = QuantConv2d(10, 20, kernel_size=5, **kwargs)
        self.fc1 = QuantLinear(320, 50, **kwargs)
        self.fc2 = QuantLinear(50, 10, **kwargs)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

@pytest.fixture
def resnet18():
    import torchvision
    return torchvision.models.resnet18()

@pytest.fixture
def quant_lenet():
    return QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor())