r"""Importing this file includes common utility methods and base clases for
checking quantization api and properties of resulting modules.
"""

import torch
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.quantized.dynamic as nnqd
import torch.distributed as dist

from torch.testing._internal.common_utils import TestCase
from torch.quantization import QuantWrapper, QuantStub, DeQuantStub, \
    default_qconfig, default_dynamic_qconfig, default_per_channel_qconfig, QConfig, default_observer, default_weight_observer, \
    propagate_qconfig_, convert, get_default_qconfig, quantize_dynamic_jit, quantize_jit, float_qparams_dynamic_qconfig, \
    get_default_qat_qconfig
from torch.quantization import (
    is_custom_module_class,
    is_observed_custom_module,
)
from torch.quantization.quantization_mappings import (
    get_dynamic_quant_module_mappings,
    get_qconfig_propagation_list,
    get_qat_module_mappings,
)
# symbolic trace
from torch._fx import symbolic_trace

# graph mode quantization based on fx
from torch.quantization import (
    QuantType,
    prepare_fx,
    prepare_dynamic_fx,
    convert_fx,
    convert_dynamic_fx,
)

import copy
import io
import functools
import time
import os

import unittest
import numpy as np
from torch.testing import FileCheck

class NodeSpec:
    ''' Used for checking GraphModule Node
    '''
    def __init__(self, op, target):
        '''
        op: call_function | call_module
        target:
          for call_function, target would be a function
          for call_module, target would be the type of PyTorch module
        '''
        self.op = op
        self.target = target

    @classmethod
    def call_function(cls, target):
        return NodeSpec('call_function', target)

    @classmethod
    def call_method(cls, target):
        return NodeSpec('call_method', target)

    @classmethod
    def call_module(cls, target):
        return NodeSpec('call_module', target)

    def __hash__(self):
        return hash((self.op, self.target))

    def __eq__(self, other):
        if not isinstance(other, NodeSpec):
            return NotImplemented

        return self.op == other.op and self.target == other.target

    def __repr__(self):
        return repr(self.op) + " " + repr(self.target)

def test_only_eval_fn(model, calib_data):
    r"""
    Default evaluation function takes a torch.utils.data.Dataset or a list of
    input Tensors and run the model on the dataset
    """
    for inp in calib_data:
        output = model(*inp)

_default_loss_fn = torch.nn.CrossEntropyLoss()
def test_only_train_fn(model, train_data, loss_fn=_default_loss_fn):
    r"""
    Default train function takes a torch.utils.data.Dataset and train the model
    on the dataset
    """
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    train_loss, correct, total = 0, 0, 0
    for i in range(10):
        model.train()
        for data, target in train_data:
            optimizer.zero_grad()
            output = model(data)
            loss = loss_fn(output, target)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            _, predicted = torch.max(output, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    return train_loss, correct, total

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def train_one_epoch(model, criterion, optimizer, data_loader, device, ntrain_batches):
    model.train()
    cnt = 0
    for image, target in data_loader:
        start_time = time.time()
        print('.', end='')
        cnt += 1
        image, target = image.to(device), target.to(device)
        output = model(image)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        if cnt >= ntrain_batches:
            return
    return

def ddp_setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'

    # initialize the process group
    dist.init_process_group("gloo", rank=rank, world_size=world_size)

def ddp_cleanup():
    dist.destroy_process_group()

def run_ddp(rank, world_size, prepared):
    ddp_setup(rank, world_size)
    prepared.cuda()
    prepared = torch.nn.parallel.DistributedDataParallel(prepared, device_ids=[rank])
    prepared.to(rank)
    model_with_ddp = prepared
    optimizer = torch.optim.SGD(model_with_ddp.parameters(), lr=0.0001)
    train_one_epoch(model_with_ddp, criterion, optimizer, dataset, rank, 1)
    ddp_cleanup()


def convert_dynamic(module):
    convert(module, get_dynamic_quant_module_mappings(), inplace=True)

def prepare_dynamic(model, qconfig_dict=None):
    propagate_qconfig_(model, qconfig_dict)

def _make_conv_test_input(
    batch_size, in_channels_per_group, input_feature_map_size,
    out_channels_per_group, groups, kernel_size, X_scale, X_zero_point, W_scale,
    W_zero_point, use_bias, use_channelwise,
):
    in_channels = in_channels_per_group * groups
    out_channels = out_channels_per_group * groups

    (X_value_min, X_value_max) = (0, 4)
    X_init = torch.randint(
        X_value_min, X_value_max,
        (batch_size, in_channels,) + input_feature_map_size)
    X = X_scale * (X_init - X_zero_point).float()
    X_q = torch.quantize_per_tensor(
        X, scale=X_scale, zero_point=X_zero_point, dtype=torch.quint8)

    W_scale = W_scale * out_channels
    W_zero_point = W_zero_point * out_channels
    # Resize W_scale and W_zero_points arrays equal to out_channels
    W_scale = W_scale[:out_channels]
    W_zero_point = W_zero_point[:out_channels]
    # For testing, we use small values for weights and for activations so that
    # no overflow occurs in vpmaddubsw instruction. If the overflow occurs in
    # qconv implementation and if there is no overflow.
    # In reference we can't exactly match the results with reference.
    # Please see the comment in qconv implementation file
    #   aten/src/ATen/native/quantized/cpu/qconv.cpp for more details.
    (W_value_min, W_value_max) = (-5, 5)
    # The operator expects them in the format
    # (out_channels, in_channels/groups,) + kernel_size
    W_init = torch.randint(
        W_value_min, W_value_max,
        (out_channels, in_channels_per_group,) + kernel_size)
    b_init = torch.randint(0, 10, (out_channels,))

    if use_channelwise:
        W_shape = (-1, 1) + (1,) * len(kernel_size)
        W_scales_tensor = torch.tensor(W_scale, dtype=torch.float)
        W_zero_points_tensor = torch.tensor(W_zero_point, dtype=torch.float)
        W = W_scales_tensor.reshape(*W_shape) * (
            W_init.float() - W_zero_points_tensor.reshape(*W_shape)).float()
        b = X_scale * W_scales_tensor * b_init.float()
        W_q = torch.quantize_per_channel(
            W, W_scales_tensor.double(), W_zero_points_tensor.long(), 0,
            dtype=torch.qint8)
    else:
        W = W_scale[0] * (W_init - W_zero_point[0]).float()
        b = X_scale * W_scale[0] * b_init.float()
        W_q = torch.quantize_per_tensor(
            W, scale=W_scale[0], zero_point=W_zero_point[0], dtype=torch.qint8)

    return (X, X_q, W, W_q, b if use_bias else None)

def skipIfNoFBGEMM(fn):
    reason = 'Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs with instruction set support AVX2 or newer.'
    if isinstance(fn, type):
        if 'fbgemm' not in torch.backends.quantized.supported_engines:
            fn.__unittest_skip__ = True
            fn.__unittest_skip_why__ = reason
        return fn

    @functools.wraps(fn)
    def wrapper(*args, **kwargs):
        if 'fbgemm' not in torch.backends.quantized.supported_engines:
            raise unittest.SkipTest(reason)
        else:
            fn(*args, **kwargs)
    return wrapper

try:
    import torchvision  # noqa: F401
    HAS_TORCHVISION = True
except ImportError:
    HAS_TORCHVISION = False
skip_if_no_torchvision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")

def get_script_module(model, tracing, data):
    return torch.jit.trace(model, data) if tracing else torch.jit.script(model)

def lengths_to_offsets(t, offset_type=np.int64, use_begin_offset=True):
    """
    Convert lengths to offsets for embedding_bag
    """
    tt = np.zeros((t.shape[0] + 1,), dtype=offset_type)
    tt[1:] = t
    tt = torch.from_numpy(np.cumsum(tt, dtype=offset_type))
    if use_begin_offset:
        return tt[:-1]
    return tt[1:]

# QuantizationTestCase used as a base class for testing quantization on modules
class QuantizationTestCase(TestCase):
    def setUp(self):
        super().setUp()
        self.calib_data = [[torch.rand(2, 5, dtype=torch.float)] for _ in range(2)]
        self.train_data = [[torch.rand(2, 5, dtype=torch.float), torch.randint(0, 1, (2,), dtype=torch.long)] for _ in range(2)]
        self.img_data_1d = [[torch.rand(2, 3, 10, dtype=torch.float)]
                            for _ in range(2)]
        self.img_data_2d = [[torch.rand(1, 3, 10, 10, dtype=torch.float)]
                            for _ in range(2)]
        self.img_data_3d = [[torch.rand(1, 3, 5, 5, 5, dtype=torch.float)]
                            for _ in range(2)]
        self.img_data_1d_train = [[torch.rand(2, 3, 10, dtype=torch.float),
                                   torch.randint(0, 1, (1,), dtype=torch.long)]
                                  for _ in range(2)]
        self.img_data_2d_train = [[torch.rand(1, 3, 10, 10, dtype=torch.float),
                                   torch.randint(0, 1, (1,), dtype=torch.long)]
                                  for _ in range(2)]
        self.img_data_3d_train = [[torch.rand(1, 3, 5, 5, 5, dtype=torch.float),
                                   torch.randint(0, 1, (1,), dtype=torch.long)]
                                  for _ in range(2)]

        self.img_data_dict = {1 : self.img_data_1d,
                              2 : self.img_data_2d,
                              3 : self.img_data_3d}

        # Quant types that produce statically quantized ops
        self.static_quant_types = [QuantType.STATIC, QuantType.QAT]
        # All quant types for (fx based) graph mode quantization
        self.all_quant_types = [QuantType.DYNAMIC, QuantType.STATIC, QuantType.QAT]

    def checkNoPrepModules(self, module):
        r"""Checks the module does not contain child
            modules for quantization prepration, e.g.
            quant, dequant and observer
        """
        self.assertFalse(hasattr(module, 'quant'))
        self.assertFalse(hasattr(module, 'dequant'))

    def checkNoQconfig(self, module):
        r"""Checks the module does not contain qconfig
        """
        self.assertFalse(hasattr(module, 'qconfig'))

        for child in module.children():
            self.checkNoQconfig(child)

    def checkHasPrepModules(self, module):
        r"""Checks the module contains child
            modules for quantization prepration, e.g.
            quant, dequant and observer
        """
        self.assertTrue(hasattr(module, 'module'))
        self.assertTrue(hasattr(module, 'quant'))
        self.assertTrue(hasattr(module, 'dequant'))

    def checkObservers(self, module, propagate_qconfig_list=None):
        r"""Checks the module or module's leaf descendants
            have observers in preperation for quantization
        """
        if propagate_qconfig_list is None:
            propagate_qconfig_list = get_qconfig_propagation_list()

        # check if a module is a leaf module, ignoring activation_post_process attribute
        def is_leaf_module(module):
            submodule_name_count = 0
            for name, _ in module.named_children():
                if name != 'activation_post_process':
                    submodule_name_count += 1
            return submodule_name_count == 0

        if (hasattr(module, 'qconfig') and module.qconfig is not None and
           is_leaf_module(module) and not isinstance(module, torch.nn.Sequential)
           and type(module) in propagate_qconfig_list) or \
           is_custom_module_class(type(module)):
            self.assertTrue(hasattr(module, 'activation_post_process'),
                            'module: ' + str(type(module)) + ' do not have observer')
        # we don't need to check observers for child modules of the
        # qat modules
        if type(module) not in get_qat_module_mappings().values() and \
           not is_observed_custom_module(module):
            for child in module.children():
                self.checkObservers(child)

    def checkQuantDequant(self, mod):
        r"""Checks that mod has nn.Quantize and
            nn.DeQuantize submodules inserted
        """
        self.assertEqual(type(mod.quant), nnq.Quantize)
        self.assertEqual(type(mod.dequant), nnq.DeQuantize)

    def checkWrappedQuantizedLinear(self, mod):
        r"""Checks that mod has been swapped for an nnq.Linear
            module, the bias is qint32, and that the module
            has Quantize and DeQuantize submodules
        """
        self.assertEqual(type(mod.module), nnq.Linear)
        self.checkQuantDequant(mod)

    def checkQuantizedLinear(self, mod):
        self.assertEqual(type(mod), nnq.Linear)

    def checkDynamicQuantizedLinear(self, mod, dtype):
        r"""Checks that mod has been swapped for an nnqd.Linear
            module, the bias is float.
        """
        self.assertEqual(type(mod), nnqd.Linear)
        self.assertEqual(mod._packed_params.dtype, dtype)

    def check_eager_serialization(self, ref_model, loaded_model, x):
        # Check state dict serialization and torch.save APIs
        model_dict = ref_model.state_dict()
        b = io.BytesIO()
        torch.save(model_dict, b)
        b.seek(0)
        loaded_dict = torch.load(b)
        loaded_model.load_state_dict(loaded_dict)
        ref_out = ref_model(*x)
        load_out = loaded_model(*x)

        def check_outputs(ref_out, load_out):
            self.assertEqual(ref_out[0], load_out[0])
            if isinstance(ref_out[1], tuple):
                self.assertEqual(ref_out[1][0], load_out[1][0])
                self.assertEqual(ref_out[1][1], load_out[1][1])
            else:
                self.assertEqual(ref_out[1], load_out[1])

        check_outputs(ref_out, load_out)
        b = io.BytesIO()
        torch.save(ref_model, b)
        b.seek(0)
        loaded = torch.load(b)
        load_out = loaded(*x)
        check_outputs(ref_out, load_out)

    def check_weight_bias_api(self, ref_model, weight_keys, bias_keys):
        weight = ref_model.get_weight()
        bias = ref_model.get_bias()
        self.assertEqual(weight_keys ^ weight.keys(), set())
        self.assertEqual(bias_keys ^ bias.keys(), set())

    def checkDynamicQuantizedLSTM(self, mod, reference_module_type, dtype):
        r"""Checks that mod has been swapped for an nnqd.LSTM type
            module, the bias is float.
        """
        wt_dtype_map = {torch.qint8: 'quantized_dynamic', torch.float16: 'quantized_fp16'}
        self.assertEqual(type(mod), reference_module_type)
        for packed_params in mod._all_weight_values:
            self.assertEqual(packed_params.param.__getstate__()[0][0], wt_dtype_map[dtype])

    def checkLinear(self, mod):
        self.assertEqual(type(mod), torch.nn.Linear)

    def checkDynamicQuantizedModule(self, mod, reference_module_type, dtype):
        r"""Checks that mod has been swapped for an nnqd.Linear
            module, the bias is float.
        """
        wt_dtype_map = {torch.qint8: 'quantized_dynamic', torch.float16: 'quantized_fp16'}
        self.assertEqual(type(mod), reference_module_type)
        if hasattr(mod, '_all_weight_values'):
            for packed_params in mod._all_weight_values:
                self.assertEqual(packed_params.param.__getstate__()[0][0], wt_dtype_map[dtype])

    def checkScriptable(self, orig_mod, calib_data, check_save_load=False):
        scripted = torch.jit.script(orig_mod)
        self._checkScriptable(orig_mod, scripted, calib_data, check_save_load)

        # Use first calib_data entry as trace input
        traced = torch.jit.trace(orig_mod, calib_data[0])
        self._checkScriptable(orig_mod, traced, calib_data, check_save_load)

    # Call this twice: once for a scripted module and once for a traced module
    def _checkScriptable(self, orig_mod, script_mod, calib_data, check_save_load):
        self._checkModuleCorrectnessAgainstOrig(orig_mod, script_mod, calib_data)

        # Test save/load
        buffer = io.BytesIO()
        torch.jit.save(script_mod, buffer)

        buffer.seek(0)
        loaded_mod = torch.jit.load(buffer)
        # Pending __get_state_ and __set_state__ support
        # See tracking task https://github.com/pytorch/pytorch/issues/23984
        if check_save_load:
            self._checkModuleCorrectnessAgainstOrig(orig_mod, loaded_mod, calib_data)

    def _checkModuleCorrectnessAgainstOrig(self, orig_mod, test_mod, calib_data):
        for inp in calib_data:
            ref_output = orig_mod(*inp)
            scripted_output = test_mod(*inp)
            self.assertEqual(scripted_output, ref_output)


    def checkGraphModeOp(self, module, inputs, quantized_op, tracing=False, debug=False,
                         check=True, eval_mode=True, dynamic=False, qconfig=None):
        if debug:
            print('Testing:', str(module))
        qconfig_dict = {'': get_default_qconfig(torch.backends.quantized.engine)}

        if eval_mode:
            module = module.eval()
        if dynamic:
            qconfig_dict = {'': default_dynamic_qconfig if qconfig is None else qconfig}
        model = get_script_module(module, tracing, inputs[0]).eval()
        if debug:
            print('input graph:', model.graph)
        models = {}
        outputs = {}
        for d in [True, False]:
            if dynamic:
                models[d] = quantize_dynamic_jit(model, qconfig_dict, debug=d)
                # make sure it runs
                outputs[d] = models[d](inputs)
            else:
                # module under test can contain in-place ops, and we depend on
                # input data staying constant for comparisons
                inputs_copy = copy.deepcopy(inputs)
                models[d] = quantize_jit(
                    model, qconfig_dict, test_only_eval_fn, [inputs_copy], inplace=False,
                    debug=d)
                # make sure it runs
                outputs[d] = models[d](*inputs[0])

        if debug:
            print('debug graph:', models[True].graph)
            print('non debug graph:', models[False].graph)

        if check:
            # debug and non-debug option should have the same numerics
            self.assertEqual(outputs[True], outputs[False])

            # non debug graph should produce quantized op
            FileCheck().check(quantized_op) \
                       .run(models[False].graph)

        return models[False]

    def checkGraphModuleNodes(
            self, graph_module,
            expected_node=None,
            expected_node_occurrence=None,
            expected_node_list=None):
        """ Check if GraphModule contains the target node
        Args:
            graph_module: the GraphModule instance we want to check
            expected_node, expected_node_occurrence, expected_node_list:
               see docs for checkGraphModeFxOp
        """
        nodes_in_graph = dict()
        node_list = []
        modules = dict(graph_module.named_modules())
        for node in graph_module.graph.nodes:
            n = None
            if node.op == 'call_function' or node.op == 'call_method':
                n = NodeSpec(node.op, node.target)
            elif node.op == 'call_module':
                n = NodeSpec(node.op, type(modules[node.target]))

            if n is not None:
                node_list.append(n)
                if n in nodes_in_graph:
                    nodes_in_graph[n] += 1
                else:
                    nodes_in_graph[n] = 1

        if expected_node is not None:
            self.assertTrue(expected_node in nodes_in_graph, 'node:' + str(expected_node) +
                            ' not found in the graph module')

        if expected_node_occurrence is not None:
            for expected_node, occurrence in expected_node_occurrence.items():
                if occurrence != 0:
                    self.assertTrue(
                        expected_node in nodes_in_graph,
                        'Check failed for node:' + str(expected_node) +
                        ' not found')
                    self.assertTrue(
                        nodes_in_graph[expected_node] == occurrence,
                        'Check failed for node:' + str(expected_node) +
                        ' Expected occurrence:' + str(occurrence) +
                        ' Found occurrence:' + str(nodes_in_graph[expected_node]))
                else:
                    self.assertTrue(
                        expected_node not in nodes_in_graph,
                        'Check failed for node:' + str(expected_node) +
                        ' expected no occurrence but found')

        if expected_node_list is not None:
            cur_index = 0
            for n in node_list:
                if cur_index == len(expected_node_list):
                    return
                if n == expected_node_list[cur_index]:
                    cur_index += 1
            self.assertTrue(
                cur_index == len(expected_node_list),
                "Check failed for graph:" +
                self.printGraphModule(graph_module, print_str=False) +
                "Expected ordered list:" +
                str(expected_node_list))

    def printGraphModule(self, graph_module, print_str=True):
        modules = dict(graph_module.named_modules())
        node_infos = []
        for n in graph_module.graph.nodes:
            node_info = ' '.join(map(repr, [n.op, n.name, n.target, n.args, n.kwargs]))
            if n.op == 'call_module':
                node_info += ' module type: ' + repr(type(modules[n.target]))
            node_infos.append(node_info)
        str_to_print = '\n'.join(node_infos)
        if print_str:
            print(str_to_print)
        return str_to_print

    def checkGraphModeFxOp(self, model, inputs, quant_type,
                           expected_node=None,
                           expected_node_occurrence=None,
                           expected_node_list=None,
                           debug=False,
                           print_debug_info=False):
        """ Quantizes model with graph mode quantization on fx and check if the
        quantized model contains the quantized_node

        Args:
            model: floating point torch.nn.Module
            inputs: one positional sample input arguments for model
            expected_node: NodeSpec
                  e.g. NodeSpec.call_function(torch.quantize_per_tensor)
            expected_node_occurrence: a dict from NodeSpec to
                  expected number of occurences (int)
                  e.g. {NodeSpec.call_function(torch.quantize_per_tensor) : 1,
                        NodeSpec.call_method('dequantize'): 1}
            expected_node_list: a list of NodeSpec, used to check the order
                  of the occurrence of Node
                  e.g. [NodeSpec.call_function(torch.quantize_per_tensor),
                        NodeSpec.call_module(nnq.Conv2d),
                        NodeSpec.call_function(F.hardtanh_),
                        NodeSpec.call_method('dequantize')]
        """
        # TODO: make img_data a single example instead of a list
        if type(inputs) == list:
            inputs = inputs[0]
        if quant_type == QuantType.QAT:
            qconfig_dict = {'': get_default_qat_qconfig(torch.backends.quantized.engine)}
            model.train()
        else:
            qconfig_dict = {'': get_default_qconfig(torch.backends.quantized.engine)}
            model.eval()
        original = symbolic_trace(model)

        if quant_type == QuantType.DYNAMIC:
            prepare = prepare_dynamic_fx
            convert = convert_dynamic_fx
        else:
            prepare = prepare_fx
            convert = convert_fx

        prepared = prepare(original, qconfig_dict)
        prepared(*inputs)
        qgraph = convert(prepared)
        qgraph_debug = convert(prepared, debug=True)

        result = qgraph(*inputs)
        result_debug = qgraph_debug(*inputs)

        self.assertEqual((result - result_debug).abs().max(), 0), \
            'Expecting debug and non-debug option to produce identical result'

        if print_debug_info:
            print()
            print('quant type:', quant_type)
            print('origianl graph module:', type(model))
            self.printGraphModule(original)
            print()
            print('quantized graph module:', type(qgraph))
            self.printGraphModule(qgraph)
            print()
        qgraph_to_check = qgraph_debug if debug else qgraph
        self.checkGraphModuleNodes(
            qgraph_to_check, expected_node, expected_node_occurrence, expected_node_list)


    def checkEmbeddingSerialization(self, qemb, num_embeddings, embedding_dim, indices, offsets, set_qconfig, is_emb_bag):
        # Test serialization of dynamic EmbeddingBag module using state_dict
        if is_emb_bag:
            inputs = [indices, offsets]
        else:
            inputs = [indices]
        emb_dict = qemb.state_dict()
        b = io.BytesIO()
        torch.save(emb_dict, b)
        b.seek(0)
        loaded_dict = torch.load(b)
        embedding_unpack = torch.ops.quantized.embedding_bag_unpack
        # Check unpacked weight values explicitly
        for key in emb_dict:
            if isinstance(emb_dict[key], torch._C.ScriptObject):
                assert isinstance(loaded_dict[key], torch._C.ScriptObject)
                emb_weight = embedding_unpack(emb_dict[key])
                loaded_weight = embedding_unpack(loaded_dict[key])
                self.assertEqual(emb_weight, loaded_weight)

        # Check state dict serialization and torch.save APIs
        if is_emb_bag:
            loaded_qemb = nnq.EmbeddingBag(num_embeddings=num_embeddings, embedding_dim=embedding_dim,
                                           include_last_offset=True, mode='sum')
        else:
            loaded_qemb = nnq.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)
        self.check_eager_serialization(qemb, loaded_qemb, inputs)

        loaded_qemb.load_state_dict(loaded_dict)
        self.assertEqual(embedding_unpack(qemb._packed_params._packed_weight),
                         embedding_unpack(loaded_qemb._packed_params._packed_weight))


        # Test JIT serialization
        self.checkScriptable(qemb, [inputs], check_save_load=True)

        # Test from_float call
        if is_emb_bag:
            float_embedding = torch.nn.EmbeddingBag(num_embeddings=num_embeddings, embedding_dim=embedding_dim,
                                                    include_last_offset=True, scale_grad_by_freq=False, mode='sum')
        else:
            float_embedding = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)

        if set_qconfig:
            float_embedding.qconfig = float_qparams_dynamic_qconfig

        prepare_dynamic(float_embedding)

        float_embedding(*inputs)
        if is_emb_bag:
            q_embeddingbag = nnq.EmbeddingBag.from_float(float_embedding)
            expected_name = "QuantizedEmbeddingBag"
        else:
            q_embeddingbag = nnq.Embedding.from_float(float_embedding)
            expected_name = "QuantizedEmbedding"

        q_embeddingbag(*inputs)

        self.assertTrue(expected_name in str(q_embeddingbag))


# Below are a series of neural net models to use in testing quantization
# Single layer models
class SingleLayerLinearModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.fc1(x)
        return x

class AnnotatedSingleLayerLinearModel(torch.nn.Module):
    def __init__(self, qengine='fbgemm'):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.fc1 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float))

    def forward(self, x):
        x = self.fc1(x)
        return x

class SingleLayerLinearDynamicModel(torch.nn.Module):
    def __init__(self, qengine='fbgemm'):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.fc1(x)
        return x

class RNNDynamicModel(torch.nn.Module):
    def __init__(self, mod_type):
        super().__init__()
        self.qconfig = default_dynamic_qconfig
        if mod_type == 'GRU':
            self.mod = torch.nn.GRU(2, 2).to(dtype=torch.float)
        if mod_type == 'LSTM':
            self.mod = torch.nn.LSTM(2, 2).to(dtype=torch.float)

    def forward(self, x):
        x = self.mod(x)
        return x

class RNNCellDynamicModel(torch.nn.Module):
    def __init__(self, mod_type):
        super().__init__()
        self.qconfig = default_dynamic_qconfig
        if mod_type == 'GRUCell':
            self.mod = torch.nn.GRUCell(2, 2).to(dtype=torch.float)
        if mod_type == 'LSTMCell':
            self.mod = torch.nn.LSTMCell(2, 2).to(dtype=torch.float)
        if mod_type == 'RNNReLU':
            self.mod = torch.nn.RNNCell(2, 2, nonlinearity='relu').to(dtype=torch.float)
        if mod_type == 'RNNTanh':
            self.mod = torch.nn.RNNCell(2, 2, nonlinearity='tanh').to(dtype=torch.float)

    def forward(self, x):
        x = self.mod(x)
        return x

class LSTMwithHiddenDynamicModel(torch.nn.Module):
    def __init__(self, qengine='fbgemm'):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.lstm = torch.nn.LSTM(2, 2).to(dtype=torch.float)

    def forward(self, x, hid):
        x, hid = self.lstm(x, hid)
        return x, hid

class ConvModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(3, 5, 3, bias=False).to(dtype=torch.float)

    def forward(self, x):
        x = self.conv(x)
        return x

class ConvTransposeModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.ConvTranspose2d(3, 5, 3, bias=False).to(dtype=torch.float)

    def forward(self, x):
        x = self.conv(x)
        return x

class AnnotatedConvModel(torch.nn.Module):
    def __init__(self, qengine):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.conv = torch.nn.Conv2d(3, 5, 3, bias=False).to(dtype=torch.float)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = self.dequant(x)
        return x

class AnnotatedConvTransposeModel(torch.nn.Module):
    def __init__(self, qengine):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.conv = torch.nn.ConvTranspose2d(3, 5, 3, bias=False).to(dtype=torch.float)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = self.dequant(x)
        return x

class ConvBnModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(3, 5, 3, bias=False).to(dtype=torch.float)
        self.bn = torch.nn.BatchNorm2d(5).to(dtype=torch.float)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return x

class AnnotatedConvBnModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.qconfig = default_qconfig
        self.conv = torch.nn.Conv2d(3, 5, 3, bias=False).to(dtype=torch.float)
        self.bn = torch.nn.BatchNorm2d(5).to(dtype=torch.float)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = self.bn(x)
        x = self.dequant(x)
        return x

class AnnotatedConvBnReLUModel(torch.nn.Module):
    def __init__(self, qengine='fbgemm'):
        super(AnnotatedConvBnReLUModel, self).__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.conv = torch.nn.Conv2d(3, 5, 3, bias=False).to(dtype=torch.float)
        self.bn = torch.nn.BatchNorm2d(5).to(dtype=torch.float)
        self.relu = nn.ReLU(inplace=True)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.dequant(x)
        return x

    def fuse_model(self):
        torch.quantization.fuse_modules(self, [['conv', 'bn', 'relu']], inplace=True)

class TwoLayerLinearModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float)
        self.fc2 = torch.nn.Linear(8, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        return x

class LinearModelWithSubmodule(nn.Module):
    def __init__(self):
        super(LinearModelWithSubmodule, self).__init__()
        self.subm = TwoLayerLinearModel()
        self.fc = nn.Linear(5, 5)

    def forward(self, x):
        x = self.subm(x)
        x = self.fc(x)
        return x

class AnnotatedTwoLayerLinearModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float)
        self.fc2 = QuantWrapper(torch.nn.Linear(8, 5).to(dtype=torch.float))
        self.fc2.qconfig = torch.quantization.get_default_qconfig("fbgemm")

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        return x

class ActivationsTestModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig("fbgemm")
        self.quant = torch.quantization.QuantStub()
        self.hardswish = torch.nn.Hardswish().to(dtype=torch.float)
        self.elu = torch.nn.ELU().to(dtype=torch.float)
        self.dequant = torch.quantization.DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.hardswish(x)
        x = self.elu(x)
        x = self.dequant(x)
        return x

class LinearReluModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc(x))
        return x

class NormalizationTestModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.quant = torch.quantization.QuantStub()
        self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float)
        self.layer_norm = torch.nn.LayerNorm((8))
        self.group_norm = torch.nn.GroupNorm(2, 8)
        self.instance_norm1d = torch.nn.InstanceNorm1d(8)
        self.instance_norm2d = torch.nn.InstanceNorm2d(8)
        self.instance_norm3d = torch.nn.InstanceNorm3d(8)

    def forward(self, x):
        x = self.quant(x)
        x = self.fc1(x)
        x = self.layer_norm(x)
        x = self.group_norm(x.unsqueeze(-1))
        x = self.instance_norm1d(x)
        x = self.instance_norm2d(x.unsqueeze(-1))
        x = self.instance_norm3d(x.unsqueeze(-1))
        return x

class NestedModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.sub1 = LinearReluModel()
        self.sub2 = TwoLayerLinearModel()
        self.fc3 = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.sub1(x)
        x = self.sub2(x)
        x = self.fc3(x)
        return x

class AnnotatedNestedModel(torch.nn.Module):
    def __init__(self, qengine):
        super().__init__()
        self.sub1 = LinearReluModel()
        self.sub2 = TwoLayerLinearModel()
        self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float))
        self.fc3.qconfig = default_qconfig
        self.sub2.fc1 = QuantWrapper(self.sub2.fc1)
        if qengine == 'fbgemm':
            self.sub2.fc1.qconfig = default_per_channel_qconfig
        else:
            self.sub2.fc1.qconfig = default_qconfig

    def forward(self, x):
        x = self.sub1(x)
        x = self.sub2(x)
        x = self.fc3(x)
        return x

class AnnotatedSubNestedModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.sub1 = LinearReluModel()
        self.sub2 = QuantWrapper(TwoLayerLinearModel())
        self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float))
        self.fc3.qconfig = default_qconfig
        self.sub2.qconfig = default_qconfig

    def forward(self, x):
        x = self.sub1(x)
        x = self.sub2(x)
        x = self.fc3(x)
        return x

class AnnotatedCustomConfigNestedModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.sub1 = LinearReluModel()
        self.sub2 = TwoLayerLinearModel()
        self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float))
        self.fc3.qconfig = default_qconfig
        self.sub2.qconfig = default_qconfig

        custom_options = {
            'dtype': torch.quint8,
            'qscheme': torch.per_tensor_affine
        }
        custom_qconfig = QConfig(activation=default_observer.with_args(**custom_options),
                                 weight=default_weight_observer)
        self.sub2.fc1.qconfig = custom_qconfig

        self.sub2.fc1 = QuantWrapper(self.sub2.fc1)
        self.sub2.fc2 = QuantWrapper(self.sub2.fc2)

    def forward(self, x):
        x = self.sub1(x)
        x = self.sub2(x)
        x = self.fc3(x)
        return x

class QuantSubModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.sub1 = LinearReluModel()
        self.sub2 = QuantWrapper(TwoLayerLinearModel())
        self.sub2.qconfig = default_qconfig
        self.fc3 = torch.nn.Linear(5, 5).to(dtype=torch.float)
        self.fc3.qconfig = default_qconfig

    def forward(self, x):
        x = self.sub1(x)
        x = self.sub2(x)
        x = self.fc3(x)
        return x

class InnerModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float)
        self.relu1 = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(8, 5).to(dtype=torch.float)
        self.relu2 = torch.nn.ReLU()

    def forward(self, x):
        return self.relu2(self.fc2(self.relu1(self.fc1(x))))

    def fuse_modules(self):
        fusable_layers = []
        named_children = list(self.named_children())
        for idx, (current_name, layer) in enumerate(named_children):
            if isinstance(layer, torch.nn.Linear):
                if idx >= len(named_children) - 1:
                    break
                if isinstance(named_children[idx + 1][1], torch.nn.ReLU):
                    fusable_layers.append([current_name,
                                           named_children[idx + 1][0]])
        torch.quantization.fuse_modules(self, fusable_layers, inplace=True)

class SkipQuantModel(torch.nn.Module):
    r"""We can skip quantization by explicitly
    setting qconfig of a submodule to None
    """
    def __init__(self):
        super().__init__()
        self.sub = InnerModule()
        self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        return self.fc(self.sub(x))

    def fuse_modules(self):
        self.sub.fuse_modules()

class AnnotatedSkipQuantModel(torch.nn.Module):
    r"""We can skip quantization by explicitly
    setting qconfig of a submodule to None
    """
    def __init__(self, qengine):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig(qengine)
        self.sub = QuantWrapper(InnerModule())
        self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float)
        # don't quantize this fc
        self.fc.qconfig = None

    def forward(self, x):
        return self.fc(self.sub(x))

    def fuse_modules(self):
        self.sub.module.fuse_modules()

class QuantStubModel(torch.nn.Module):
    r"""A Module with manually inserted `QuantStub` and `DeQuantStub`
    """
    def __init__(self):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qconfig("qnnpack")
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.quant(x)
        x = self.fc(x)
        return self.dequant(x)

class ManualLinearQATModel(torch.nn.Module):
    r"""A Module with manually inserted `QuantStub` and `DeQuantStub`
    """
    def __init__(self, qengine):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qat_qconfig(qengine)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        self.fc1 = torch.nn.Linear(5, 1).to(dtype=torch.float)
        self.fc2 = torch.nn.Linear(1, 10).to(dtype=torch.float)

    def forward(self, x):
        x = self.quant(x)
        x = self.fc1(x)
        x = self.fc2(x)
        return self.dequant(x)

class ManualConvLinearQATModel(torch.nn.Module):
    r"""A module with manually inserted `QuantStub` and `DeQuantStub`
    and contains both linear and conv modules
    """
    def __init__(self):
        super().__init__()
        self.qconfig = torch.quantization.get_default_qat_qconfig("qnnpack")
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        self.conv = torch.nn.Conv2d(3, 1, kernel_size=3).to(dtype=torch.float)
        self.fc1 = torch.nn.Linear(64, 10).to(dtype=torch.float)
        self.fc2 = torch.nn.Linear(10, 10).to(dtype=torch.float)

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = x.view(-1, 64).contiguous()
        x = self.fc1(x)
        x = self.fc2(x)
        return self.dequant(x)


class SubModelForFusion(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(2, 2, 1, bias=None).to(dtype=torch.float)
        self.bn = nn.BatchNorm2d(2).to(dtype=torch.float)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return x


class SubModelWithoutFusion(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(2, 2, 1, bias=None).to(dtype=torch.float)
        self.relu = nn.ReLU(inplace=False).to(dtype=torch.float)

    def forward(self, x):
        return self.relu(self.conv(x))

class ModelForFusion(nn.Module):
    def __init__(self, qconfig):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 2, 1, bias=None).to(dtype=torch.float)
        self.bn1 = nn.BatchNorm2d(2).to(dtype=torch.float)
        self.relu1 = nn.ReLU(inplace=True).to(dtype=torch.float)
        self.sub1 = SubModelForFusion()
        self.sub2 = SubModelWithoutFusion()
        self.fc = nn.Linear(36, 10).to(dtype=torch.float)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        self.qconfig = qconfig
        self.conv2 = nn.Conv3d(3, 2, (1, 1, 1), bias=None).to(dtype=torch.float)
        self.relu2 = nn.ReLU(inplace=False).to(dtype=torch.float)
        self.bn2 = nn.BatchNorm3d(2).to(dtype=torch.float)
        self.relu3 = nn.ReLU(inplace=True).to(dtype=torch.float)
        self.conv3 = nn.Conv1d(3, 3, 2).to(dtype=torch.float)
        self.bn3 = nn.BatchNorm1d(3).to(dtype=torch.float)
        self.relu4 = nn.ReLU(inplace=True).to(dtype=torch.float)
        # don't quantize sub2
        self.sub2.qconfig = None
        self.fc.qconfig = None

    def forward(self, x):
        x = x.squeeze(2)
        x = self.quant(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu4(x)
        x = x.unsqueeze(2)
        y = x.unsqueeze(2)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.sub1(x)
        x = self.dequant(x)
        x = self.sub2(x)
        x = x.view(-1, 36).contiguous()
        x = self.fc(x)
        y = self.conv2(y)
        y = self.relu2(y)
        y = self.bn2(y)
        y = self.relu3(y)
        y = self.dequant(y)
        return x

class ConvBNReLU(nn.Sequential):
    def __init__(self):
        super().__init__(
            nn.Conv2d(3, 3, 1, 1, bias=False),
            nn.BatchNorm2d(3),
            nn.ReLU(inplace=False)
        )

class ModelWithSequentialFusion(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 3, 1)
        self.relu1 = nn.ReLU(inplace=False)
        layers = []
        for i in range(3):
            layers.append(ConvBNReLU())
        self.features = nn.Sequential(*layers)
        head = [nn.Linear(300, 10), nn.ReLU(inplace=False)]
        self.classifier = nn.Sequential(*head)
        self.seq = nn.Sequential()
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.features(x)
        x = torch.reshape(x, (-1, 3 * 10 * 10))
        x = self.classifier(x)
        x = self.seq(x)
        x = self.dequant(x)
        return x

class ModelForFusionWithBias(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 2, 5, bias=True).to(dtype=torch.float)
        self.bn1 = nn.BatchNorm2d(2).to(dtype=torch.float)
        self.relu1 = nn.ReLU(inplace=True).to(dtype=torch.float)
        self.conv2 = nn.Conv2d(2, 2, 1, bias=True).to(dtype=torch.float)
        self.bn2 = nn.BatchNorm2d(2).to(dtype=torch.float)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.dequant(x)
        return x

class DummyObserver(torch.nn.Module):
    def calculate_qparams(self):
        return 1.0, 0

    def forward(self, x):
        return x


class ModelWithFunctionals(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.mycat = nnq.FloatFunctional()
        self.myadd = nnq.FloatFunctional()
        self.myadd_relu = nnq.FloatFunctional()
        # Tracing doesnt work yet for c10 ops with scalar inputs
        # https://github.com/pytorch/pytorch/issues/27097
        # self.my_scalar_add = nnq.FloatFunctional()
        # self.my_scalar_mul = nnq.FloatFunctional()

    def forward(self, x):
        y = self.mycat.cat([x, x, x])
        z = self.myadd.add(y, y)
        w = self.myadd_relu.add_relu(z, z)
        # Tracing doesnt work yet for c10 ops with scalar inputs
        # https://github.com/pytorch/pytorch/issues/27097
        # w = self.my_scalar_add.add_scalar(w, -0.5)
        # w = self.my_scalar_mul.mul_scalar(w, 0.5)
        return w


class ResNetBase(torch.nn.Module):
    def __init__(self):
        super().__init__()
        norm_layer = nn.BatchNorm2d
        inplanes = 3
        self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False)
        self.bn1 = norm_layer(inplanes)
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.downsample = torch.nn.Identity()
        self.myop = nn.quantized.FloatFunctional()
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))


    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        identity = self.downsample(x)
        out = self.myop.add(out, identity)
        out = self.relu2(out)
        out = self.avgpool(out)
        return out

class ModelMultipleOps(torch.nn.Module):
    def __init__(self):
        super().__init__()
        norm_layer = nn.BatchNorm2d
        inplanes = 3
        self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False)
        self.conv2 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False)
        self.bn1 = norm_layer(inplanes)
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.downsample = torch.nn.Identity()
        self.skip_add = nn.quantized.FloatFunctional()
        self.cat = nn.quantized.FloatFunctional()
        self.avgpool = nn.AdaptiveAvgPool2d((4, 4))
        self.fc = nn.Linear(12, 6)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        identity = self.downsample(x)
        out = self.skip_add.add(out, identity)
        out = self.relu2(out)
        out = self.avgpool(out)
        out = self.conv2(out)
        out = torch.nn.functional.max_pool2d(out, 2, 2)
        out = self.cat.cat([out, out])
        out = out.reshape(-1, 3 * 2 * 2)
        out = self.fc(out)
        return out

# Model to ensure consistency of fake quant with true quant
# Average pooling and mean operations are not modelled
# accurately with fake-quant so this model does not
# contain those operations
class ModelMultipleOpsNoAvgPool(torch.nn.Module):
    def __init__(self):
        super().__init__()
        norm_layer = nn.BatchNorm2d
        inplanes = 3
        self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False)
        self.conv2 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False)
        self.bn1 = norm_layer(inplanes)
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.skip_add = nn.quantized.FloatFunctional()
        self.cat = nn.quantized.FloatFunctional()
        self.maxpool = nn.MaxPool2d((4, 4))
        self.fc = nn.Linear(12, 6)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        skip = self.conv2(x)
        out = self.skip_add.add(out, skip)
        out = self.relu2(out)
        out = self.maxpool(out)
        out = self.conv2(out)
        out = torch.nn.functional.max_pool2d(out, 2, 2)
        out = self.cat.cat([out, out])
        out = out.reshape(-1, 3 * 2 * 2)
        out = self.fc(out)
        return out

class EmbeddingBagModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.emb = torch.nn.EmbeddingBag(num_embeddings=10, embedding_dim=12,
                                         include_last_offset=True, scale_grad_by_freq=False, mode='sum')

    def forward(self, indices, offsets, per_sample_weights):
        return self.emb(indices, offsets, per_sample_weights)

class EmbeddingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.emb = torch.nn.Embedding(num_embeddings=10, embedding_dim=12)

    def forward(self, indices):
        return self.emb(indices)

class EmbeddingWithLinear(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.emb = torch.nn.Embedding(num_embeddings=10, embedding_dim=12)
        self.fc = torch.nn.Linear(5, 5)
        self.emb.qconfig = float_qparams_dynamic_qconfig
        self.qconfig = default_qconfig

    def forward(self, indices, linear_in):
        return self.emb(indices), self.fc(linear_in)