import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

module_flop_count = []
old_functions = {}


class FlopsProfiler(object):
    """Measures the latency, number of estimated floating point operations and parameters of each module in a PyTorch model.

    The flops-profiler profiles the forward pass of a PyTorch model and prints the model graph with the measured profile attached to each module. It shows how latency, flops and parameters are spent in the model and which modules or layers could be the bottleneck. It also outputs the names of the top k modules in terms of aggregated latency, flops, and parameters at depth l with k and l specified by the user. The output profile is computed for each batch of input.
    The DeepSpeed flops profiler can be used with the DeepSpeed runtime or as a standalone package.
    When using DeepSpeed for model training, the flops profiler can be configured in the deepspeed_config file and no user code change is required.

    If using the profiler as a standalone package, one imports the flops_profiler package and use the APIs.

    Here is an example for usage in a typical training workflow:

        .. code-block:: python

            model = Model()
            prof = FlopsProfiler(model)

            for step, batch in enumerate(data_loader):
                if step == profile_step:
                    prof.start_profile()

                loss = model(batch)

                if step == profile_step:
                    flops = prof.get_total_flops(as_string=True)
                    params = prof.get_total_params(as_string=True)
                    prof.print_model_profile(profile_step=profile_step)
                    prof.end_profile()

                loss.backward()
                optimizer.step()

    To profile a trained model in inference, use the `get_model_profile` API.

    Args:
        object (torch.nn.Module): The PyTorch model to profile.
    """
    def __init__(self, model):
        self.model = model

    def start_profile(self, ignore_list=None):
        """Starts profiling.

        Extra attributes are added recursively to all the modules and the profiled torch.nn.functionals are monkey patched.

        Args:
            ignore_list (list, optional): the list of modules to ignore while profiling. Defaults to None.
        """
        self.reset_profile()
        _patch_functionals()

        def register_module_hooks(module, ignore_list):
            if ignore_list and type(module) in ignore_list:
                return

            # if computing the flops of a module directly
            if type(module) in MODULE_HOOK_MAPPING:
                module.__flops_handle__ = module.register_forward_hook(
                    MODULE_HOOK_MAPPING[type(module)])
                return

            # if computing the flops of the functionals in a module
            def pre_hook(module, input):
                module_flop_count.append([])

            module.__pre_hook_handle__ = module.register_forward_pre_hook(pre_hook)

            def post_hook(module, input, output):
                if module_flop_count:
                    module.__flops__ += sum([elem[1] for elem in module_flop_count[-1]])
                    module_flop_count.pop()

            module.__post_hook_handle__ = module.register_forward_hook(post_hook)

            def start_time_hook(module, input):
                module.__start_time__ = time.time()

            module.__start_time_hook_handle__ = module.register_forward_pre_hook(
                start_time_hook)

            def end_time_hook(module, input, output):
                module.__duration__ += time.time() - module.__start_time__

            module.__end_time_hook_handle__ = module.register_forward_hook(end_time_hook)

        self.model.apply(partial(register_module_hooks, ignore_list=ignore_list))

    def end_profile(self):
        """Ends profiling.

        Added attributes and handles are removed recursively on all the modules and the torch.nn.functionals are restored.
        """
        def remove_profile_attrs(module):
            if hasattr(module, "__flops__"):
                del module.__flops__
            if hasattr(module, "__params__"):
                del module.__params__
            if hasattr(module, "__start_time__"):
                del module.__start_time__
            if hasattr(module, "__duration__"):
                del module.__duration__
            if hasattr(module, "__pre_hook_handle__"):
                module.__pre_hook_handle__.remove()
                del module.__pre_hook_handle__
            if hasattr(module, "__post_hook_handle__"):
                module.__post_hook_handle__.remove()
                del module.__post_hook_handle__
            if hasattr(module, "__flops_handle__"):
                module.__flops_handle__.remove()
                del module.__flops_handle__
            if hasattr(module, "__start_time_hook_handle__"):
                module.__start_time_hook_handle__.remove()
                del module.__start_time_hook_handle__
            if hasattr(module, "__end_time_hook_handle__"):
                module.__end_time_hook_handle__.remove()
                del module.__end_time_hook_handle__

        self.model.apply(remove_profile_attrs)
        _reload_functionals()

    def reset_profile(self):
        """Resets the profiling.

        Adds or resets the extra attributes.
        """
        def add_or_reset_attrs(module):
            module.__flops__ = 0
            module.__params__ = sum(p.numel() for p in module.parameters()
                                    if p.requires_grad)
            module.__start_time__ = 0
            module.__duration__ = 0

        self.model.apply(add_or_reset_attrs)

    def get_total_flops(self, as_string=False):
        """Returns the total flops of the model.

        Args:
            as_string (bool, optional): whether to output the flops as string. Defaults to False.

        Returns:
            The number of multiply-accumulate operations of the model forward pass.
        """
        total_flops = get_module_flops(self.model)
        return macs_to_string(total_flops) if as_string else total_flops

    def get_total_duration(self, as_string=False):
        """Returns the total duration of the model forward pass.

        Args:
            as_string (bool, optional): whether to output the duration as string. Defaults to False.

        Returns:
            The latency of the model forward pass.
        """
        total_duration = self.model.__duration__
        return duration_to_string(total_duration) if as_string else total_duration

    def get_total_params(self, as_string=False):
        """Returns the total parameters of the model.

        Args:
            as_string (bool, optional): whether to output the parameters as string. Defaults to False.

        Returns:
            The number of parameters in the model.
        """
        return params_to_string(
            self.model.__params__) if as_string else self.model.__params__

    def print_model_profile(self,
                            profile_step=1,
                            module_depth=-1,
                            top_modules=3,
                            detailed=True):
        """Prints the model graph with the measured profile attached to each module.

        Args:
            profile_step (int, optional): The global training step at which to profile. Note that warm up steps are needed for accurate time measurement.
            module_depth (int, optional): The depth of the model at which to print the aggregated module information. When set to -1, it prints information on the innermost modules (with the maximum depth).
            top_modules (int, optional): Limits the aggregated profile output to the number of top modules specified.
            detailed (bool, optional): Whether to print the detailed model profile.
        """

        total_flops = self.get_total_flops()
        total_duration = self.get_total_duration()
        total_params = self.get_total_params()

        self.flops = total_flops
        self.params = total_params

        print(
            "\n-------------------------- DeepSpeed Flops Profiler --------------------------"
        )
        print("Summary of forward pass:")
        print('{:<30}  {:<8}'.format('Profile step: ', profile_step))
        print('{:<30}  {:<8}'.format('Number of parameters: ',
                                     params_to_string(total_params)))
        print('{:<30}  {:<8}'.format('Number of multiply-accumulate operations (MACs): ',
                                     num_to_string(total_flops)))
        print('{:<30}  {:<8}'.format(
            'Number of floating point operations ( = 2 * MACs): ',
            num_to_string(2 * total_flops)))
        print('{:<30}  {:<8}'.format('Latency: ', duration_to_string(total_duration)))
        print('{:<30}  {:<8}'.format('Floating point operations per second(FLOPS): ',
                                     flops_to_string(2 * total_flops / total_duration)))

        def flops_repr(module):
            params = module.__params__
            flops = get_module_flops(module)
            items = [
                params_to_string(params),
                "{:.2%} Params".format(params / total_params),
                macs_to_string(flops),
                "{:.2%} MACs".format(0.0 if total_flops == 0 else flops / total_flops),
            ]
            duration = module.__duration__
            if duration == 0:  # e.g. ModuleList
                for m in module.children():
                    duration += m.__duration__

            items.append(duration_to_string(duration))
            items.append(
                "{:.2%} latency".format(0.0 if total_duration == 0 else duration /
                                        total_duration))
            # flops = 2 * MACs
            items.append(flops_to_string(0.0 if duration == 0 else 2 * flops / duration))
            items.append(module.original_extra_repr())
            return ", ".join(items)

        def add_extra_repr(module):
            flops_extra_repr = flops_repr.__get__(module)
            if module.extra_repr != flops_extra_repr:
                module.original_extra_repr = module.extra_repr
                module.extra_repr = flops_extra_repr
                assert module.extra_repr != module.original_extra_repr

        def del_extra_repr(module):
            if hasattr(module, "original_extra_repr"):
                module.extra_repr = module.original_extra_repr
                del module.original_extra_repr

        self.model.apply(add_extra_repr)

        print(
            "\n----------------------------- Aggregated Profile -----------------------------"
        )
        self.print_model_aggregated_profile(module_depth=module_depth,
                                            top_modules=top_modules)

        if detailed:
            print(
                "\n------------------------------ Detailed Profile ------------------------------"
            )
            print(
                "Each module profile is listed after its name in the following order: \nnumber of parameters, percentage of total parameters, number of multiply-accumulate operations (MACs), percentage of total MACs, latency, percentage of total latency, number of floating point operations per second (FLOPS, computed as 2 * MACs / latency)."
            )
            print(
                "Note: \n1. A module can have torch.nn.functional (e.g. to compute logits) along with submodules, thus making the difference between the parent's MACs(or latency) and the sum of its submodules'.\n2. Number of floating point operations is a theoretical estimation, thus FLOPS computed using that could be larger than the maximum system throughput.\n"
            )
            print(self.model)

        self.model.apply(del_extra_repr)

        print(
            "------------------------------------------------------------------------------"
        )

    def print_model_aggregated_profile(self, module_depth=-1, top_modules=3):
        """Prints the names of the top top_modules modules in terms of aggregated time, flops, and parameters at depth module_depth.

        Args:
            module_depth (int, optional): the depth of the modules to show. Defaults to -1 (the innermost modules).
            top_modules (int, optional): the number of top modules to show. Defaults to 3.
        """
        info = {}
        if not hasattr(self.model, "__flops__"):
            print(
                "no __flops__ attribute in the model, call this function after start_profile and before end_profile"
            )
            return

        def walk_module(module, curr_depth, info):
            if curr_depth not in info:
                info[curr_depth] = {}
            if module.__class__.__name__ not in info[curr_depth]:
                info[curr_depth][module.__class__.__name__] = [
                    0,
                    0,
                    0,
                ]  # flops, params, time
            info[curr_depth][module.__class__.__name__][0] += module.__flops__
            info[curr_depth][module.__class__.__name__][1] += module.__params__
            info[curr_depth][module.__class__.__name__][2] += (module.__duration__)
            has_children = len(module._modules.items()) != 0
            if has_children:
                for child in module.children():
                    walk_module(child, curr_depth + 1, info)

        walk_module(self.model, 0, info)

        depth = module_depth
        if module_depth == -1:
            depth = len(info) - 1

        num_items = min(top_modules, len(info[depth]))

        sort_flops = {
            k: macs_to_string(v[0])
            for k,
            v in sorted(info[depth].items(),
                        key=lambda item: item[1][0],
                        reverse=True)[:num_items]
        }
        sort_params = {
            k: params_to_string(v[1])
            for k,
            v in sorted(info[depth].items(),
                        key=lambda item: item[1][1],
                        reverse=True)[:num_items]
        }
        sort_time = {
            k: duration_to_string(v[2])
            for k,
            v in sorted(info[depth].items(),
                        key=lambda item: item[1][2],
                        reverse=True)[:num_items]
        }
        print(f"Top {num_items} modules in MACs at depth {depth} are {sort_flops}")
        print(f"Top {num_items} modules in params at depth {depth} are {sort_params}")
        print(f"Top {num_items} modules in latency at depth {depth} are {sort_time}")


def _prod(dims):
    p = 1
    for v in dims:
        p *= v
    return p


def _linear_flops_compute(input, weight, bias=None):
    out_features = weight.shape[0]
    return torch.numel(input) * out_features


def _relu_flops_compute(input, inplace=False):
    return torch.numel(input)


def _pool_flops_compute(
    input,
    kernel_size,
    stride=None,
    padding=0,
    ceil_mode=False,
    count_include_pad=True,
    divisor_override=None,
):
    return torch.numel(input)


def _conv_flops_compute(input,
                        weight,
                        bias=None,
                        stride=1,
                        padding=0,
                        dilation=1,
                        groups=1):
    assert weight.shape[1] * groups == input.shape[1]

    batch_size = input.shape[0]
    in_channels = input.shape[1]
    out_channels = weight.shape[0]
    kernel_dims = list(weight.shape[-2:])
    input_dims = list(input.shape[2:])

    paddings = padding if type(padding) is tuple else (padding, padding)
    strides = stride if type(stride) is tuple else (stride, stride)
    dilations = dilation if type(dilation) is tuple else (dilation, dilation)

    output_dims = [0, 0]
    output_dims[0] = (input_dims[0] + 2 * paddings[0] -
                      (dilations[0] * (kernel_dims[0] - 1) + 1)) // strides[0] + 1
    output_dims[1] = (input_dims[1] + 2 * paddings[1] -
                      (dilations[1] * (kernel_dims[1] - 1) + 1)) // strides[1] + 1

    filters_per_channel = out_channels // groups
    conv_per_position_flops = int(_prod(kernel_dims)) * in_channels * filters_per_channel
    active_elements_count = batch_size * int(_prod(output_dims))
    overall_conv_flops = conv_per_position_flops * active_elements_count

    bias_flops = 0
    if bias is not None:
        bias_flops = out_channels * active_elements_count

    overall_flops = overall_conv_flops + bias_flops

    return int(overall_flops)


def _conv_trans_flops_compute(
    input,
    weight,
    bias=None,
    stride=1,
    padding=0,
    output_padding=0,
    groups=1,
    dilation=1,
):
    batch_size = input.shape[0]
    in_channels = input.shape[1]
    out_channels = weight.shape[0]
    kernel_dims = list(weight.shape[-2:])
    input_dims = list(input.shape[2:])

    paddings = padding if type(padding) is tuple else (padding, padding)
    strides = stride if type(stride) is tuple else (stride, stride)
    dilations = dilation if type(dilation) is tuple else (dilation, dilation)

    output_dims = [0, 0]
    output_dims[0] = (input_dims[0] + 2 * paddings[0] -
                      (dilations[0] * (kernel_dims[0] - 1) + 1)) // strides[0] + 1
    output_dims[1] = (input_dims[1] + 2 * paddings[1] -
                      (dilations[1] * (kernel_dims[1] - 1) + 1)) // strides[1] + 1

    filters_per_channel = out_channels // groups
    conv_per_position_flops = int(_prod(kernel_dims)) * in_channels * filters_per_channel
    active_elements_count = batch_size * int(_prod(input_dims))
    overall_conv_flops = conv_per_position_flops * active_elements_count

    bias_flops = 0
    if bias is not None:
        bias_flops = out_channels * batch_size * int(_prod(output_dims))

    overall_flops = overall_conv_flops + bias_flops

    return int(overall_flops)


def _batch_norm_flops_compute(
    input,
    running_mean,
    running_var,
    weight=None,
    bias=None,
    training=False,
    momentum=0.1,
    eps=1e-05,
):
    # assume affine is true
    flops = 2 * torch.numel(input)
    return flops


def _upsample_flops_compute(input,
                            size=None,
                            scale_factor=None,
                            mode="nearest",
                            align_corners=None):
    if size is not None:
        return int(_prod(size))
    assert scale_factor is not None
    flops = torch.numel(input)
    if len(scale_factor) == len(input):
        flops * int(_prod(scale_factor))
    else:
        flops * scale_factor**len(input)
    return flops


def _softmax_flops_compute(input, dim=None, _stacklevel=3, dtype=None):
    return torch.numel(input)


def _embedding_flops_compute(
    input,
    weight,
    padding_idx=None,
    max_norm=None,
    norm_type=2.0,
    scale_grad_by_freq=False,
    sparse=False,
):
    return 0


def _dropout_flops_compute(input, p=0.5, training=True, inplace=False):
    return 0


def wrapFunc(func, funcFlopCompute):
    oldFunc = func
    name = func.__name__
    old_functions[func.__name__] = oldFunc

    def newFunc(*args, **kwds):
        flops = funcFlopCompute(*args, **kwds)
        if module_flop_count:
            module_flop_count[-1].append((name, flops))
        return oldFunc(*args, **kwds)

    return newFunc


def _patch_functionals():
    # FC
    F.linear = wrapFunc(F.linear, _linear_flops_compute)

    # convolutions
    F.conv1d = wrapFunc(F.conv1d, _conv_flops_compute)
    F.conv2d = wrapFunc(F.conv2d, _conv_flops_compute)
    F.conv3d = wrapFunc(F.conv3d, _conv_flops_compute)

    # conv transposed
    F.conv_transpose1d = wrapFunc(F.conv_transpose1d, _conv_trans_flops_compute)
    F.conv_transpose2d = wrapFunc(F.conv_transpose2d, _conv_trans_flops_compute)
    F.conv_transpose3d = wrapFunc(F.conv_transpose3d, _conv_trans_flops_compute)

    # activations
    F.relu = wrapFunc(F.relu, _relu_flops_compute)
    F.prelu = wrapFunc(F.prelu, _relu_flops_compute)
    F.elu = wrapFunc(F.elu, _relu_flops_compute)
    F.leaky_relu = wrapFunc(F.leaky_relu, _relu_flops_compute)
    F.relu6 = wrapFunc(F.relu6, _relu_flops_compute)

    # BatchNorms
    F.batch_norm = wrapFunc(F.batch_norm, _batch_norm_flops_compute)

    # poolings
    F.avg_pool1d = wrapFunc(F.avg_pool1d, _pool_flops_compute)
    F.avg_pool2d = wrapFunc(F.avg_pool2d, _pool_flops_compute)
    F.avg_pool3d = wrapFunc(F.avg_pool3d, _pool_flops_compute)
    F.max_pool1d = wrapFunc(F.max_pool1d, _pool_flops_compute)
    F.max_pool2d = wrapFunc(F.max_pool2d, _pool_flops_compute)
    F.max_pool3d = wrapFunc(F.max_pool3d, _pool_flops_compute)
    F.adaptive_avg_pool1d = wrapFunc(F.adaptive_avg_pool1d, _pool_flops_compute)
    F.adaptive_avg_pool2d = wrapFunc(F.adaptive_avg_pool2d, _pool_flops_compute)
    F.adaptive_avg_pool3d = wrapFunc(F.adaptive_avg_pool3d, _pool_flops_compute)
    F.adaptive_max_pool1d = wrapFunc(F.adaptive_max_pool1d, _pool_flops_compute)
    F.adaptive_max_pool2d = wrapFunc(F.adaptive_max_pool2d, _pool_flops_compute)
    F.adaptive_max_pool3d = wrapFunc(F.adaptive_max_pool3d, _pool_flops_compute)

    # upsample
    F.upsample = wrapFunc(F.upsample, _upsample_flops_compute)
    F.interpolate = wrapFunc(F.interpolate, _upsample_flops_compute)

    # softmax
    F.softmax = wrapFunc(F.softmax, _softmax_flops_compute)

    # embedding
    F.embedding = wrapFunc(F.embedding, _embedding_flops_compute)


def _reload_functionals():
    # torch.nn.functional does not support importlib.reload()
    F.linear = old_functions["linear"]
    F.conv1d = old_functions["conv1d"]
    F.conv2d = old_functions["conv2d"]
    F.conv3d = old_functions["conv3d"]
    F.conv_transpose1d = old_functions["conv_transpose1d"]
    F.conv_transpose2d = old_functions["conv_transpose2d"]
    F.conv_transpose3d = old_functions["conv_transpose3d"]
    F.relu = old_functions["relu"]
    F.prelu = old_functions["prelu"]
    F.elu = old_functions["elu"]
    F.leaky_relu = old_functions["leaky_relu"]
    F.relu6 = old_functions["relu6"]
    F.batch_norm = old_functions["batch_norm"]
    F.avg_pool1d = old_functions["avg_pool1d"]
    F.avg_pool2d = old_functions["avg_pool2d"]
    F.avg_pool3d = old_functions["avg_pool3d"]
    F.max_pool1d = old_functions["max_pool1d"]
    F.max_pool2d = old_functions["max_pool2d"]
    F.max_pool3d = old_functions["max_pool3d"]
    F.adaptive_avg_pool1d = old_functions["adaptive_avg_pool1d"]
    F.adaptive_avg_pool2d = old_functions["adaptive_avg_pool2d"]
    F.adaptive_avg_pool3d = old_functions["adaptive_avg_pool3d"]
    F.adaptive_max_pool1d = old_functions["adaptive_max_pool1d"]
    F.adaptive_max_pool2d = old_functions["adaptive_max_pool2d"]
    F.adaptive_max_pool3d = old_functions["adaptive_max_pool3d"]
    F.upsample = old_functions["upsample"]
    F.interpolate = old_functions["interpolate"]
    F.softmax = old_functions["softmax"]
    F.embedding = old_functions["embedding"]


def _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
    # matrix matrix mult ih state and internal state
    flops += w_ih.shape[0] * w_ih.shape[1]
    # matrix matrix mult hh state and internal state
    flops += w_hh.shape[0] * w_hh.shape[1]
    if isinstance(rnn_module, (nn.RNN, nn.RNNCell)):
        # add both operations
        flops += rnn_module.hidden_size
    elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)):
        # hadamard of r
        flops += rnn_module.hidden_size
        # adding operations from both states
        flops += rnn_module.hidden_size * 3
        # last two hadamard _product and add
        flops += rnn_module.hidden_size * 3
    elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)):
        # adding operations from both states
        flops += rnn_module.hidden_size * 4
        # two hadamard _product and add for C state
        flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
        # final hadamard
        flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
    return flops


def _rnn_forward_hook(rnn_module, input, output):
    flops = 0
    # input is a tuple containing a sequence to process and (optionally) hidden state
    inp = input[0]
    batch_size = inp.shape[0]
    seq_length = inp.shape[1]
    num_layers = rnn_module.num_layers

    for i in range(num_layers):
        w_ih = rnn_module.__getattr__("weight_ih_l" + str(i))
        w_hh = rnn_module.__getattr__("weight_hh_l" + str(i))
        if i == 0:
            input_size = rnn_module.input_size
        else:
            input_size = rnn_module.hidden_size
        flops = _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size)
        if rnn_module.bias:
            b_ih = rnn_module.__getattr__("bias_ih_l" + str(i))
            b_hh = rnn_module.__getattr__("bias_hh_l" + str(i))
            flops += b_ih.shape[0] + b_hh.shape[0]

    flops *= batch_size
    flops *= seq_length
    if rnn_module.bidirectional:
        flops *= 2
    rnn_module.__flops__ += int(flops)


def _rnn_cell_forward_hook(rnn_cell_module, input, output):
    flops = 0
    inp = input[0]
    batch_size = inp.shape[0]
    w_ih = rnn_cell_module.__getattr__("weight_ih")
    w_hh = rnn_cell_module.__getattr__("weight_hh")
    input_size = inp.shape[1]
    flops = _rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size)
    if rnn_cell_module.bias:
        b_ih = rnn_cell_module.__getattr__("bias_ih")
        b_hh = rnn_cell_module.__getattr__("bias_hh")
        flops += b_ih.shape[0] + b_hh.shape[0]

    flops *= batch_size
    rnn_cell_module.__flops__ += int(flops)


MODULE_HOOK_MAPPING = {
    # RNN
    nn.RNN: _rnn_forward_hook,
    nn.GRU: _rnn_forward_hook,
    nn.LSTM: _rnn_forward_hook,
    nn.RNNCell: _rnn_cell_forward_hook,
    nn.LSTMCell: _rnn_cell_forward_hook,
    nn.GRUCell: _rnn_cell_forward_hook,
}


def num_to_string(num, precision=2):
    if num // 10**9 > 0:
        return str(round(num / 10.0**9, precision)) + " G"
    elif num // 10**6 > 0:
        return str(round(num / 10.0**6, precision)) + " M"
    elif num // 10**3 > 0:
        return str(round(num / 10.0**3, precision)) + " K"
    else:
        return str(num)


def macs_to_string(macs, units=None, precision=2):
    if units is None:
        if macs // 10**9 > 0:
            return str(round(macs / 10.0**9, precision)) + " GMACs"
        elif macs // 10**6 > 0:
            return str(round(macs / 10.0**6, precision)) + " MMACs"
        elif macs // 10**3 > 0:
            return str(round(macs / 10.0**3, precision)) + " KMACs"
        else:
            return str(macs) + " MACs"
    else:
        if units == "GMACs":
            return str(round(macs / 10.0**9, precision)) + " " + units
        elif units == "MMACs":
            return str(round(macs / 10.0**6, precision)) + " " + units
        elif units == "KMACs":
            return str(round(macs / 10.0**3, precision)) + " " + units
        else:
            return str(macs) + " MACs"


def flops_to_string(flops, units=None, precision=2):
    if units is None:
        if flops // 10**12 > 0:
            return str(round(flops / 10.0**12, precision)) + " TFLOPS"
        if flops // 10**9 > 0:
            return str(round(flops / 10.0**9, precision)) + " GFLOPS"
        elif flops // 10**6 > 0:
            return str(round(flops / 10.0**6, precision)) + " MFLOPS"
        elif flops // 10**3 > 0:
            return str(round(flops / 10.0**3, precision)) + " KFLOPS"
        else:
            return str(flops) + " FLOPS"
    else:
        if units == "TFLOPS":
            return str(round(flops / 10.0**12, precision)) + " " + units
        if units == "GFLOPS":
            return str(round(flops / 10.0**9, precision)) + " " + units
        elif units == "MFLOPS":
            return str(round(flops / 10.0**6, precision)) + " " + units
        elif units == "KFLOPS":
            return str(round(flops / 10.0**3, precision)) + " " + units
        else:
            return str(flops) + " FLOPS"


def params_to_string(params_num, units=None, precision=2):
    if units is None:
        if params_num // 10**6 > 0:
            return str(round(params_num / 10**6, 2)) + " M"
        elif params_num // 10**3:
            return str(round(params_num / 10**3, 2)) + " k"
        else:
            return str(params_num)
    else:
        if units == "M":
            return str(round(params_num / 10.0**6, precision)) + " " + units
        elif units == "K":
            return str(round(params_num / 10.0**3, precision)) + " " + units
        else:
            return str(params_num)


def duration_to_string(duration, units=None, precision=2):
    if units is None:
        if duration > 1:
            return str(round(duration, precision)) + " s"
        elif duration * 10**3 > 1:
            return str(round(duration * 10**3, precision)) + " ms"
        elif duration * 10**6 > 1:
            return str(round(duration * 10**6, precision)) + " us"
        else:
            return str(duration)
    else:
        if units == "us":
            return str(round(duration * 10.0**6, precision)) + " " + units
        elif units == "ms":
            return str(round(duration * 10.0**3, precision)) + " " + units
        else:
            return str(round(duration, precision)) + " s"


    # can not iterate over all submodules using self.model.modules()
    # since modules() returns duplicate modules only once
def get_module_flops(module):
    sum = module.__flops__
    # iterate over immediate children modules
    for child in module.children():
        sum += get_module_flops(child)
    return sum


def get_model_profile(
    model,
    input_res,
    input_constructor=None,
    print_profile=True,
    detailed=True,
    module_depth=-1,
    top_modules=3,
    warm_up=1,
    as_string=True,
    ignore_modules=None,
):
    """Returns the total MACs and parameters of a model.

    Example:

    .. code-block:: python

        model = torchvision.models.alexnet()
        batch_size = 256
        macs, params = get_model_profile(model=model, input_res= (batch_size, 3, 224, 224)))

    Args:
        model ([torch.nn.Module]): the PyTorch model to be profiled.
        input_res (list): input shape or input to the input_constructor
        input_constructor (func, optional): input constructor. If specified, the constructor is applied to input_res and the constructor output is used as the input to the model. Defaults to None.
        print_profile (bool, optional): whether to print the model profile. Defaults to True.
        detailed (bool, optional): whether to print the detailed model profile. Defaults to True.
        module_depth (int, optional): the depth into the nested modules. Defaults to -1 (the inner most modules).
        top_modules (int, optional): the number of top modules to print in the aggregated profile. Defaults to 3.
        warm_up (int, optional): the number of warm-up steps before measuring the latency of each module. Defaults to 1.
        as_string (bool, optional): whether to print the output as string. Defaults to True.
        ignore_modules ([type], optional): the list of modules to ignore during profiling. Defaults to None.

    Returns:
        The number of multiply-accumulate operations (MACs) and parameters in the model.
    """
    assert type(input_res) is tuple
    assert len(input_res) >= 1
    assert isinstance(model, nn.Module)
    prof = FlopsProfiler(model)
    model.eval()
    for _ in range(warm_up):
        if input_constructor:
            input = input_constructor(input_res)
            _ = model(**input)
        else:
            try:
                batch = torch.ones(()).new_empty(
                    (*input_res,
                     ),
                    dtype=next(model.parameters()).dtype,
                    device=next(model.parameters()).device,
                )
            except StopIteration:
                batch = torch.ones(()).new_empty((*input_res, ))
            _ = model(batch)

    prof.start_profile(ignore_list=ignore_modules)

    if input_constructor:
        input = input_constructor(input_res)
        _ = model(**input)
    else:
        try:
            batch = torch.ones(()).new_empty(
                (*input_res,
                 ),
                dtype=next(model.parameters()).dtype,
                device=next(model.parameters()).device,
            )
        except StopIteration:
            batch = torch.ones(()).new_empty((*input_res, ))
        _ = model(batch)

    flops = prof.get_total_flops()
    params = prof.get_total_params()
    if print_profile:
        prof.print_model_profile(profile_step=warm_up,
                                 module_depth=module_depth,
                                 top_modules=top_modules,
                                 detailed=detailed)

    prof.end_profile()
    if as_string:
        return macs_to_string(flops), params_to_string(params)

    return flops, params
