import torch.nn as nn
import torch
import numpy as np

'''
---- 1) FLOPs: floating point operations
---- 2) #Activations: the number of elements of all ‘Conv2d’ outputs
---- 3) #Conv2d: the number of ‘Conv2d’ layers
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 21/July/2020
# --------------------------------------------
# Reference
https://github.com/sovrasov/flops-counter.pytorch.git
# If you use this code, please consider the following citation:
@inproceedings{zhang2020aim, % 
  title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},
  author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},
  booktitle={European Conference on Computer Vision Workshops},
  year={2020}
}
# --------------------------------------------
'''

def get_model_flops(model, input_res, print_per_layer_stat=True,
                              input_constructor=None):
    assert type(input_res) is tuple, 'Please provide the size of the input image.'
    assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
    flops_model = add_flops_counting_methods(model)
    flops_model.eval().start_flops_count()
    if input_constructor:
        input = input_constructor(input_res)
        _ = flops_model(**input)
    else:
        device = list(flops_model.parameters())[-1].device       
        batch = torch.Tensor(1, *input_res).type(next(model.parameters()).dtype).to(device)
        _ = flops_model(batch)

    if print_per_layer_stat:
        print_model_with_flops(flops_model)
    flops_count = flops_model.compute_average_flops_cost()
    flops_model.stop_flops_count()

    return flops_count

def get_model_activation(model, input_res, input_constructor=None):
    assert type(input_res) is tuple, 'Please provide the size of the input image.'
    assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
    activation_model = add_activation_counting_methods(model)
    activation_model.eval().start_activation_count()
    if input_constructor:
        input = input_constructor(input_res)
        _ = activation_model(**input)
    else:
        device = list(activation_model.parameters())[-1].device
        batch = torch.FloatTensor(1, *input_res).to(device)
        _ = activation_model(batch)

    activation_count, num_conv = activation_model.compute_average_activation_cost()
    activation_model.stop_activation_count()

    return activation_count, num_conv


def get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True,
                              input_constructor=None):
    assert type(input_res) is tuple
    assert len(input_res) >= 3
    flops_model = add_flops_counting_methods(model)
    flops_model.eval().start_flops_count()
    if input_constructor:
        input = input_constructor(input_res)
        _ = flops_model(**input)
    else:
        batch = torch.FloatTensor(1, *input_res)
        _ = flops_model(batch)

    if print_per_layer_stat:
        print_model_with_flops(flops_model)
    flops_count = flops_model.compute_average_flops_cost()
    params_count = get_model_parameters_number(flops_model)
    flops_model.stop_flops_count()

    if as_strings:
        return flops_to_string(flops_count), params_to_string(params_count)

    return flops_count, params_count


def flops_to_string(flops, units='GMac', precision=2):
    if units is None:
        if flops // 10**9 > 0:
            return str(round(flops / 10.**9, precision)) + ' GMac'
        elif flops // 10**6 > 0:
            return str(round(flops / 10.**6, precision)) + ' MMac'
        elif flops // 10**3 > 0:
            return str(round(flops / 10.**3, precision)) + ' KMac'
        else:
            return str(flops) + ' Mac'
    else:
        if units == 'GMac':
            return str(round(flops / 10.**9, precision)) + ' ' + units
        elif units == 'MMac':
            return str(round(flops / 10.**6, precision)) + ' ' + units
        elif units == 'KMac':
            return str(round(flops / 10.**3, precision)) + ' ' + units
        else:
            return str(flops) + ' Mac'


def params_to_string(params_num):
    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)


def print_model_with_flops(model, units='GMac', precision=3):
    total_flops = model.compute_average_flops_cost()

    def accumulate_flops(self):
        if is_supported_instance(self):
            return self.__flops__ / model.__batch_counter__
        else:
            sum = 0
            for m in self.children():
                sum += m.accumulate_flops()
            return sum

    def flops_repr(self):
        accumulated_flops_cost = self.accumulate_flops()
        return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision),
                          '{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
                          self.original_extra_repr()])

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

    def del_extra_repr(m):
        if hasattr(m, 'original_extra_repr'):
            m.extra_repr = m.original_extra_repr
            del m.original_extra_repr
        if hasattr(m, 'accumulate_flops'):
            del m.accumulate_flops

    model.apply(add_extra_repr)
    print(model)
    model.apply(del_extra_repr)


def get_model_parameters_number(model):
    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return params_num


def add_flops_counting_methods(net_main_module):
    # adding additional methods to the existing module object,
    # this is done this way so that each function has access to self object
    # embed()
    net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
    net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
    net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
    net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)

    net_main_module.reset_flops_count()
    return net_main_module


def compute_average_flops_cost(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.
    Returns current mean flops consumption per image.
    """

    flops_sum = 0
    for module in self.modules():
        if is_supported_instance(module):
            flops_sum += module.__flops__

    return flops_sum


def start_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.
    Activates the computation of mean flops consumption per image.
    Call it before you run the network.
    """
    self.apply(add_flops_counter_hook_function)


def stop_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.
    Stops computing the mean flops consumption per image.
    Call whenever you want to pause the computation.
    """
    self.apply(remove_flops_counter_hook_function)


def reset_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.
    Resets statistics computed so far.
    """
    self.apply(add_flops_counter_variable_or_reset)


def add_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops_handle__'):
            return

        if isinstance(module, (nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d)):
            handle = module.register_forward_hook(conv_flops_counter_hook)
        elif isinstance(module, (nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6)):
            handle = module.register_forward_hook(relu_flops_counter_hook)
        elif isinstance(module, nn.Linear):
            handle = module.register_forward_hook(linear_flops_counter_hook)
        elif isinstance(module, (nn.BatchNorm2d)):
            handle = module.register_forward_hook(bn_flops_counter_hook)
        else:
            handle = module.register_forward_hook(empty_flops_counter_hook)
        module.__flops_handle__ = handle


def remove_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops_handle__'):
            module.__flops_handle__.remove()
            del module.__flops_handle__


def add_flops_counter_variable_or_reset(module):
    if is_supported_instance(module):
        module.__flops__ = 0


# ---- Internal functions
def is_supported_instance(module):
    if isinstance(module,
                  (
                          nn.Conv2d, nn.ConvTranspose2d,
                          nn.BatchNorm2d,
                          nn.Linear,
                          nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6,
                  )):
        return True

    return False


def conv_flops_counter_hook(conv_module, input, output):
    # Can have multiple inputs, getting the first one
    # input = input[0]

    batch_size = output.shape[0]
    output_dims = list(output.shape[2:])

    kernel_dims = list(conv_module.kernel_size)
    in_channels = conv_module.in_channels
    out_channels = conv_module.out_channels
    groups = conv_module.groups

    filters_per_channel = out_channels // groups
    conv_per_position_flops = np.prod(kernel_dims) * in_channels * filters_per_channel

    active_elements_count = batch_size * np.prod(output_dims)
    overall_conv_flops = int(conv_per_position_flops) * int(active_elements_count)

    # overall_flops = overall_conv_flops

    conv_module.__flops__ += int(overall_conv_flops)
    # conv_module.__output_dims__ = output_dims


def relu_flops_counter_hook(module, input, output):
    active_elements_count = output.numel()
    module.__flops__ += int(active_elements_count)
    # print(module.__flops__, id(module))
    # print(module)


def linear_flops_counter_hook(module, input, output):
    input = input[0]
    if len(input.shape) == 1:
        batch_size = 1
        module.__flops__ += int(batch_size * input.shape[0] * output.shape[0])
    else:
        batch_size = input.shape[0]
        module.__flops__ += int(batch_size * input.shape[1] * output.shape[1])


def bn_flops_counter_hook(module, input, output):
    # input = input[0]
    # TODO: need to check here
    # batch_flops = np.prod(input.shape)
    # if module.affine:
    #     batch_flops *= 2
    # module.__flops__ += int(batch_flops)
    batch = output.shape[0]
    output_dims = output.shape[2:]
    channels = module.num_features
    batch_flops = batch * channels * np.prod(output_dims)
    if module.affine:
        batch_flops *= 2
    module.__flops__ += int(batch_flops)


# ---- Count the number of convolutional layers and the activation
def add_activation_counting_methods(net_main_module):
    # adding additional methods to the existing module object,
    # this is done this way so that each function has access to self object
    # embed()
    net_main_module.start_activation_count = start_activation_count.__get__(net_main_module)
    net_main_module.stop_activation_count = stop_activation_count.__get__(net_main_module)
    net_main_module.reset_activation_count = reset_activation_count.__get__(net_main_module)
    net_main_module.compute_average_activation_cost = compute_average_activation_cost.__get__(net_main_module)

    net_main_module.reset_activation_count()
    return net_main_module


def compute_average_activation_cost(self):
    """
    A method that will be available after add_activation_counting_methods() is called
    on a desired net object.
    Returns current mean activation consumption per image.
    """

    activation_sum = 0
    num_conv = 0
    for module in self.modules():
        if is_supported_instance_for_activation(module):
            activation_sum += module.__activation__
            num_conv += module.__num_conv__
    return activation_sum, num_conv


def start_activation_count(self):
    """
    A method that will be available after add_activation_counting_methods() is called
    on a desired net object.
    Activates the computation of mean activation consumption per image.
    Call it before you run the network.
    """
    self.apply(add_activation_counter_hook_function)


def stop_activation_count(self):
    """
    A method that will be available after add_activation_counting_methods() is called
    on a desired net object.
    Stops computing the mean activation consumption per image.
    Call whenever you want to pause the computation.
    """
    self.apply(remove_activation_counter_hook_function)


def reset_activation_count(self):
    """
    A method that will be available after add_activation_counting_methods() is called
    on a desired net object.
    Resets statistics computed so far.
    """
    self.apply(add_activation_counter_variable_or_reset)


def add_activation_counter_hook_function(module):
    if is_supported_instance_for_activation(module):
        if hasattr(module, '__activation_handle__'):
            return

        if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
            handle = module.register_forward_hook(conv_activation_counter_hook)
            module.__activation_handle__ = handle


def remove_activation_counter_hook_function(module):
    if is_supported_instance_for_activation(module):
        if hasattr(module, '__activation_handle__'):
            module.__activation_handle__.remove()
            del module.__activation_handle__


def add_activation_counter_variable_or_reset(module):
    if is_supported_instance_for_activation(module):
        module.__activation__ = 0
        module.__num_conv__ = 0


def is_supported_instance_for_activation(module):
    if isinstance(module,
                  (
                          nn.Conv2d, nn.ConvTranspose2d, nn.Conv1d, nn.Linear, nn.ConvTranspose1d
                  )):
        return True

    return False

def conv_activation_counter_hook(module, input, output):
    """
    Calculate the activations in the convolutional operation.
    Reference: Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár, Designing Network Design Spaces.
    :param module:
    :param input:
    :param output:
    :return:
    """
    module.__activation__ += output.numel()
    module.__num_conv__ += 1


def empty_flops_counter_hook(module, input, output):
    module.__flops__ += 0


def upsample_flops_counter_hook(module, input, output):
    output_size = output[0]
    batch_size = output_size.shape[0]
    output_elements_count = batch_size
    for val in output_size.shape[1:]:
        output_elements_count *= val
    module.__flops__ += int(output_elements_count)


def pool_flops_counter_hook(module, input, output):
    input = input[0]
    module.__flops__ += int(np.prod(input.shape))


def dconv_flops_counter_hook(dconv_module, input, output):
    input = input[0]

    batch_size = input.shape[0]
    output_dims = list(output.shape[2:])

    m_channels, in_channels, kernel_dim1, _, = dconv_module.weight.shape
    out_channels, _, kernel_dim2, _, = dconv_module.projection.shape
    # groups = dconv_module.groups

    # filters_per_channel = out_channels // groups
    conv_per_position_flops1 = kernel_dim1 ** 2 * in_channels * m_channels
    conv_per_position_flops2 = kernel_dim2 ** 2 * out_channels * m_channels
    active_elements_count = batch_size * np.prod(output_dims)

    overall_conv_flops = (conv_per_position_flops1 + conv_per_position_flops2) * active_elements_count
    overall_flops = overall_conv_flops

    dconv_module.__flops__ += int(overall_flops)
    # dconv_module.__output_dims__ = output_dims

def get_params_flops(model, input_dim=(1, 256, 256)):
    input_dim = input_dim
    flops = get_model_flops(model, input_dim, print_per_layer_stat=False)
    flops = flops / 10 ** 9
    num_parameters = sum(map(lambda x: x.numel(), model.parameters()))
    num_parameters = num_parameters / 10 ** 6
    return flops, num_parameters