# The implementation here is modified based on EfficientNet,
# originally Apache 2.0 License and publicly available at https://github.com/lukemelas/EfficientNet-PyTorch

import collections
import math
import re
from functools import partial

import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo

GlobalParams = collections.namedtuple('GlobalParams', [
    'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
    'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
    'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'
])

BlockArgs = collections.namedtuple('BlockArgs', [
    'num_repeat', 'kernel_size', 'stride', 'expand_ratio', 'input_filters',
    'output_filters', 'se_ratio', 'id_skip'
])

GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields)

if hasattr(nn, 'SiLU'):
    Swish = nn.SiLU
else:

    class Swish(nn.Module):

        def forward(self, x):
            return x * torch.sigmoid(x)


class SwishImplementation(torch.autograd.Function):

    @staticmethod
    def forward(ctx, i):
        result = i * torch.sigmoid(i)
        ctx.save_for_backward(i)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        i = ctx.saved_tensors[0]
        sigmoid_i = torch.sigmoid(i)
        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))


class MemoryEfficientSwish(nn.Module):

    def forward(self, x):
        return SwishImplementation.apply(x)


def round_filters(filters, global_params):
    """Calculate and round number of filters based on width multiplier.
       Use width_coefficient, depth_divisor and min_depth of global_params.
    Args:
        filters (int): Filters number to be calculated.
        global_params (namedtuple): Global params of the model.
    Returns:
        new_filters: New filters number after calculating.
    """
    multiplier = global_params.width_coefficient
    if not multiplier:
        return filters

    divisor = global_params.depth_divisor
    min_depth = global_params.min_depth
    filters *= multiplier
    min_depth = min_depth or divisor
    new_filters = max(min_depth,
                      int(filters + divisor / 2) // divisor * divisor)
    if new_filters < 0.9 * filters:
        new_filters += divisor
    return int(new_filters)


def round_repeats(repeats, global_params):
    """Calculate module's repeat number of a block based on depth multiplier.
       Use depth_coefficient of global_params.
    Args:
        repeats (int): num_repeat to be calculated.
        global_params (namedtuple): Global params of the model.
    Returns:
        new repeat: New repeat number after calculating.
    """
    multiplier = global_params.depth_coefficient
    if not multiplier:
        return repeats
    return int(math.ceil(multiplier * repeats))


def drop_connect(inputs, p, training):
    """Drop connect.
    Args:
        input (tensor: BCWH): Input of this structure.
        p (float: 0.0~1.0): Probability of drop connection.
        training (bool): The running mode.
    Returns:
        output: Output after drop connection.
    """
    assert 0 <= p <= 1, 'p must be in range of [0,1]'

    if not training:
        return inputs

    batch_size = inputs.shape[0]
    keep_prob = 1 - p

    random_tensor = keep_prob
    random_tensor += torch.rand([batch_size, 1, 1, 1],
                                dtype=inputs.dtype,
                                device=inputs.device)
    binary_tensor = torch.floor(random_tensor)

    output = inputs / keep_prob * binary_tensor
    return output


def get_width_and_height_from_size(x):
    """Obtain height and width from x.
    Args:
        x (int, tuple or list): Data size.
    Returns:
        size: A tuple or list (H,W).
    """
    if isinstance(x, int):
        return x, x
    if isinstance(x, list) or isinstance(x, tuple):
        return x
    else:
        raise TypeError()


def calculate_output_image_size(input_image_size, stride):
    """Calculates the output image size when using Conv2dSamePadding with a stride.
       Necessary for static padding. Thanks to mannatsingh for pointing this out.
    Args:
        input_image_size (int, tuple or list): Size of input image.
        stride (int, tuple or list): Conv2d operation's stride.
    Returns:
        output_image_size: A list [H,W].
    """
    if input_image_size is None:
        return None
    image_height, image_width = get_width_and_height_from_size(
        input_image_size)
    stride = stride if isinstance(stride, int) else stride[0]
    image_height = int(math.ceil(image_height / stride))
    image_width = int(math.ceil(image_width / stride))
    return [image_height, image_width]


def get_same_padding_conv2d(image_size=None):
    """Chooses static padding if you have specified an image size, and dynamic padding otherwise.
       Static padding is necessary for ONNX exporting of models.
    Args:
        image_size (int or tuple): Size of the image.
    Returns:
        Conv2dDynamicSamePadding or Conv2dStaticSamePadding.
    """
    if image_size is None:
        return Conv2dDynamicSamePadding
    else:
        return partial(Conv2dStaticSamePadding, image_size=image_size)


class Conv2dDynamicSamePadding(nn.Conv2d):
    """2D Convolutions like TensorFlow, for a dynamic image size.
       The padding is operated in forward function by calculating dynamically.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 dilation=1,
                 groups=1,
                 bias=True):
        super().__init__(in_channels, out_channels, kernel_size, stride, 0,
                         dilation, groups, bias)
        self.stride = self.stride if len(
            self.stride) == 2 else [self.stride[0]] * 2

    def forward(self, x):
        ih, iw = x.size()[-2:]
        kh, kw = self.weight.size()[-2:]
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        a1 = (oh - 1) * self.stride[0]
        pad_h = max(a1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
        a2 = (ow - 1) * self.stride[1]
        pad_w = max(a2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [
                pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
            ])
        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
                        self.dilation, self.groups)


class Conv2dStaticSamePadding(nn.Conv2d):
    """2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.
       The padding module is calculated in construction function, then used in forward.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 image_size=None,
                 **kwargs):
        super().__init__(in_channels, out_channels, kernel_size, stride,
                         **kwargs)
        self.stride = self.stride if len(
            self.stride) == 2 else [self.stride[0]] * 2

        assert image_size is not None
        ih, iw = (image_size,
                  image_size) if isinstance(image_size, int) else image_size
        kh, kw = self.weight.size()[-2:]
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        b1 = (oh - 1) * self.stride[0]
        pad_h = max(b1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
        b2 = (ow - 1) * self.stride[1]
        pad_w = max(b2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            self.static_padding = nn.ZeroPad2d(
                (pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
                 pad_h - pad_h // 2))
        else:
            self.static_padding = nn.Identity()

    def forward(self, x):
        x = self.static_padding(x)
        x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
                     self.dilation, self.groups)
        return x


def get_same_padding_maxPool2d(image_size=None):
    """Chooses static padding if you have specified an image size, and dynamic padding otherwise.
       Static padding is necessary for ONNX exporting of models.
    Args:
        image_size (int or tuple): Size of the image.
    Returns:
        MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding.
    """
    if image_size is None:
        return MaxPool2dDynamicSamePadding
    else:
        return partial(MaxPool2dStaticSamePadding, image_size=image_size)


class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
    """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
       The padding is operated in forward function by calculating dynamically.
    """

    def __init__(self,
                 kernel_size,
                 stride,
                 padding=0,
                 dilation=1,
                 return_indices=False,
                 ceil_mode=False):
        super().__init__(kernel_size, stride, padding, dilation,
                         return_indices, ceil_mode)
        self.stride = [self.stride] * 2 if isinstance(self.stride,
                                                      int) else self.stride
        self.kernel_size = [self.kernel_size] * 2 if isinstance(
            self.kernel_size, int) else self.kernel_size
        self.dilation = [self.dilation] * 2 if isinstance(
            self.dilation, int) else self.dilation

    def forward(self, x):
        ih, iw = x.size()[-2:]
        kh, kw = self.kernel_size
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        c1 = (oh - 1) * self.stride[0]
        pad_h = max(c1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
        c2 = (ow - 1) * self.stride[1]
        pad_w = max(c2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [
                pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
            ])
        return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
                            self.dilation, self.ceil_mode, self.return_indices)


class MaxPool2dStaticSamePadding(nn.MaxPool2d):
    """2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size.
       The padding module is calculated in construction function, then used in forward.
    """

    def __init__(self, kernel_size, stride, image_size=None, **kwargs):
        super().__init__(kernel_size, stride, **kwargs)
        self.stride = [self.stride] * 2 if isinstance(self.stride,
                                                      int) else self.stride
        self.kernel_size = [self.kernel_size] * 2 if isinstance(
            self.kernel_size, int) else self.kernel_size
        self.dilation = [self.dilation] * 2 if isinstance(
            self.dilation, int) else self.dilation

        assert image_size is not None
        ih, iw = (image_size,
                  image_size) if isinstance(image_size, int) else image_size
        kh, kw = self.kernel_size
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        d1 = (oh - 1) * self.stride[0]
        pad_h = max(d1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
        d2 = (ow - 1) * self.stride[1]
        pad_w = max(d2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            self.static_padding = nn.ZeroPad2d(
                (pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
                 pad_h - pad_h // 2))
        else:
            self.static_padding = nn.Identity()

    def forward(self, x):
        x = self.static_padding(x)
        x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
                         self.dilation, self.ceil_mode, self.return_indices)
        return x


class BlockDecoder(object):
    """Block Decoder for readability,
       straight from the official TensorFlow repository.
    """

    @staticmethod
    def _decode_block_string(block_string):
        """Get a block through a string notation of arguments.
        Args:
            block_string (str): A string notation of arguments.
                                Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.
        Returns:
            BlockArgs: The namedtuple defined at the top of this file.
        """
        assert isinstance(block_string, str)

        ops = block_string.split('_')
        options = {}
        for op in ops:
            splits = re.split(r'(\d.*)', op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

        # Check stride
        assert (('s' in options and len(options['s']) == 1)
                or (len(options['s']) == 2
                    and options['s'][0] == options['s'][1]))

        return BlockArgs(
            num_repeat=int(options['r']),
            kernel_size=int(options['k']),
            stride=[int(options['s'][0])],
            expand_ratio=int(options['e']),
            input_filters=int(options['i']),
            output_filters=int(options['o']),
            se_ratio=float(options['se']) if 'se' in options else None,
            id_skip=('noskip' not in block_string))

    @staticmethod
    def _encode_block_string(block):
        """Encode a block to a string.
        Args:
            block (namedtuple): A BlockArgs type argument.
        Returns:
            block_string: A String form of BlockArgs.
        """
        args = [
            'r%d' % block.num_repeat,
            'k%d' % block.kernel_size,
            's%d%d' % (block.strides[0], block.strides[1]),
            'e%s' % block.expand_ratio,
            'i%d' % block.input_filters,
            'o%d' % block.output_filters
        ]
        if 0 < block.se_ratio <= 1:
            args.append('se%s' % block.se_ratio)
        if block.id_skip is False:
            args.append('noskip')
        return '_'.join(args)

    @staticmethod
    def decode(string_list):
        """Decode a list of string notations to specify blocks inside the network.
        Args:
            string_list (list[str]): A list of strings, each string is a notation of block.
        Returns:
            blocks_args: A list of BlockArgs namedtuples of block args.
        """
        assert isinstance(string_list, list)
        blocks_args = []
        for block_string in string_list:
            blocks_args.append(BlockDecoder._decode_block_string(block_string))
        return blocks_args

    @staticmethod
    def encode(blocks_args):
        """Encode a list of BlockArgs to a list of strings.
        Args:
            blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.
        Returns:
            block_strings: A list of strings, each string is a notation of block.
        """
        block_strings = []
        for block in blocks_args:
            block_strings.append(BlockDecoder._encode_block_string(block))
        return block_strings


def efficientnet_params(model_name):
    """Map EfficientNet model name to parameter coefficients.
    Args:
        model_name (str): Model name to be queried.
    Returns:
        params_dict[model_name]: A (width,depth,res,dropout) tuple.
    """
    params_dict = {
        'efficientnet-b0': (1.0, 1.0, 112, 0.2),
        'efficientnet-b1': (1.0, 1.1, 240, 0.2),
        'efficientnet-b2': (1.1, 1.2, 260, 0.3),
        'efficientnet-b3': (1.2, 1.4, 300, 0.3),
        'efficientnet-b4': (1.4, 1.8, 380, 0.4),
        'efficientnet-b5': (1.6, 2.2, 456, 0.4),
        'efficientnet-b6': (1.8, 2.6, 528, 0.5),
        'efficientnet-b7': (2.0, 3.1, 600, 0.5),
        'efficientnet-b8': (2.2, 3.6, 672, 0.5),
        'efficientnet-l2': (4.3, 5.3, 800, 0.5),
    }
    return params_dict[model_name]


def efficientnet(width_coefficient=None,
                 depth_coefficient=None,
                 image_size=None,
                 dropout_rate=0.2,
                 drop_connect_rate=0.2,
                 num_classes=1000,
                 include_top=True):
    """Create BlockArgs and GlobalParams for efficientnet model.
    Args:
        width_coefficient (float)
        depth_coefficient (float)
        image_size (int)
        dropout_rate (float)
        drop_connect_rate (float)
        num_classes (int)
        Meaning as the name suggests.
    Returns:
        blocks_args, global_params.
    """

    blocks_args = [
        'r1_k3_s11_e1_i32_o16_se0.25',
        'r2_k3_s22_e6_i16_o24_se0.25',
        'r2_k5_s22_e6_i24_o40_se0.25',
        'r3_k3_s22_e6_i40_o80_se0.25',
        'r3_k5_s11_e6_i80_o112_se0.25',
        'r4_k5_s22_e6_i112_o192_se0.25',
        'r1_k3_s11_e6_i192_o320_se0.25',
    ]
    blocks_args = BlockDecoder.decode(blocks_args)

    global_params = GlobalParams(
        width_coefficient=width_coefficient,
        depth_coefficient=depth_coefficient,
        image_size=image_size,
        dropout_rate=dropout_rate,
        num_classes=num_classes,
        batch_norm_momentum=0.99,
        batch_norm_epsilon=1e-3,
        drop_connect_rate=drop_connect_rate,
        depth_divisor=8,
        min_depth=None,
        include_top=include_top,
    )
    return blocks_args, global_params


def get_model_params(model_name, override_params):
    """Get the block args and global params for a given model name.
    Args:
        model_name (str): Model's name.
        override_params (dict): A dict to modify global_params.
    Returns:
        blocks_args, global_params
    """
    if model_name.startswith('efficientnet'):
        w, d, s, p = efficientnet_params(model_name)
        blocks_args, global_params = efficientnet(
            width_coefficient=w,
            depth_coefficient=d,
            dropout_rate=p,
            image_size=s)
    else:
        raise NotImplementedError(
            'model name is not pre-defined: {}'.format(model_name))
    if override_params:
        global_params = global_params._replace(**override_params)
    return blocks_args, global_params


def load_pretrained_weights(model,
                            model_name,
                            weights_path=None,
                            load_fc=True,
                            advprop=False,
                            verbose=True):
    """Loads pretrained weights from weights path or download using url.
    Args:
        model (Module): The whole model of efficientnet.
        model_name (str): Model name of efficientnet.
        weights_path (None or str):
            str: path to pretrained weights file on the local disk.
            None: use pretrained weights downloaded from the Internet.
        load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
        advprop (bool): Whether to load pretrained weights
                        trained with advprop (valid when weights_path is None).
    """
    if isinstance(weights_path, str):
        state_dict = torch.load(weights_path, weights_only=True)
    else:
        url_map_ = url_map_advprop if advprop else url_map
        state_dict = model_zoo.load_url(url_map_[model_name])

    if load_fc:
        ret = model.load_state_dict(state_dict, strict=False)
        assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(
            ret.missing_keys)
    else:
        state_dict.pop('_fc.weight')
        state_dict.pop('_fc.bias')
        ret = model.load_state_dict(state_dict, strict=False)
        assert set(ret.missing_keys) == set([
            '_fc.weight', '_fc.bias'
        ]), 'Missing keys when loading pretrained weights: {}'.format(
            ret.missing_keys)
    assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(
        ret.unexpected_keys)

    if verbose:
        print('Loaded pretrained weights for {}'.format(model_name))