"""RegNet

Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py

Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
and cleaned up with more descriptive variable names.

Weights from original impl have been modified
* first layer from BGR -> RGB as most PyTorch models are
* removed training specific dict entries from checkpoints and keep model state_dict only
* remap names to match the ones here

"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from .registry import register_model
from .helpers import load_pretrained
from .layers import SelectAdaptivePool2d, AvgPool2dSame, ConvBnAct, SEModule
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


def _mcfg(**kwargs):
    cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32)
    cfg.update(**kwargs)
    return cfg


# Model FLOPS = three trailing digits * 10^8
model_cfgs = dict(
    x_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
    x_004=_mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
    x_006=_mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
    x_008=_mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
    x_016=_mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
    x_032=_mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
    x_040=_mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
    x_064=_mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
    x_080=_mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
    x_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
    x_160=_mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
    x_320=_mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
    y_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
    y_004=_mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
    y_006=_mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
    y_008=_mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
    y_016=_mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
    y_032=_mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
    y_040=_mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
    y_064=_mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25),
    y_080=_mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25),
    y_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25),
    y_160=_mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25),
    y_320=_mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25),
)


def _cfg(url=''):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.conv', 'classifier': 'head.fc',
    }


default_cfgs = dict(
    x_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth'),
    x_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth'),
    x_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth'),
    x_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth'),
    x_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth'),
    x_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth'),
    x_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth'),
    x_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth'),
    x_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth'),
    x_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth'),
    x_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth'),
    x_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth'),
    y_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth'),
    y_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth'),
    y_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth'),
    y_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth'),
    y_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth'),
    y_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_032-62b47782.pth'),
    y_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'),
    y_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'),
    y_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth'),
    y_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'),
    y_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth'),
    y_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
)


def quantize_float(f, q):
    """Converts a float to closest non-zero int divisible by q."""
    return int(round(f / q) * q)


def adjust_widths_groups_comp(widths, bottle_ratios, groups):
    """Adjusts the compatibility of widths and groups."""
    bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)]
    groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)]
    bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
    widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)]
    return widths, groups


def generate_regnet(width_slope, width_initial, width_mult, depth, q=8):
    """Generates per block widths from RegNet parameters."""
    assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0
    widths_cont = np.arange(depth) * width_slope + width_initial
    width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
    widths = width_initial * np.power(width_mult, width_exps)
    widths = np.round(np.divide(widths, q)) * q
    num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
    widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
    return widths, num_stages, max_stage, widths_cont


class Bottleneck(nn.Module):
    """ RegNet Bottleneck

    This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
    after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
    """

    def __init__(self, in_chs, out_chs, stride=1, bottleneck_ratio=1, group_width=1, se_ratio=0.25,
                 dilation=1, first_dilation=None, downsample=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
                 aa_layer=None, drop_block=None, drop_path=None):
        super(Bottleneck, self).__init__()
        bottleneck_chs = int(round(out_chs * bottleneck_ratio))
        groups = bottleneck_chs // group_width
        first_dilation = first_dilation or dilation

        cargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block)
        self.conv1 = ConvBnAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
        self.conv2 = ConvBnAct(
            bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=first_dilation,
            groups=groups, **cargs)
        if se_ratio:
            se_channels = int(round(in_chs * se_ratio))
            self.se = SEModule(bottleneck_chs, reduction_channels=se_channels)
        else:
            self.se = None
        cargs['act_layer'] = None
        self.conv3 = ConvBnAct(bottleneck_chs, out_chs, kernel_size=1, **cargs)
        self.act3 = act_layer(inplace=True)
        self.downsample = downsample
        self.drop_path = drop_path

    def zero_init_last_bn(self):
        nn.init.zeros_(self.conv3.bn.weight)

    def forward(self, x):
        shortcut = x
        x = self.conv1(x)
        x = self.conv2(x)
        if self.se is not None:
            x = self.se(x)
        x = self.conv3(x)
        if self.drop_path is not None:
            x = self.drop_path(x)
        if self.downsample is not None:
            shortcut = self.downsample(shortcut)
        x += shortcut
        x = self.act3(x)
        return x


def downsample_conv(
        in_chs, out_chs, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
    norm_layer = norm_layer or nn.BatchNorm2d
    kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
    first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
    return ConvBnAct(
        in_chs, out_chs, kernel_size, stride=stride, dilation=first_dilation, norm_layer=norm_layer, act_layer=None)


def downsample_avg(
        in_chs, out_chs, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
    """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
    norm_layer = norm_layer or nn.BatchNorm2d
    avg_stride = stride if dilation == 1 else 1
    pool = nn.Identity()
    if stride > 1 or dilation > 1:
        avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
        pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
    return nn.Sequential(*[
        pool, ConvBnAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, act_layer=None)])


class RegStage(nn.Module):
    """Stage (sequence of blocks w/ the same output shape)."""

    def __init__(self, in_chs, out_chs, stride, depth, block_fn, bottle_ratio, group_width, se_ratio):
        super(RegStage, self).__init__()
        block_kwargs = {}  # FIXME setup to pass various aa, norm, act layer common args
        for i in range(depth):
            block_stride = stride if i == 0 else 1
            block_in_chs = in_chs if i == 0 else out_chs
            if (block_in_chs != out_chs) or (block_stride != 1):
                proj_block = downsample_conv(block_in_chs, out_chs, 1, stride)
            else:
                proj_block = None

            name = "b{}".format(i + 1)
            self.add_module(
                name, block_fn(
                    block_in_chs, out_chs, block_stride, bottle_ratio, group_width, se_ratio,
                    downsample=proj_block, **block_kwargs)
            )

    def forward(self, x):
        for block in self.children():
            x = block(x)
        return x


class ClassifierHead(nn.Module):
    """Head."""

    def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.):
        super(ClassifierHead, self).__init__()
        self.drop_rate = drop_rate
        self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
        if num_classes > 0:
            self.fc = nn.Linear(in_chs, num_classes, bias=True)
        else:
            self.fc = nn.Identity()

    def forward(self, x):
        x = self.global_pool(x).flatten(1)
        if self.drop_rate:
            x = F.dropout(x, p=float(self.drop_rate), training=self.training)
        x = self.fc(x)
        return x


class RegNet(nn.Module):
    """RegNet model.

    Paper: https://arxiv.org/abs/2003.13678
    Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
    """

    def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.,
                 zero_init_last_bn=True):
        super().__init__()
        # TODO add drop block, drop path, anti-aliasing, custom bn/act args
        self.num_classes = num_classes
        self.drop_rate = drop_rate

        # Construct the stem
        stem_width = cfg['stem_width']
        self.stem = ConvBnAct(in_chans, stem_width, 3, stride=2)
        
        # Construct the stages
        block_fn = Bottleneck
        prev_width = stem_width
        stage_params = self._get_stage_params(cfg)
        se_ratio = cfg['se_ratio']
        for i, (d, w, s, br, gw) in enumerate(stage_params):
            self.add_module(
                "s{}".format(i + 1), RegStage(prev_width, w, s, d, block_fn, br, gw, se_ratio))
            prev_width = w

        # Construct the head
        self.num_features = prev_width
        self.head = ClassifierHead(
            in_chs=prev_width, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, mean=0.0, std=0.01)
                nn.init.zeros_(m.bias)
        if zero_init_last_bn:
            for m in self.modules():
                if hasattr(m, 'zero_init_last_bn'):
                    m.zero_init_last_bn()

    def _get_stage_params(self, cfg, stride=2):
        # Generate RegNet ws per block
        w_a, w_0, w_m, d = cfg['wa'], cfg['w0'], cfg['wm'], cfg['depth']
        widths, num_stages, _, _ = generate_regnet(w_a, w_0, w_m, d)

        # Convert to per stage format
        stage_widths, stage_depths = np.unique(widths, return_counts=True)

        # Use the same group width, bottleneck mult and stride for each stage
        stage_groups = [cfg['group_w'] for _ in range(num_stages)]
        stage_bottle_ratios = [cfg['bottle_ratio'] for _ in range(num_stages)]
        stage_strides = [stride for _ in range(num_stages)]
        # FIXME add dilation / output_stride support

        # Adjust the compatibility of ws and gws
        stage_widths, stage_groups = adjust_widths_groups_comp(stage_widths, stage_bottle_ratios, stage_groups)
        stage_params = list(zip(stage_depths, stage_widths, stage_strides, stage_bottle_ratios, stage_groups))
        return stage_params

    def get_classifier(self):
        return self.head.fc

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)

    def forward_features(self, x):
        for block in list(self.children())[:-1]:
            x = block(x)
        return x

    def forward(self, x):
        for block in self.children():
            x = block(x)
        return x


def _regnet(variant, pretrained, **kwargs):
    load_strict = True
    model_class = RegNet
    if kwargs.pop('features_only', False):
        assert False, 'Not Implemented'  # TODO
        load_strict = False
        kwargs.pop('num_classes', 0)
    model_cfg = model_cfgs[variant]
    default_cfg = default_cfgs[variant]
    model = model_class(model_cfg, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(
            model, default_cfg,
            num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), strict=load_strict)
    return model


@register_model
def regnetx_002(pretrained=False, **kwargs):
    """RegNetX-200MF"""
    return _regnet('x_002', pretrained, **kwargs)


@register_model
def regnetx_004(pretrained=False, **kwargs):
    """RegNetX-400MF"""
    return _regnet('x_004', pretrained, **kwargs)


@register_model
def regnetx_006(pretrained=False, **kwargs):
    """RegNetX-600MF"""
    return _regnet('x_006', pretrained, **kwargs)


@register_model
def regnetx_008(pretrained=False, **kwargs):
    """RegNetX-800MF"""
    return _regnet('x_008', pretrained, **kwargs)


@register_model
def regnetx_016(pretrained=False, **kwargs):
    """RegNetX-1.6GF"""
    return _regnet('x_016', pretrained, **kwargs)


@register_model
def regnetx_032(pretrained=False, **kwargs):
    """RegNetX-3.2GF"""
    return _regnet('x_032', pretrained, **kwargs)


@register_model
def regnetx_040(pretrained=False, **kwargs):
    """RegNetX-4.0GF"""
    return _regnet('x_040', pretrained, **kwargs)


@register_model
def regnetx_064(pretrained=False, **kwargs):
    """RegNetX-6.4GF"""
    return _regnet('x_064', pretrained, **kwargs)


@register_model
def regnetx_080(pretrained=False, **kwargs):
    """RegNetX-8.0GF"""
    return _regnet('x_080', pretrained, **kwargs)


@register_model
def regnetx_120(pretrained=False, **kwargs):
    """RegNetX-12GF"""
    return _regnet('x_120', pretrained, **kwargs)


@register_model
def regnetx_160(pretrained=False, **kwargs):
    """RegNetX-16GF"""
    return _regnet('x_160', pretrained, **kwargs)


@register_model
def regnetx_320(pretrained=False, **kwargs):
    """RegNetX-32GF"""
    return _regnet('x_320', pretrained, **kwargs)


@register_model
def regnety_002(pretrained=False, **kwargs):
    """RegNetY-200MF"""
    return _regnet('y_002', pretrained, **kwargs)


@register_model
def regnety_004(pretrained=False, **kwargs):
    """RegNetY-400MF"""
    return _regnet('y_004', pretrained, **kwargs)


@register_model
def regnety_006(pretrained=False, **kwargs):
    """RegNetY-600MF"""
    return _regnet('y_006', pretrained, **kwargs)


@register_model
def regnety_008(pretrained=False, **kwargs):
    """RegNetY-800MF"""
    return _regnet('y_008', pretrained, **kwargs)


@register_model
def regnety_016(pretrained=False, **kwargs):
    """RegNetY-1.6GF"""
    return _regnet('y_016', pretrained, **kwargs)


@register_model
def regnety_032(pretrained=False, **kwargs):
    """RegNetY-3.2GF"""
    return _regnet('y_032', pretrained, **kwargs)


@register_model
def regnety_040(pretrained=False, **kwargs):
    """RegNetY-4.0GF"""
    return _regnet('y_040', pretrained, **kwargs)


@register_model
def regnety_064(pretrained=False, **kwargs):
    """RegNetY-6.4GF"""
    return _regnet('y_064', pretrained, **kwargs)


@register_model
def regnety_080(pretrained=False, **kwargs):
    """RegNetY-8.0GF"""
    return _regnet('y_080', pretrained, **kwargs)


@register_model
def regnety_120(pretrained=False, **kwargs):
    """RegNetY-12GF"""
    return _regnet('y_120', pretrained, **kwargs)


@register_model
def regnety_160(pretrained=False, **kwargs):
    """RegNetY-16GF"""
    return _regnet('y_160', pretrained, **kwargs)


@register_model
def regnety_320(pretrained=False, **kwargs):
    """RegNetY-32GF"""
    return _regnet('y_320', pretrained, **kwargs)