import warnings
import jittor as jt
from collections import OrderedDict
from jittor import nn
from jdet.utils.equivalent import FieldType, Rot2dOnR2
from jdet.models.equivalent_modules import convnxn, build_norm_layer, ennReLU, ennInterpolate, ennMaxPool
from jdet.utils.registry import NECKS

class ConvModule(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias='auto',
                 conv_cfg=None,
                 norm_cfg=None,
                 activation='relu',
                 inplace=True,
                 order=('conv', 'norm', 'act'),
                 gspace=None,
                 fixparams=False):
        super(ConvModule, self).__init__()
        assert conv_cfg is None or isinstance(conv_cfg, dict)
        assert norm_cfg is None or isinstance(norm_cfg, dict)
        self.gspace = gspace
        self.in_type = FieldType(
            gspace, [gspace.regular_repr] * in_channels)
        self.out_type = FieldType(
            gspace, [gspace.regular_repr] * out_channels)
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.activation = activation
        self.inplace = inplace
        self.order = order
        assert isinstance(self.order, tuple) and len(self.order) == 3
        assert set(order) == set(['conv', 'norm', 'act'])

        self.with_norm = norm_cfg is not None
        self.with_activatation = activation is not None
        # if the conv layer is before a norm layer, bias is unnecessary.
        if bias == 'auto':
            bias = False if self.with_norm else True
        self.with_bias = bias

        if self.with_norm and self.with_bias:
            warnings.warn('ConvModule has norm and bias at the same time')
        # build convolution layer
        self.conv = convnxn(
            gspace,
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias)
        # export the attributes of self.conv to a higher level for convenience
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = False
        self.output_padding = padding
        self.groups = groups

        # build normalization layers
        if self.with_norm:
            # norm layer is after conv layer
            if order.index('norm') > order.index('conv'):
                norm_channels = out_channels
            else:
                norm_channels = in_channels
            if conv_cfg != None and conv_cfg['type'] == 'ORConv':
                norm_channels = int(norm_channels * 8)
            self.norm_name, norm = build_norm_layer(
                norm_cfg, gspace, norm_channels)
            self.__setattr__(self.norm_name, norm)

        # build activation layer
        if self.with_activatation:
            # TODO: introduce `act_cfg` and supports more activation layers
            if self.activation not in ['relu']:
                raise ValueError('{} is currently not supported.'.format(
                    self.activation))
            if self.activation == 'relu':
                self.activate = ennReLU(
                    gspace, out_channels, inplace=self.inplace)

        # Use msra init by default
        self.init_weights()

    @property
    def norm(self):
        return getattr(self, self.norm_name)

    def init_weights(self):
        nonlinearity = 'relu' if self.activation is None else self.activation

    def execute(self, x, activate=True, norm=True):
        for layer in self.order:
            if layer == 'conv':
                x = self.conv(x)
            elif layer == 'norm' and norm and self.with_norm:
                x = self.norm(x)
            elif layer == 'act' and activate and self.with_activatation:
                x = self.activate(x)
        return x

    def evaluate_output_shape(self, input_shape):
        return input_shape

@NECKS.register_module()
class ReFPN(nn.Module):

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_outs,
                 start_level=0,
                 end_level=-1,
                 add_extra_convs=False,
                 extra_convs_on_inputs=True,
                 relu_before_extra_convs=False,
                 no_norm_on_lateral=False,
                 conv_cfg=None,
                 norm_cfg=None,
                 activation=None,
                 orientation=8,
                 fixparams=False):
        super(ReFPN, self).__init__()
        assert isinstance(in_channels, list)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_ins = len(in_channels)
        self.num_outs = num_outs
        self.activation = activation

        self.orientation = orientation
        self.fixparams = fixparams
        self.gspace = Rot2dOnR2(orientation)
        self.in_type = FieldType(
            self.gspace, [self.gspace.trivial_repr] * 3)

        self.relu_before_extra_convs = relu_before_extra_convs
        self.no_norm_on_lateral = no_norm_on_lateral
        self.fp16_enabled = False
        if end_level == -1:
            self.backbone_end_level = self.num_ins
            assert num_outs >= self.num_ins - start_level
        else:
            # if end_level < inputs, no extra level is allowed
            self.backbone_end_level = end_level
            assert end_level <= len(in_channels)
            assert num_outs == end_level - start_level
        self.start_level = start_level
        self.end_level = end_level
        self.add_extra_convs = add_extra_convs
        self.extra_convs_on_inputs = extra_convs_on_inputs

        self.lateral_convs = nn.ModuleList()
        self.up_samples = nn.ModuleList()
        self.fpn_convs = nn.ModuleList()

        for i in range(self.start_level, self.backbone_end_level):
            l_conv = ConvModule(
                in_channels[i],
                out_channels,
                1,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
                activation=self.activation,
                inplace=False,
                gspace=self.gspace,
                fixparams=fixparams)
            up_sample = ennInterpolate(self.gspace, out_channels, 2)
            fpn_conv = ConvModule(
                out_channels,
                out_channels,
                3,
                padding=1,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                activation=self.activation,
                inplace=False,
                gspace=self.gspace,
                fixparams=fixparams)

            self.lateral_convs.append(l_conv)
            self.up_samples.append(up_sample)
            self.fpn_convs.append(fpn_conv)

        # add extra conv layers (e.g., RetinaNet)
        extra_levels = num_outs - self.backbone_end_level + self.start_level
        if add_extra_convs and extra_levels >= 1:
            for i in range(extra_levels):
                if i == 0 and self.extra_convs_on_inputs:
                    in_channels = self.in_channels[self.backbone_end_level - 1]
                else:
                    in_channels = out_channels
                extra_fpn_conv = ConvModule(
                    in_channels,
                    out_channels,
                    3,
                    stride=2,
                    padding=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    activation=self.activation,
                    inplace=False,
                    gspace=self.gspace,
                    fixparams=fixparams)
                self.fpn_convs.append(extra_fpn_conv)

        self.max_pools = nn.ModuleList()
        self.relus = nn.ModuleList()

        used_backbone_levels = len(self.lateral_convs)
        if self.num_outs > used_backbone_levels:
            # use max pool to get more levels on top of outputs
            # (e.g., Faster R-CNN, Mask R-CNN)
            if not self.add_extra_convs:
                for i in range(self.num_outs - used_backbone_levels):
                    self.max_pools.append(
                        ennMaxPool(self.gspace, out_channels, 1, stride=2))
            # add conv layers on top of original feature maps (RetinaNet)
            else:
                for i in range(used_backbone_levels + 1, self.num_outs):
                    self.relus.append(ennReLU(self.gspace, out_channels))

    # default init_weights for conv(msra) and norm in ConvModule
    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                jt.init.xavier_uniform_(m)

    def execute(self, inputs):
        assert len(inputs) == len(self.in_channels)

        # build laterals
        laterals = [
            lateral_conv(inputs[i + self.start_level])
            for i, lateral_conv in enumerate(self.lateral_convs)
        ]

        # build top-down path
        used_backbone_levels = len(laterals)
        for i in range(used_backbone_levels - 1, 0, -1):
            # laterals[i - 1] += F.interpolate(
            #     laterals[i], scale_factor=2, mode='nearest')
            laterals[i - 1] += self.up_samples[i](laterals[i])

        # build outputs
        # part 1: from original levels
        outs = [
            self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
        ]
        # part 2: add extra levels
        if self.num_outs > len(outs):
            # use max pool to get more levels on top of outputs
            # (e.g., Faster R-CNN, Mask R-CNN)
            if not self.add_extra_convs:
                for i in range(self.num_outs - used_backbone_levels):
                    outs.append(self.max_pools[i](outs[-1]))
            # add conv layers on top of original feature maps (RetinaNet)
            else:
                if self.extra_convs_on_inputs:
                    orig = inputs[self.backbone_end_level - 1]
                    outs.append(self.fpn_convs[used_backbone_levels](orig))
                else:
                    outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))
                for i in range(used_backbone_levels + 1, self.num_outs):
                    if self.relu_before_extra_convs:
                        outs.append(self.fpn_convs[i](self.relus[i](outs[-1])))
                    else:
                        outs.append(self.fpn_convs[i](outs[-1]))

        # convert to tensor
        outs = [out.tensor for out in outs]

        return tuple(outs)
