import math
import warnings
import numpy as np
import torch
import torch.nn as nn


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    """Copy from timm"""
    with torch.no_grad():
        """Copy from timm"""
        def norm_cdf(x):
            return (1. + math.erf(x / math.sqrt(2.))) / 2.

        if (mean < a - 2 * std) or (mean > b + 2 * std):
            warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
                        "The distribution of values may be incorrect.",
                        stacklevel=2)

        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        tensor.uniform_(2 * l - 1, 2 * u - 1)
        tensor.erfinv_()

        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        tensor.clamp_(min=a, max=b)

        return tensor


# ---------------------------- NMS ----------------------------
## basic NMS
def nms(bboxes, scores, nms_thresh):
    """"Pure Python NMS."""
    x1 = bboxes[:, 0]  #xmin
    y1 = bboxes[:, 1]  #ymin
    x2 = bboxes[:, 2]  #xmax
    y2 = bboxes[:, 3]  #ymax

    areas = (x2 - x1) * (y2 - y1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        # compute iou
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(1e-10, xx2 - xx1)
        h = np.maximum(1e-10, yy2 - yy1)
        inter = w * h

        iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-14)
        #reserve all the boundingbox whose ovr less than thresh
        inds = np.where(iou <= nms_thresh)[0]
        order = order[inds + 1]

    return keep

## class-agnostic NMS 
def multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh):
    # nms
    keep = nms(bboxes, scores, nms_thresh)
    scores = scores[keep]
    labels = labels[keep]
    bboxes = bboxes[keep]

    return scores, labels, bboxes

## class-aware NMS 
def multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes):
    # nms
    keep = np.zeros(len(bboxes), dtype=np.int32)
    for i in range(num_classes):
        inds = np.where(labels == i)[0]
        if len(inds) == 0:
            continue
        c_bboxes = bboxes[inds]
        c_scores = scores[inds]
        c_keep = nms(c_bboxes, c_scores, nms_thresh)
        keep[inds[c_keep]] = 1
    keep = np.where(keep > 0)
    scores = scores[keep]
    labels = labels[keep]
    bboxes = bboxes[keep]

    return scores, labels, bboxes

## multi-class NMS 
def multiclass_nms(scores, labels, bboxes, nms_thresh, num_classes, class_agnostic=False):
    if class_agnostic:
        return multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh)
    else:
        return multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes)


# ----------------- Customed NormLayer Ops -----------------
class LayerNorm2D(nn.Module):
    def __init__(self, normalized_shape, norm_layer=nn.LayerNorm):
        super().__init__()
        self.ln = norm_layer(normalized_shape) if norm_layer is not None else nn.Identity()

    def forward(self, x):
        """
        x: N C H W
        """
        x = x.permute(0, 2, 3, 1)
        x = self.ln(x)
        x = x.permute(0, 3, 1, 2)
        return x


# ----------------- Basic CNN Ops -----------------
def get_conv2d(c1, c2, k, p, s, g, bias=False):
    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, groups=g, bias=bias)

    return conv

def get_activation(act_type=None):
    if act_type == 'relu':
        return nn.ReLU(inplace=True)
    elif act_type == 'lrelu':
        return nn.LeakyReLU(0.1, inplace=True)
    elif act_type == 'mish':
        return nn.Mish(inplace=True)
    elif act_type == 'silu':
        return nn.SiLU(inplace=True)
    elif act_type == 'gelu':
        return nn.GELU()
    elif act_type is None:
        return nn.Identity()
    else:
        raise NotImplementedError
        
def get_norm(norm_type, dim):
    if norm_type == 'BN':
        return nn.BatchNorm2d(dim)
    elif norm_type == 'GN':
        return nn.GroupNorm(num_groups=32, num_channels=dim)
    elif norm_type is None:
        return nn.Identity()
    else:
        raise NotImplementedError

class BasicConv(nn.Module):
    def __init__(self, 
                 in_dim,                   # in channels
                 out_dim,                  # out channels 
                 kernel_size=1,            # kernel size 
                 padding=0,                # padding
                 stride=1,                 # padding
                 act_type  :str = 'lrelu', # activation
                 norm_type :str = 'BN',    # normalization
                ):
        super(BasicConv, self).__init__()
        add_bias = False if norm_type else True
        self.conv = get_conv2d(in_dim, out_dim, k=kernel_size, p=padding, s=stride, g=1, bias=add_bias)
        self.norm = get_norm(norm_type, out_dim)
        self.act  = get_activation(act_type)

    def forward(self, x):
        return self.act(self.norm(self.conv(x)))

class UpSampleWrapper(nn.Module):
    """Upsample last feat map to specific stride."""
    def __init__(self, in_dim, upsample_factor):
        super(UpSampleWrapper, self).__init__()
        # ---------- Basic parameters ----------
        self.upsample_factor = upsample_factor

        # ---------- Network parameters ----------
        if upsample_factor == 1:
            self.upsample = nn.Identity()
        else:
            scale = int(math.log2(upsample_factor))
            dim = in_dim
            layers = []
            for _ in range(scale-1):
                layers += [
                    nn.ConvTranspose2d(dim, dim, kernel_size=2, stride=2),
                    LayerNorm2D(dim),
                    nn.GELU()
                ]
            layers += [nn.ConvTranspose2d(dim, dim, kernel_size=2, stride=2)]
            self.upsample = nn.Sequential(*layers)
            self.out_dim = dim

    def forward(self, x):
        x = self.upsample(x)

        return x


# ----------------- MLP modules -----------------
class MLP(nn.Module):
    def __init__(self, in_dim, hidden_dim, out_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([in_dim] + h, h + [out_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x

class FFN(nn.Module):
    def __init__(self, d_model=256, mlp_ratio=4.0, dropout=0., act_type='relu', pre_norm=False):
        super().__init__()
        # ----------- Basic parameters -----------
        self.pre_norm = pre_norm
        self.fpn_dim = round(d_model * mlp_ratio)
        # ----------- Network parameters -----------
        self.linear1 = nn.Linear(d_model, self.fpn_dim)
        self.activation = get_activation(act_type)
        self.dropout2 = nn.Dropout(dropout)
        self.linear2 = nn.Linear(self.fpn_dim, d_model)
        self.dropout3 = nn.Dropout(dropout)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, src):
        if self.pre_norm:
            src = self.norm(src)
            src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
            src = src + self.dropout3(src2)
        else:
            src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
            src = src + self.dropout3(src2)
            src = self.norm(src)
        
        return src