import torch
import torch.nn as nn
from ..base_model import BaseModel
from ..modules import HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks, SetBlockWrapper, ParallelBN1d

# ******* Copy from https://github.com/haofanwang/video-swin-transformer-pytorch/blob/main/video_swin_transformer.py *******
from functools import reduce, lru_cache
from operator import mul
from einops import rearrange

import torch.nn.functional as F
class Mlp(nn.Module):
    """ Multilayer perceptron."""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, D, H, W, C)
        window_size (tuple[int]): window size
    Returns:
        windows: (B*num_windows, window_size*window_size, C)
    """
    B, D, H, W, C = x.shape
    x = x.view(B, D // window_size[0], window_size[0], H // window_size[1], window_size[1], W // window_size[2], window_size[2], C)
    windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), C)
    return windows


def window_reverse(windows, window_size, B, D, H, W):
    """
    Args:
        windows: (B*num_windows, window_size, window_size, C)
        window_size (tuple[int]): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, D, H, W, C)
    """
    x = windows.view(B, D // window_size[0], H // window_size[1], W // window_size[2], window_size[0], window_size[1], window_size[2], -1)
    x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1)
    return x


def get_window_size(x_size, window_size, shift_size=None):
    use_window_size = list(window_size)
    if shift_size is not None:
        use_shift_size = list(shift_size)
    for i in range(len(x_size)):
        if x_size[i] <= window_size[i]:
            use_window_size[i] = x_size[i]
            if shift_size is not None:
                use_shift_size[i] = 0

    if shift_size is None:
        return tuple(use_window_size)
    else:
        return tuple(use_window_size), tuple(use_shift_size)


from torch.nn.init import _calculate_fan_in_and_fan_out


import math
import warnings
# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/weight_init.py
def _trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        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)

    # Values are generated by using a truncated uniform distribution and
    # then using the inverse CDF for the normal distribution.
    # Get upper and lower cdf values
    l = norm_cdf((a - mean) / std)
    u = norm_cdf((b - mean) / std)

    # Uniformly fill tensor with values from [l, u], then translate to
    # [2l-1, 2u-1].
    tensor.uniform_(2 * l - 1, 2 * u - 1)

    # Use inverse cdf transform for normal distribution to get truncated
    # standard normal
    tensor.erfinv_()

    # Transform to proper mean, std
    tensor.mul_(std * math.sqrt(2.))
    tensor.add_(mean)

    # Clamp to ensure it's in the proper range
    tensor.clamp_(min=a, max=b)
    return tensor

# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/weight_init.py
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    # type: (Tensor, float, float, float, float) -> Tensor
    r"""Fills the input Tensor with values drawn from a truncated
    normal distribution. The values are effectively drawn from the
    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \leq \text{mean} \leq b`.
    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
    applied while sampling the normal with mean/std applied, therefore a, b args
    should be adjusted to match the range of mean, std args.
    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value
    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.trunc_normal_(w)
    """
    with torch.no_grad():
        return _trunc_normal_(tensor, mean, std, a, b)

class WindowAttention3D(nn.Module):
    """ Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The temporal length, height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wd, Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) * (2 * window_size[2] - 1), num_heads))  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_d = torch.arange(self.window_size[0])
        coords_h = torch.arange(self.window_size[1])
        coords_w = torch.arange(self.window_size[2])
        coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))  # 3, Wd, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 3, Wd*Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 3, Wd*Wh*Ww, Wd*Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wd*Wh*Ww, Wd*Wh*Ww, 3
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 2] += self.window_size[2] - 1

        relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
        relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1)
        relative_position_index = relative_coords.sum(-1)  # Wd*Wh*Ww, Wd*Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """ Forward function.
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, N, N) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # B_, nH, N, C

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index[:N, :N].reshape(-1)].reshape(
            N, N, -1)  # Wd*Wh*Ww,Wd*Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wd*Wh*Ww, Wd*Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0) # B_, nH, N, N

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/drop.py
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
    if keep_prob > 0.0 and scale_by_keep:
        random_tensor.div_(keep_prob)
    return x * random_tensor

# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/drop.py
class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)

    def extra_repr(self):
        return f'drop_prob={round(self.drop_prob,3):0.3f}'

class SwinTransformerBlock3D(nn.Module):
    """ Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (tuple[int]): Window size.
        shift_size (tuple[int]): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, num_heads, window_size=(2,7,7), shift_size=(0,0,0),
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_checkpoint=False):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.use_checkpoint=use_checkpoint

        assert 0 <= self.shift_size[0] < self.window_size[0], "shift_size must in 0-window_size"
        assert 0 <= self.shift_size[1] < self.window_size[1], "shift_size must in 0-window_size"
        assert 0 <= self.shift_size[2] < self.window_size[2], "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention3D(
            dim, window_size=self.window_size, num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward_part1(self, x, mask_matrix):
        B, D, H, W, C = x.shape
        window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size)

        x = self.norm1(x)
        # pad feature maps to multiples of window size
        pad_l = pad_t = pad_d0 = 0
        pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0]
        pad_b = (window_size[1] - H % window_size[1]) % window_size[1]
        pad_r = (window_size[2] - W % window_size[2]) % window_size[2]
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
        _, Dp, Hp, Wp, _ = x.shape
        # cyclic shift
        if any(i > 0 for i in shift_size):
            shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None
        # partition windows
        x_windows = window_partition(shifted_x, window_size)  # B*nW, Wd*Wh*Ww, C
        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=attn_mask)  # B*nW, Wd*Wh*Ww, C
        # merge windows
        attn_windows = attn_windows.view(-1, *(window_size+(C,)))
        shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp)  # B D' H' W' C
        # reverse cyclic shift
        if any(i > 0 for i in shift_size):
            x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
        else:
            x = shifted_x

        if pad_d1 >0 or pad_r > 0 or pad_b > 0:
            x = x[:, :D, :H, :W, :].contiguous()
        return x

    def forward_part2(self, x):
        return self.drop_path(self.mlp(self.norm2(x)))

    def forward(self, x, mask_matrix):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, D, H, W, C).
            mask_matrix: Attention mask for cyclic shift.
        """

        shortcut = x
        if self.use_checkpoint:
            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
        else:
            x = self.forward_part1(x, mask_matrix)
        x = shortcut + self.drop_path(x)

        if self.use_checkpoint:
            x = x + checkpoint.checkpoint(self.forward_part2, x)
        else:
            x = x + self.forward_part2(x)

        return x


class PatchMerging(nn.Module):
    """ Patch Merging Layer
    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """
    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, D, H, W, C).
        """
        B, D, H, W, C = x.shape

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        x0 = x[:, :, 0::2, 0::2, :]  # B D H/2 W/2 C
        x1 = x[:, :, 1::2, 0::2, :]  # B D H/2 W/2 C
        x2 = x[:, :, 0::2, 1::2, :]  # B D H/2 W/2 C
        x3 = x[:, :, 1::2, 1::2, :]  # B D H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B D H/2 W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x


# cache each stage results
@lru_cache()
def compute_mask(D, H, W, window_size, shift_size, device):
    img_mask = torch.zeros((1, D, H, W, 1), device=device)  # 1 Dp Hp Wp 1
    cnt = 0
    for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0],None):
        for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1],None):
            for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2],None):
                img_mask[:, d, h, w, :] = cnt
                cnt += 1
    mask_windows = window_partition(img_mask, window_size)  # nW, ws[0]*ws[1]*ws[2], 1
    mask_windows = mask_windows.squeeze(-1)  # nW, ws[0]*ws[1]*ws[2]
    attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
    return attn_mask

import numpy as np
class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (tuple[int]): Local window size. Default: (1,7,7).
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
    """

    def __init__(self,
                 dim,
                 depth,
                 num_heads,
                 window_size=(1,7,7),
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 norm_layer=nn.LayerNorm,
                 downsample=None,
                 use_checkpoint=False):
        super().__init__()
        self.window_size = window_size
        self.shift_size = tuple(i // 2 for i in window_size)
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock3D(
                dim=dim,
                num_heads=num_heads,
                window_size=window_size,
                shift_size=(0,0,0) if (i % 2 == 0) else self.shift_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop,
                attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer,
                use_checkpoint=use_checkpoint,
            )
            for i in range(depth)]
        )
        
        self.downsample = downsample
        if self.downsample == False: 
            self.downsample = lambda x: x
        else: 
            if self.downsample is not None:
                self.downsample = downsample(dim=dim, norm_layer=norm_layer)
            else:
                self.downsample = nn.Sequential(
                    norm_layer(dim), 
                    nn.Linear(dim, 2 * dim, bias=False)
                )

    def forward(self, x):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, C, D, H, W).
        """
        # calculate attention mask for SW-MSA
        B, C, D, H, W = x.shape
        window_size, shift_size = get_window_size((D,H,W), self.window_size, self.shift_size)
        x = rearrange(x, 'b c d h w -> b d h w c')
        Dp = int(np.ceil(D / window_size[0])) * window_size[0]
        Hp = int(np.ceil(H / window_size[1])) * window_size[1]
        Wp = int(np.ceil(W / window_size[2])) * window_size[2]
        attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device)
        for blk in self.blocks:
            x = blk(x, attn_mask)
        x = x.view(B, D, H, W, -1)

        if self.downsample is not None:
            x = self.downsample(x)
        x = rearrange(x, 'b d h w c -> b c d h w')
        return x


class PatchEmbed3D(nn.Module):
    """ Video to Patch Embedding.
    Args:
        patch_size (int): Patch token size. Default: (2,4,4).
        in_chans (int): Number of input video channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """
    def __init__(self, patch_size=(2,4,4), in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        """Forward function."""
        # padding
        _, _, D, H, W = x.size()
        if W % self.patch_size[2] != 0:
            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
        if H % self.patch_size[1] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
        if D % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))

        x = self.proj(x)  # B C D Wh Ww
        if self.norm is not None:
            D, Wh, Ww = x.size(2), x.size(3), x.size(4)
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)

        return x

class SwinTransformer3D(nn.Module):
    """ Swin Transformer backbone.
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030
    Args:
        patch_size (int | tuple(int)): Patch size. Default: (4,4,4).
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        depths (tuple[int]): Depths of each Swin Transformer stage.
        num_heads (tuple[int]): Number of attention head of each stage.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): Dropout rate.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
        norm_layer: Normalization layer. Default: nn.LayerNorm.
        patch_norm (bool): If True, add normalization after patch embedding. Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
    """

    def __init__(self,
                 pretrained=None,
                 pretrained2d=True,
                 patch_size=(4,4,4),
                 in_chans=3,
                 embed_dim=96,
                 depths=[2, 2, 6, 2],
                 num_heads=[3, 6, 12, 24],
                 window_size=(2,7,7),
                 mlp_ratio=4.,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.2,
                 norm_layer=nn.LayerNorm,
                 patch_norm=False,
                 frozen_stages=-1,
                 use_checkpoint=False, 
                 downsample=[1, 2, 2, 1]):
        super().__init__()

        self.pretrained = pretrained
        self.pretrained2d = pretrained2d
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.frozen_stages = frozen_stages
        self.window_size = window_size
        self.patch_size = patch_size

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed3D(
            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            if downsample[i_layer]== 2: 
                dfunc = PatchMerging
            elif downsample[i_layer] == 1: 
                dfunc = None
            elif downsample[i_layer] == 0:
                dfunc = False
            else: 
                raise ValueError('xxx')

            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                norm_layer=norm_layer,
                downsample=dfunc, 
                use_checkpoint=use_checkpoint)
            
            self.layers.append(layer)

        # self.num_features = int(embed_dim * 2**self.num_layers)
        self.num_features = 512

        # add a norm layer for each output
        self.norm = norm_layer(self.num_features)

        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False

        if self.frozen_stages >= 1:
            self.pos_drop.eval()
            for i in range(0, self.frozen_stages):
                m = self.layers[i]
                m.eval()
                for param in m.parameters():
                    param.requires_grad = False

    def inflate_weights(self, logger):
        """Inflate the swin2d parameters to swin3d.
        The differences between swin3d and swin2d mainly lie in an extra
        axis. To utilize the pretrained parameters in 2d model,
        the weight of swin2d models should be inflated to fit in the shapes of
        the 3d counterpart.
        Args:
            logger (logging.Logger): The logger used to print
                debugging infomation.
        """
        checkpoint = torch.load(self.pretrained, map_location='cpu')
        state_dict = checkpoint['model']

        # delete relative_position_index since we always re-init it
        relative_position_index_keys = [k for k in state_dict.keys() if "relative_position_index" in k]
        for k in relative_position_index_keys:
            del state_dict[k]

        # delete attn_mask since we always re-init it
        attn_mask_keys = [k for k in state_dict.keys() if "attn_mask" in k]
        for k in attn_mask_keys:
            del state_dict[k]

        state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).repeat(1,1,self.patch_size[0],1,1) / self.patch_size[0]

        # bicubic interpolate relative_position_bias_table if not match
        relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
        for k in relative_position_bias_table_keys:
            relative_position_bias_table_pretrained = state_dict[k]
            relative_position_bias_table_current = self.state_dict()[k]
            L1, nH1 = relative_position_bias_table_pretrained.size()
            L2, nH2 = relative_position_bias_table_current.size()
            L2 = (2*self.window_size[1]-1) * (2*self.window_size[2]-1)
            wd = self.window_size[0]
            if nH1 != nH2:
                logger.warning(f"Error in loading {k}, passing")
            else:
                if L1 != L2:
                    S1 = int(L1 ** 0.5)
                    relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate(
                        relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(2*self.window_size[1]-1, 2*self.window_size[2]-1),
                        mode='bicubic')
                    relative_position_bias_table_pretrained = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0)
            state_dict[k] = relative_position_bias_table_pretrained.repeat(2*wd-1,1)

        msg = self.load_state_dict(state_dict, strict=False)
        logger.info(msg)
        logger.info(f"=> loaded successfully '{self.pretrained}'")
        del checkpoint
        torch.cuda.empty_cache()

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.
        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        if pretrained:
            self.pretrained = pretrained
        if isinstance(self.pretrained, str):
            self.apply(_init_weights)
            logger = get_root_logger()
            logger.info(f'load model from: {self.pretrained}')

            if self.pretrained2d:
                # Inflate 2D model into 3D model.
                self.inflate_weights(logger)
            else:
                # Directly load 3D model.
                load_checkpoint(self, self.pretrained, strict=False, logger=logger)
        elif self.pretrained is None:
            self.apply(_init_weights)
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x):
        """Forward function."""
        x = self.patch_embed(x)

        x = self.pos_drop(x)

        for layer in self.layers:
            x = layer(x.contiguous())

        x = rearrange(x, 'n c d h w -> n d h w c')
        x = self.norm(x)
        x = rearrange(x, 'n d h w c -> n c d h w')

        return x

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(SwinTransformer3D, self).train(mode)
        self._freeze_stages()

# ******* Copy from https://github.com/haofanwang/video-swin-transformer-pytorch/blob/main/video_swin_transformer.py *******

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)

def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

# import copy

from ..modules import BasicBlock2D, BasicBlockP3D

import torch.optim as optim
import os.path as osp
from collections import OrderedDict
from opengait.utils import get_valid_args, get_attr_from

class SwinGait(BaseModel):
    def __init__(self, cfgs, training): 
        self.T_max_iter = cfgs['trainer_cfg']['T_max_iter']
        super(SwinGait, self).__init__(cfgs, training=training)

    def build_network(self, model_cfg):
        channels = model_cfg['Backbone']['channels']
        layers   = model_cfg['Backbone']['layers']
        in_c     = model_cfg['Backbone']['in_channels']

        self.inplanes = channels[0]
        self.layer0 = SetBlockWrapper(nn.Sequential(
            conv3x3(in_c, self.inplanes, 1), 
            nn.BatchNorm2d(self.inplanes), 
            nn.ReLU(inplace=True)
        ))
        self.layer1 = SetBlockWrapper(self.make_layer(BasicBlock2D, channels[0], stride=[1, 1], blocks_num=layers[0], mode='2d'))
        self.layer2 = self.make_layer(BasicBlockP3D, channels[1], stride=[2, 2], blocks_num=layers[1], mode='p3d')

        self.ulayer = SetBlockWrapper(nn.UpsamplingBilinear2d(size=(30, 20)))

        self.transformer = SwinTransformer3D(
            patch_size = [1, 2, 2], 
            in_chans = channels[1], 
            embed_dim = 256, 
            depths = [layers[2], layers[3]], 
            num_heads = [16, 32], 
            window_size = [3, 3, 5], 
            downsample = [1, 0], 
            drop_path_rate = 0.1, 
            patch_norm = True, 
        )

        self.FCs = SeparateFCs(model_cfg['SeparateBNNecks']['parts_num'], in_channels=512, out_channels=256)
        self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
        self.TP = PackSequenceWrapper(torch.max)
        self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num'])

    def get_optimizer(self, optimizer_cfg):
        self.msg_mgr.log_info(optimizer_cfg)
        optimizer = get_attr_from([optim], optimizer_cfg['solver'])
        valid_arg = get_valid_args(optimizer, optimizer_cfg, ['solver'])

        transformer_no_decay = ['patch_embed', 'norm', 'relative_position_bias_table']
        transformer_params = list(self.transformer.named_parameters())
        params_list = [
            {'params': [p for n, p in transformer_params if any(nd in n for nd in transformer_no_decay)], 'lr': optimizer_cfg['lr'], 'weight_decay': 0.}, 
            {'params': [p for n, p in transformer_params if not any(nd in n for nd in transformer_no_decay)], 'lr': optimizer_cfg['lr'], 'weight_decay': optimizer_cfg['weight_decay']}, 
            {'params': self.FCs.parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']}, 
            {'params': self.BNNecks.parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']}, 
        ]
        for i in range(5): 
            if hasattr(self, 'layer%d'%i): 
                params_list.append(
                    {'params': getattr(self, 'layer%d'%i).parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']}
                )

        optimizer = optimizer(params_list)
        return optimizer

    def init_parameters(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)
            elif isinstance(m, (nn.Conv3d, nn.Conv2d, nn.Conv1d)):
                nn.init.xavier_uniform_(m.weight.data)
                if m.bias is not None:
                    nn.init.constant_(m.bias.data, 0.0)
            elif isinstance(m, (nn.BatchNorm3d, nn.BatchNorm2d, nn.BatchNorm1d)):
                if m.affine:
                    nn.init.normal_(m.weight.data, 1.0, 0.02)
                    nn.init.constant_(m.bias.data, 0.0)

    def make_layer(self, block, planes, stride, blocks_num, mode='2d'): 

        if max(stride) > 1 or self.inplanes != planes * block.expansion: 
            if mode == '3d': 
                downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=stride, padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion))
            elif mode == '2d':
                downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride=stride), nn.BatchNorm2d(planes * block.expansion))
            elif mode == 'p3d':
                downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=[1, *stride], padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion))
            else:
                raise TypeError('xxx')
        else: 
            downsample = lambda x: x

        layers = [block(self.inplanes, planes, stride=stride, downsample=downsample)]
        self.inplanes = planes * block.expansion
        s = [1, 1] if mode in ['2d', 'p3d'] else [1, 1, 1]
        for i in range(1, blocks_num): 
            layers.append(
                    block(self.inplanes, planes, stride=s)
            )
        return nn.Sequential(*layers)

    def forward(self, inputs):
        if self.training:
            adjust_learning_rate(self.optimizer, self.iteration, T_max_iter=self.T_max_iter)
        ipts, labs, _, _, seqL = inputs

        sils = ipts[0].unsqueeze(1)

        del ipts

        out0 = self.layer0(sils)
        out1 = self.layer1(out0)
        out2 = self.layer2(out1) # [n, c, s, h, w]
        out2 = self.ulayer(out2)
        out4 = self.transformer(out2) # [n, 768, s/4, 4, 3]

        # Temporal Pooling, TP
        outs = self.TP(out4, seqL, options={"dim": 2})[0]  # [n, c, h, w]
        # Horizontal Pooling Matching, HPM
        feat = self.HPP(outs)  # [n, c, p]

        feat = torch.cat([feat, feat[:, :, -1].clone().detach().unsqueeze(-1)], dim=-1)
        embed_1 = self.FCs(feat)  # [n, c, p]
        embed_2, logits = self.BNNecks(embed_1)  # [n, c, p]
        embed_1 = embed_1.contiguous()[:, :, :-1]  # [n, p, c]
        embed_2 = embed_2.contiguous()[:, :, :-1]  # [n, p, c]
        logits = logits.contiguous()[:, :, :-1]  # [n, p, c]

        embed = embed_1

        retval = {
            'training_feat': {
                'triplet': {'embeddings': embed_1, 'labels': labs},
                'softmax': {'logits': logits, 'labels': labs}
            },
            'visual_summary': {
                'image/sils': rearrange(sils,'n c s h w -> (n s) c h w')
            },
            'inference_feat': {
                'embeddings': embed
            }
        }
        return retval

import math
def adjust_learning_rate(optimizer, iteration, iteration_per_epoch=1000, T_max_iter=10000, min_lr=1e-6):
    """Decay the learning rate based on schedule"""
    if iteration < T_max_iter:
        if iteration % iteration_per_epoch == 0 :
            alpha = 0.5 * (1. + math.cos(math.pi * iteration / T_max_iter))
            for param_group in optimizer.param_groups:
                param_group['lr'] = max(param_group['initial_lr'] * alpha, min_lr)
        else:
            pass
    elif iteration == T_max_iter:
        for param_group in optimizer.param_groups:
                param_group['lr'] = min_lr
    else:
        pass