import math
import torch
import xformers
import open_clip
import xformers.ops
import torch.nn as nn
from torch import einsum
from einops import rearrange
from functools import partial
import torch.nn.functional as F
from rotary_embedding_torch import RotaryEmbedding
from fairscale.nn.checkpoint import checkpoint_wrapper

# from .mha_flash import FlashAttentionBlock
from utils.registry_class import MODEL


### load all keys started with prefix and replace them with new_prefix
def load_Block(state, prefix, new_prefix=None):
    if new_prefix is None:
        new_prefix = prefix

    state_dict = {}
    state = {key:value for key,value in state.items() if prefix in key}
    for key,value in state.items():
        new_key = key.replace(prefix, new_prefix)
        state_dict[new_key]=value       
    return state_dict


def load_2d_pretrained_state_dict(state,cfg):

    new_state_dict = {}

    dim = cfg.unet_dim
    num_res_blocks = cfg.unet_res_blocks
    temporal_attention = cfg.temporal_attention
    temporal_conv = cfg.temporal_conv
    dim_mult = cfg.unet_dim_mult
    attn_scales = cfg.unet_attn_scales
    
    # params
    enc_dims = [dim * u for u in [1] + dim_mult]
    dec_dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
    shortcut_dims = []
    scale = 1.0

    #embeddings
    state_dict = load_Block(state,prefix=f'time_embedding')
    new_state_dict.update(state_dict)
    state_dict = load_Block(state,prefix=f'y_embedding')
    new_state_dict.update(state_dict)
    state_dict = load_Block(state,prefix=f'context_embedding')
    new_state_dict.update(state_dict)

    encoder_idx = 0
    ### init block
    state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}',new_prefix=f'encoder.{encoder_idx}.0')
    new_state_dict.update(state_dict)
    encoder_idx += 1

    shortcut_dims.append(dim)
    for i, (in_dim, out_dim) in enumerate(zip(enc_dims[:-1], enc_dims[1:])):
        for j in range(num_res_blocks):
            # residual (+attention) blocks
            idx = 0
            idx_ = 0
            # residual (+attention) blocks
            state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}.{idx}',new_prefix=f'encoder.{encoder_idx}.{idx_}')
            new_state_dict.update(state_dict)
            idx += 1
            idx_ = 2

            if scale in attn_scales:
                # block.append(AttentionBlock(out_dim, context_dim, num_heads, head_dim))
                state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}.{idx}',new_prefix=f'encoder.{encoder_idx}.{idx_}')
                new_state_dict.update(state_dict)
                # if temporal_attention:
                #     block.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb))
            in_dim = out_dim
            encoder_idx += 1
            shortcut_dims.append(out_dim)

            # downsample
            if i != len(dim_mult) - 1 and j == num_res_blocks - 1:
                # downsample = ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 0.5, dropout)
                state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}',new_prefix=f'encoder.{encoder_idx}.0')
                new_state_dict.update(state_dict)

                shortcut_dims.append(out_dim)
                scale /= 2.0
                encoder_idx += 1

    # middle
    # self.middle = nn.ModuleList([
    #     ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 'none'),
    #     TemporalConvBlock(out_dim),
    #     AttentionBlock(out_dim, context_dim, num_heads, head_dim)])
    # if temporal_attention:
    #     self.middle.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb =  self.rotary_emb))
    # elif temporal_conv:
    #     self.middle.append(TemporalConvBlock(out_dim,dropout=dropout))
    # self.middle.append(ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 'none'))
    # self.middle.append(TemporalConvBlock(out_dim))


    # middle
    middle_idx = 0
    # self.middle = nn.ModuleList([
    #     ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout),
    #     AttentionBlock(out_dim, context_dim, num_heads, head_dim)])
    state_dict = load_Block(state,prefix=f'middle.{middle_idx}')
    new_state_dict.update(state_dict)
    middle_idx += 2

    state_dict = load_Block(state,prefix=f'middle.1',new_prefix=f'middle.{middle_idx}')
    new_state_dict.update(state_dict)
    middle_idx += 1

    for _ in range(cfg.temporal_attn_times):
        # self.middle.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb =  self.rotary_emb))
        middle_idx += 1

    # self.middle.append(ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout))
    state_dict = load_Block(state,prefix=f'middle.2',new_prefix=f'middle.{middle_idx}')
    new_state_dict.update(state_dict)
    middle_idx += 2


    decoder_idx = 0
    for i, (in_dim, out_dim) in enumerate(zip(dec_dims[:-1], dec_dims[1:])):
        for j in range(num_res_blocks + 1):
            idx = 0
            idx_ = 0
            # residual (+attention) blocks
            # block = nn.ModuleList([ResidualBlock(in_dim + shortcut_dims.pop(), embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout)])
            state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}')
            new_state_dict.update(state_dict)
            idx += 1
            idx_ += 2
            if scale in attn_scales:
                # block.append(AttentionBlock(out_dim, context_dim, num_heads, head_dim))
                state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}')
                new_state_dict.update(state_dict)
                idx += 1
                idx_ += 1
                for _ in range(cfg.temporal_attn_times):
                #     block.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb =  self.rotary_emb))
                    idx_ +=1

            in_dim = out_dim

            # upsample
            if i != len(dim_mult) - 1 and j == num_res_blocks:

                # upsample = ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 2.0, dropout)
                state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}')
                new_state_dict.update(state_dict)
                idx += 1
                idx_ += 2

                scale *= 2.0
                # block.append(upsample)
            # self.decoder.append(block)
            decoder_idx += 1

    # head
    # self.head = nn.Sequential(
    #     nn.GroupNorm(32, out_dim),
    #     nn.SiLU(),
    #     nn.Conv3d(out_dim, self.out_dim, (1,3,3), padding=(0,1,1)))
    state_dict = load_Block(state,prefix=f'head')
    new_state_dict.update(state_dict)

    return new_state_dict

def sinusoidal_embedding(timesteps, dim):
    # check input
    half = dim // 2
    timesteps = timesteps.float()

    # compute sinusoidal embedding
    sinusoid = torch.outer(
        timesteps,
        torch.pow(10000, -torch.arange(half).to(timesteps).div(half)))
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    if dim % 2 != 0:
        x = torch.cat([x, torch.zeros_like(x[:, :1])], dim=1)
    return x

def exists(x):
    return x is not None

def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d

def prob_mask_like(shape, prob, device):
    if prob == 1:
        return torch.ones(shape, device = device, dtype = torch.bool)
    elif prob == 0:
        return torch.zeros(shape, device = device, dtype = torch.bool)
    else:
        mask = torch.zeros(shape, device = device).float().uniform_(0, 1) < prob
        ### aviod mask all, which will cause find_unused_parameters error
        if mask.all():
            mask[0]=False
        return mask


class MemoryEfficientCrossAttention(nn.Module):
    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
    def __init__(self, query_dim, max_bs=4096, context_dim=None, heads=8, dim_head=64, dropout=0.0):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.max_bs = max_bs
        self.heads = heads
        self.dim_head = dim_head
        
        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
        self.attention_op: Optional[Any] = xformers.ops.fmha.MemoryEfficientAttentionFlashAttentionOp

    def forward(self, x, context=None, mask=None):
        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        b, _, _ = q.shape
        q, k, v = map(
            lambda t: t.unsqueeze(3)
            .reshape(b, t.shape[1], self.heads, self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b * self.heads, t.shape[1], self.dim_head)
            .contiguous(),
            (q, k, v),
        )

        # actually compute the attention, what we cannot get enough of
        if q.shape[0] > self.max_bs:
            q_list = torch.chunk(q, q.shape[0] // self.max_bs, dim=0)
            k_list = torch.chunk(k, k.shape[0] // self.max_bs, dim=0)
            v_list = torch.chunk(v, v.shape[0] // self.max_bs, dim=0)
            out_list = []
            for q_1, k_1, v_1 in zip(q_list, k_list, v_list):
                out = xformers.ops.memory_efficient_attention(
                    q_1, k_1, v_1, attn_bias=None, op=self.attention_op)
                out_list.append(out)
            out = torch.cat(out_list, dim=0)
        else:
            out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)

        if exists(mask):
            raise NotImplementedError
        out = (
            out.unsqueeze(0)
            .reshape(b, self.heads, out.shape[1], self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b, out.shape[1], self.heads * self.dim_head)
        )
        return self.to_out(out)

class RelativePositionBias(nn.Module):
    def __init__(
        self,
        heads = 8,
        num_buckets = 32,
        max_distance = 128
    ):
        super().__init__()
        self.num_buckets = num_buckets
        self.max_distance = max_distance
        self.relative_attention_bias = nn.Embedding(num_buckets, heads)

    @staticmethod
    def _relative_position_bucket(relative_position, num_buckets = 32, max_distance = 128):
        ret = 0
        n = -relative_position

        num_buckets //= 2
        ret += (n < 0).long() * num_buckets
        n = torch.abs(n)

        max_exact = num_buckets // 2
        is_small = n < max_exact

        val_if_large = max_exact + (
            torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
        ).long()
        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))

        ret += torch.where(is_small, n, val_if_large)
        return ret

    def forward(self, n, device):
        q_pos = torch.arange(n, dtype = torch.long, device = device)
        k_pos = torch.arange(n, dtype = torch.long, device = device)
        rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1')
        rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance)
        values = self.relative_attention_bias(rp_bucket)
        return rearrange(values, 'i j h -> h i j')

class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    NEW: use_linear for more efficiency instead of the 1x1 convs
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None,
                 disable_self_attn=False, use_linear=False,
                 use_checkpoint=True):
        super().__init__()
        if exists(context_dim) and not isinstance(context_dim, list):
            context_dim = [context_dim]
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv2d(in_channels,
                                     inner_dim,
                                     kernel_size=1,
                                     stride=1,
                                     padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        self.transformer_blocks = nn.ModuleList(
            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
                for d in range(depth)]
        )
        if not use_linear:
            self.proj_out = zero_module(nn.Conv2d(inner_dim,
                                                  in_channels,
                                                  kernel_size=1,
                                                  stride=1,
                                                  padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if not isinstance(context, list):
            context = [context]
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        if not self.use_linear:
            x = self.proj_in(x)
        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
        if self.use_linear:
            x = self.proj_in(x)
        for i, block in enumerate(self.transformer_blocks):
            x = block(x, context=context[i])
        if self.use_linear:
            x = self.proj_out(x)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            x = self.proj_out(x)
        return x + x_in

import os
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")

class CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head ** -0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim),
            nn.Dropout(dropout)
        )

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

        # force cast to fp32 to avoid overflowing
        if _ATTN_PRECISION =="fp32":
            with torch.autocast(enabled=False, device_type = 'cuda'):
                q, k = q.float(), k.float()
                sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
        else:
            sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
        
        del q, k
    
        if exists(mask):
            mask = rearrange(mask, 'b ... -> b (...)')
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, 'b j -> (b h) () j', h=h)
            sim.masked_fill_(~mask, max_neg_value)

        # attention, what we cannot get enough of
        sim = sim.softmax(dim=-1)

        out = torch.einsum('b i j, b j d -> b i d', sim, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        return self.to_out(out)


class MemoryEfficientCrossAttention_attemask(nn.Module):
    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.heads = heads
        self.dim_head = dim_head

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
        self.attention_op: Optional[Any] = xformers.ops.fmha.MemoryEfficientAttentionFlashAttentionOp

    def forward(self, x, context=None, mask=None):
        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        b, _, _ = q.shape
        q, k, v = map(
            lambda t: t.unsqueeze(3)
            .reshape(b, t.shape[1], self.heads, self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b * self.heads, t.shape[1], self.dim_head)
            .contiguous(),
            (q, k, v),
        )

        # actually compute the attention, what we cannot get enough of
        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=xformers.ops.LowerTriangularMask(), op=self.attention_op)

        if exists(mask):
            raise NotImplementedError
        out = (
            out.unsqueeze(0)
            .reshape(b, self.heads, out.shape[1], self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b, out.shape[1], self.heads * self.dim_head)
        )
        return self.to_out(out)



class BasicTransformerBlock_attemask(nn.Module):
    # ATTENTION_MODES = {
    #     "softmax": CrossAttention,  # vanilla attention
    #     "softmax-xformers": MemoryEfficientCrossAttention
    # }
    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
                 disable_self_attn=False):
        super().__init__()
        # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
        # assert attn_mode in self.ATTENTION_MODES
        # attn_cls = CrossAttention
        attn_cls = MemoryEfficientCrossAttention_attemask
        self.disable_self_attn = disable_self_attn
        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward_(self, x, context=None):
        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)

    def forward(self, x, context=None):
        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x

class BasicTransformerBlock(nn.Module):
    # ATTENTION_MODES = {
    #     "softmax": CrossAttention,  # vanilla attention
    #     "softmax-xformers": MemoryEfficientCrossAttention
    # }
    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
                 disable_self_attn=False):
        super().__init__()
        # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
        # assert attn_mode in self.ATTENTION_MODES
        # attn_cls = CrossAttention
        attn_cls = MemoryEfficientCrossAttention
        self.disable_self_attn = disable_self_attn
        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward_(self, x, context=None):
        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)

    def forward(self, x, context=None):
        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x

# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)

def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = nn.Sequential(
            nn.Linear(dim, inner_dim),
            nn.GELU()
        ) if not glu else GEGLU(dim, inner_dim)

        self.net = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)

class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class UpsampleSR600(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
            # TODO: to match input_blocks, remove elements of two sides
            x = x[..., 1:-1, :]
        if self.use_conv:
            x = self.conv(x)
        return x


class ResBlock(nn.Module):
    """
    A residual block that can optionally change the number of channels.
    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """
    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        up=False,
        down=False,
        use_temporal_conv=True,
        use_image_dataset=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_scale_shift_norm = use_scale_shift_norm
        self.use_temporal_conv = use_temporal_conv

        self.in_layers = nn.Sequential(
            nn.GroupNorm(32, channels),
            nn.SiLU(),
            nn.Conv2d(channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            nn.Linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            nn.GroupNorm(32, self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)

        if self.use_temporal_conv:
            self.temopral_conv = TemporalConvBlock_v2(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset)
            # self.temopral_conv_2 = TemporalConvBlock(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset)

    def forward(self, x, emb, batch_size):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.
        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return self._forward(x, emb, batch_size)

    def _forward(self, x, emb, batch_size):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        h = self.skip_connection(x) + h

        if self.use_temporal_conv:
            h = rearrange(h, '(b f) c h w -> b c f h w', b=batch_size)
            h = self.temopral_conv(h)
            # h = self.temopral_conv_2(h)
            h = rearrange(h, 'b c f h w -> (b f) c h w')
        return h

class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)

class Resample(nn.Module):

    def __init__(self, in_dim, out_dim, mode):
        assert mode in ['none', 'upsample', 'downsample']
        super(Resample, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.mode = mode
    
    def forward(self, x, reference=None):
        if self.mode == 'upsample':
            assert reference is not None
            x = F.interpolate(x, size=reference.shape[-2:], mode='nearest')
        elif self.mode == 'downsample':
            x = F.adaptive_avg_pool2d(x, output_size=tuple(u // 2 for u in x.shape[-2:]))
        return x

class ResidualBlock(nn.Module):

    def __init__(self, in_dim, embed_dim, out_dim, use_scale_shift_norm=True,
                 mode='none', dropout=0.0):
        super(ResidualBlock, self).__init__()
        self.in_dim = in_dim
        self.embed_dim = embed_dim
        self.out_dim = out_dim
        self.use_scale_shift_norm = use_scale_shift_norm
        self.mode = mode

        # layers
        self.layer1 = nn.Sequential(
            nn.GroupNorm(32, in_dim),
            nn.SiLU(),
            nn.Conv2d(in_dim, out_dim, 3, padding=1))
        self.resample = Resample(in_dim, in_dim, mode)
        self.embedding = nn.Sequential(
            nn.SiLU(),
            nn.Linear(embed_dim, out_dim * 2 if use_scale_shift_norm else out_dim))
        self.layer2 = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv2d(out_dim, out_dim, 3, padding=1))
        self.shortcut = nn.Identity() if in_dim == out_dim else nn.Conv2d(in_dim, out_dim, 1)

        # zero out the last layer params
        nn.init.zeros_(self.layer2[-1].weight)
    
    def forward(self, x, e, reference=None):
        identity = self.resample(x, reference)
        x = self.layer1[-1](self.resample(self.layer1[:-1](x), reference))
        e = self.embedding(e).unsqueeze(-1).unsqueeze(-1).type(x.dtype)
        if self.use_scale_shift_norm:
            scale, shift = e.chunk(2, dim=1)
            x = self.layer2[0](x) * (1 + scale) + shift
            x = self.layer2[1:](x)
        else:
            x = x + e
            x = self.layer2(x)
        x = x + self.shortcut(identity)
        return x

class AttentionBlock(nn.Module):

    def __init__(self, dim, context_dim=None, num_heads=None, head_dim=None):
        # consider head_dim first, then num_heads
        num_heads = dim // head_dim if head_dim else num_heads
        head_dim = dim // num_heads
        assert num_heads * head_dim == dim
        super(AttentionBlock, self).__init__()
        self.dim = dim
        self.context_dim = context_dim
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.scale = math.pow(head_dim, -0.25)

        # layers
        self.norm = nn.GroupNorm(32, dim)
        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
        if context_dim is not None:
            self.context_kv = nn.Linear(context_dim, dim * 2)
        self.proj = nn.Conv2d(dim, dim, 1)

        # zero out the last layer params
        nn.init.zeros_(self.proj.weight)
    
    def forward(self, x, context=None):
        r"""x:       [B, C, H, W].
            context: [B, L, C] or None.
        """
        identity = x
        b, c, h, w, n, d = *x.size(), self.num_heads, self.head_dim

        # compute query, key, value
        x = self.norm(x)
        q, k, v = self.to_qkv(x).view(b, n * 3, d, h * w).chunk(3, dim=1)
        if context is not None:
            ck, cv = self.context_kv(context).reshape(b, -1, n * 2, d).permute(0, 2, 3, 1).chunk(2, dim=1)
            k = torch.cat([ck, k], dim=-1)
            v = torch.cat([cv, v], dim=-1)

        # compute attention
        attn = torch.matmul(q.transpose(-1, -2) * self.scale, k * self.scale)
        attn = F.softmax(attn, dim=-1)

        # gather context
        x = torch.matmul(v, attn.transpose(-1, -2))
        x = x.reshape(b, c, h, w)

        # output
        x = self.proj(x)
        return x + identity


class TemporalAttentionBlock(nn.Module):
    def __init__(
        self,
        dim,
        heads = 4,
        dim_head = 32,
        rotary_emb = None,
        use_image_dataset = False,
        use_sim_mask = False
    ):
        super().__init__()
        # consider num_heads first, as pos_bias needs fixed num_heads 
        # heads = dim // dim_head if dim_head else heads
        dim_head = dim // heads
        assert heads * dim_head == dim
        self.use_image_dataset = use_image_dataset
        self.use_sim_mask = use_sim_mask

        self.scale = dim_head ** -0.5
        self.heads = heads
        hidden_dim = dim_head * heads

        self.norm = nn.GroupNorm(32, dim)
        self.rotary_emb = rotary_emb
        self.to_qkv = nn.Linear(dim, hidden_dim * 3)#, bias = False)
        self.to_out = nn.Linear(hidden_dim, dim)#, bias = False)

        # nn.init.zeros_(self.to_out.weight)
        # nn.init.zeros_(self.to_out.bias)

    def forward(
        self,
        x,
        pos_bias = None,
        focus_present_mask = None,
        video_mask = None
    ):

        identity = x
        n, height, device = x.shape[2], x.shape[-2], x.device

        x = self.norm(x)
        x = rearrange(x, 'b c f h w -> b (h w) f c')

        qkv = self.to_qkv(x).chunk(3, dim = -1)

        if exists(focus_present_mask) and focus_present_mask.all():
            # if all batch samples are focusing on present
            # it would be equivalent to passing that token's values （v=qkv[-1]） through to the output
            values = qkv[-1]
            out = self.to_out(values)
            out = rearrange(out, 'b (h w) f c -> b c f h w', h = height)

            return out + identity

        # split out heads
        # q, k, v = rearrange_many(qkv, '... n (h d) -> ... h n d', h = self.heads)
        # shape [b (hw) h n c/h], n=f
        q= rearrange(qkv[0], '... n (h d) -> ... h n d', h = self.heads)
        k= rearrange(qkv[1], '... n (h d) -> ... h n d', h = self.heads)
        v= rearrange(qkv[2], '... n (h d) -> ... h n d', h = self.heads)


        # scale

        q = q * self.scale

        # rotate positions into queries and keys for time attention
        if exists(self.rotary_emb):
            q = self.rotary_emb.rotate_queries_or_keys(q)
            k = self.rotary_emb.rotate_queries_or_keys(k)

        # similarity
        # shape [b (hw) h n n], n=f
        sim = torch.einsum('... h i d, ... h j d -> ... h i j', q, k)

        # relative positional bias

        if exists(pos_bias):
            # print(sim.shape,pos_bias.shape)
            sim = sim + pos_bias

        if (focus_present_mask is None and video_mask is not None):
            #video_mask: [B, n]
            mask = video_mask[:, None, :] * video_mask[:, :, None] # [b,n,n]
            mask = mask.unsqueeze(1).unsqueeze(1) #[b,1,1,n,n]
            sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)
        elif exists(focus_present_mask) and not (~focus_present_mask).all():
            attend_all_mask = torch.ones((n, n), device = device, dtype = torch.bool)
            attend_self_mask = torch.eye(n, device = device, dtype = torch.bool)

            mask = torch.where(
                rearrange(focus_present_mask, 'b -> b 1 1 1 1'),
                rearrange(attend_self_mask, 'i j -> 1 1 1 i j'),
                rearrange(attend_all_mask, 'i j -> 1 1 1 i j'),
            )

            sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)

        if self.use_sim_mask:
            sim_mask = torch.tril(torch.ones((n, n), device = device, dtype = torch.bool), diagonal=0)
            sim = sim.masked_fill(~sim_mask, -torch.finfo(sim.dtype).max)

        # numerical stability
        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
        attn = sim.softmax(dim = -1)

        # aggregate values

        out = torch.einsum('... h i j, ... h j d -> ... h i d', attn, v)
        out = rearrange(out, '... h n d -> ... n (h d)')
        out = self.to_out(out)

        out = rearrange(out, 'b (h w) f c -> b c f h w', h = height)

        if self.use_image_dataset:
            out = identity + 0*out
        else:
            out = identity + out
        return out

class TemporalTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None,
                 disable_self_attn=False, use_linear=False,
                 use_checkpoint=True, only_self_att=True, multiply_zero=False):
        super().__init__()
        self.multiply_zero = multiply_zero
        self.only_self_att = only_self_att
        self.use_adaptor = False
        if self.only_self_att:
            context_dim = None
        if not isinstance(context_dim, list):
            context_dim = [context_dim]
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv1d(in_channels, 
                                    inner_dim,
                                    kernel_size=1,
                                    stride=1,
                                    padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)
            if self.use_adaptor:
                self.adaptor_in = nn.Linear(frames, frames)

        self.transformer_blocks = nn.ModuleList(
            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], 
                checkpoint=use_checkpoint)
                for d in range(depth)]
        )
        if not use_linear:
            self.proj_out = zero_module(nn.Conv1d(inner_dim,
                                                in_channels,
                                                kernel_size=1,
                                                stride=1,
                                                padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
            if self.use_adaptor:
                self.adaptor_out = nn.Linear(frames, frames)
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if self.only_self_att:
            context = None
        if not isinstance(context, list):
            context = [context]
        b, c, f, h, w = x.shape
        x_in = x
        x = self.norm(x)

        if not self.use_linear:
            x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous()
            x = self.proj_in(x)
        # [16384, 16, 320]
        if self.use_linear:
            x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous()
            x = self.proj_in(x)

        if self.only_self_att:
            x = rearrange(x, 'bhw c f -> bhw f c').contiguous()
            for i, block in enumerate(self.transformer_blocks):
                x = block(x)
            x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous()
        else:
            x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous()
            for i, block in enumerate(self.transformer_blocks):
                # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous()
                context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous()
                # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
                for j in range(b):
                    context_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous()
                    x[j] = block(x[j], context=context_i_j)
        
        if self.use_linear:
            x = self.proj_out(x)
            x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            # x = rearrange(x, 'bhw f c -> bhw c f').contiguous()
            x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous()
            x = self.proj_out(x)
            x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous()
        
        if self.multiply_zero:
            x = 0.0 * x + x_in
        else:
            x = x + x_in
        return x

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim = -1)
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        b, n, _, h = *x.shape, self.heads
        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)

        dots = torch.einsum('b h i d, b h j d -> b h i j', q, k) * self.scale

        attn = self.attend(dots)

        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class PreNormattention(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs) + x

class TransformerV2(nn.Module):
    def __init__(self, heads=8, dim=2048, dim_head_k=256, dim_head_v=256, dropout_atte = 0.05, mlp_dim=2048, dropout_ffn = 0.05, depth=1):
        super().__init__()
        self.layers = nn.ModuleList([])
        self.depth = depth
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNormattention(dim, Attention(dim, heads = heads, dim_head = dim_head_k, dropout = dropout_atte)),
                FeedForward(dim, mlp_dim, dropout = dropout_ffn),
            ]))
    def forward(self, x):
        # if self.depth
        for attn, ff in self.layers[:1]:
            x = attn(x)
            x = ff(x) + x
        if self.depth > 1:
            for attn, ff in self.layers[1:]:
                x = attn(x)
                x = ff(x) + x
        return x

class TemporalTransformer_attemask(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None,
                 disable_self_attn=False, use_linear=False,
                 use_checkpoint=True, only_self_att=True, multiply_zero=False):
        super().__init__()
        self.multiply_zero = multiply_zero
        self.only_self_att = only_self_att
        self.use_adaptor = False
        if self.only_self_att:
            context_dim = None
        if not isinstance(context_dim, list):
            context_dim = [context_dim]
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv1d(in_channels, 
                                    inner_dim,
                                    kernel_size=1,
                                    stride=1,
                                    padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)
            if self.use_adaptor:
                self.adaptor_in = nn.Linear(frames, frames)

        self.transformer_blocks = nn.ModuleList(
            [BasicTransformerBlock_attemask(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], 
                checkpoint=use_checkpoint)
                for d in range(depth)]
        )
        if not use_linear:
            self.proj_out = zero_module(nn.Conv1d(inner_dim,
                                                in_channels,
                                                kernel_size=1,
                                                stride=1,
                                                padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
            if self.use_adaptor:
                self.adaptor_out = nn.Linear(frames, frames)
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if self.only_self_att:
            context = None
        if not isinstance(context, list):
            context = [context]
        b, c, f, h, w = x.shape
        x_in = x
        x = self.norm(x)

        if not self.use_linear:
            x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous()
            x = self.proj_in(x)
        # [16384, 16, 320]
        if self.use_linear:
            x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous()
            x = self.proj_in(x)

        if self.only_self_att:
            x = rearrange(x, 'bhw c f -> bhw f c').contiguous()
            for i, block in enumerate(self.transformer_blocks):
                x = block(x)
            x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous()
        else:
            x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous()
            for i, block in enumerate(self.transformer_blocks):
                # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous()
                context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous()
                # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
                for j in range(b):
                    context_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous()
                    x[j] = block(x[j], context=context_i_j)
        
        if self.use_linear:
            x = self.proj_out(x)
            x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            # x = rearrange(x, 'bhw f c -> bhw c f').contiguous()
            x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous()
            x = self.proj_out(x)
            x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous()
        
        if self.multiply_zero:
            x = 0.0 * x + x_in
        else:
            x = x + x_in
        return x

class TemporalAttentionMultiBlock(nn.Module):
    def __init__(
        self,
        dim,
        heads=4,
        dim_head=32,
        rotary_emb=None,
        use_image_dataset=False,
        use_sim_mask=False,
        temporal_attn_times=1,
    ):
        super().__init__()
        self.att_layers = nn.ModuleList(
                [TemporalAttentionBlock(dim, heads, dim_head, rotary_emb, use_image_dataset, use_sim_mask)
                    for _ in range(temporal_attn_times)]
                )
    
    def forward(
        self,
        x,
        pos_bias = None,
        focus_present_mask = None,
        video_mask = None
    ):
        for layer in self.att_layers:
            x = layer(x, pos_bias, focus_present_mask, video_mask)
        return x


class InitTemporalConvBlock(nn.Module):

    def __init__(self, in_dim, out_dim=None, dropout=0.0,use_image_dataset=False):
        super(InitTemporalConvBlock, self).__init__()
        if out_dim is None:
            out_dim = in_dim#int(1.5*in_dim)
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.use_image_dataset = use_image_dataset

        # conv layers
        self.conv = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0)))

        # zero out the last layer params,so the conv block is identity
        # nn.init.zeros_(self.conv1[-1].weight)
        # nn.init.zeros_(self.conv1[-1].bias)
        nn.init.zeros_(self.conv[-1].weight)
        nn.init.zeros_(self.conv[-1].bias) 

    def forward(self, x):
        identity = x
        x = self.conv(x)
        if self.use_image_dataset:
            x = identity + 0*x
        else:
            x = identity + x
        return x

class TemporalConvBlock(nn.Module):

    def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset= False):
        super(TemporalConvBlock, self).__init__()
        if out_dim is None:
            out_dim = in_dim#int(1.5*in_dim)
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.use_image_dataset = use_image_dataset

        # conv layers
        self.conv1 = nn.Sequential(
            nn.GroupNorm(32, in_dim),
            nn.SiLU(),
            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0)))
        self.conv2 = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0)))

        # zero out the last layer params,so the conv block is identity
        # nn.init.zeros_(self.conv1[-1].weight)
        # nn.init.zeros_(self.conv1[-1].bias)
        nn.init.zeros_(self.conv2[-1].weight)
        nn.init.zeros_(self.conv2[-1].bias) 

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.conv2(x)
        if self.use_image_dataset:
            x = identity + 0*x
        else:
            x = identity + x
        return x

class TemporalConvBlock_v2(nn.Module):
    def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset=False):
        super(TemporalConvBlock_v2, self).__init__()
        if out_dim is None:
            out_dim = in_dim # int(1.5*in_dim)
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.use_image_dataset = use_image_dataset

        # conv layers
        self.conv1 = nn.Sequential(
            nn.GroupNorm(32, in_dim),
            nn.SiLU(),
            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0)))
        self.conv2 = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0)))
        self.conv3 = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0)))
        self.conv4 = nn.Sequential(
            nn.GroupNorm(32, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0)))

        # zero out the last layer params,so the conv block is identity
        nn.init.zeros_(self.conv4[-1].weight)
        nn.init.zeros_(self.conv4[-1].bias) 

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)

        if self.use_image_dataset:
            x = identity + 0.0 * x
        else:
            x = identity + x
        return x


class DropPath(nn.Module):
    r"""DropPath but without rescaling and supports optional all-zero and/or all-keep.
    """
    def __init__(self, p):
        super(DropPath, self).__init__()
        self.p = p
    
    def forward(self, *args, zero=None, keep=None):
        if not self.training:
            return args[0] if len(args) == 1 else args
        
        # params
        x = args[0]
        b = x.size(0)
        n = (torch.rand(b) < self.p).sum()

        # non-zero and non-keep mask
        mask = x.new_ones(b, dtype=torch.bool)
        if keep is not None:
            mask[keep] = False
        if zero is not None:
            mask[zero] = False
        
        # drop-path index
        index = torch.where(mask)[0]
        index = index[torch.randperm(len(index))[:n]]
        if zero is not None:
            index = torch.cat([index, torch.where(zero)[0]], dim=0)
        
        # drop-path multiplier
        multiplier = x.new_ones(b)
        multiplier[index] = 0.0
        output = tuple(u * self.broadcast(multiplier, u) for u in args)
        return output[0] if len(args) == 1 else output
    
    def broadcast(self, src, dst):
        assert src.size(0) == dst.size(0)
        shape = (dst.size(0), ) + (1, ) * (dst.ndim - 1)
        return src.view(shape)



