import torch
import torch.nn as nn
import pdb
import math
from transformers.activations import ACT2FN
from einops import rearrange, reduce, repeat
from inspect import isfunction
import math
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any

try:
    import xformers
    import xformers.ops

    XFORMERS_IS_AVAILBLE = True
except:
    XFORMERS_IS_AVAILBLE = False

import importlib
import numpy as np
import cv2, os
import torch.distributed as dist


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
    return total_params


def check_istarget(name, para_list):
    """
    name: full name of source para
    para_list: partial name of target para
    """
    istarget = False
    for para in para_list:
        if para in name:
            return True
    return istarget


def instantiate_from_config(config):
    if not "target" in config:
        if config == "__is_first_stage__":
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")

    return get_obj_from_str(config["target"])(**config.get("params", dict()))


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def load_npz_from_dir(data_dir):
    data = [
        np.load(os.path.join(data_dir, data_name))["arr_0"]
        for data_name in os.listdir(data_dir)
    ]
    data = np.concatenate(data, axis=0)
    return data


def load_npz_from_paths(data_paths):
    data = [np.load(data_path)["arr_0"] for data_path in data_paths]
    data = np.concatenate(data, axis=0)
    return data


def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None):
    h, w = image.shape[:2]
    if resize_short_edge is not None:
        k = resize_short_edge / min(h, w)
    else:
        k = max_resolution / (h * w)
        k = k**0.5
    h = int(np.round(h * k / 64)) * 64
    w = int(np.round(w * k / 64)) * 64
    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
    return image


def setup_dist(args):
    if dist.is_initialized():
        return
    torch.cuda.set_device(args.local_rank)
    torch.distributed.init_process_group("nccl", init_method="env://")

    
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!

import torch.nn as nn
import math
from inspect import isfunction
import torch
from torch import nn
import torch.distributed as dist


def gather_data(data, return_np=True):
    """gather data from multiple processes to one list"""
    data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
    dist.all_gather(data_list, data)  # gather not supported with NCCL
    if return_np:
        data_list = [data.cpu().numpy() for data in data_list]
    return data_list


def autocast(f):
    def do_autocast(*args, **kwargs):
        with torch.cuda.amp.autocast(
            enabled=True,
            dtype=torch.get_autocast_gpu_dtype(),
            cache_enabled=torch.is_autocast_cache_enabled(),
        ):
            return f(*args, **kwargs)

    return do_autocast


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def noise_like(shape, device, repeat=False):
    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
        shape[0], *((1,) * (len(shape) - 1))
    )
    noise = lambda: torch.randn(shape, device=device)
    return repeat_noise() if repeat else noise()


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


def exists(val):
    return val is not None


def identity(*args, **kwargs):
    return nn.Identity()


def uniq(arr):
    return {el: True for el in arr}.keys()


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def shape_to_str(x):
    shape_str = "x".join([str(x) for x in x.shape])
    return shape_str


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor



def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


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


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def nonlinearity(type="silu"):
    if type == "silu":
        return nn.SiLU()
    elif type == "leaky_relu":
        return nn.LeakyReLU()


class GroupNormSpecific(nn.GroupNorm):
    def forward(self, x):
        if x.dtype == torch.float16 or x.dtype == torch.bfloat16:
            return super().forward(x).type(x.dtype)
        else:
            return super().forward(x.float()).type(x.dtype)


def normalization(channels, num_groups=32):
    """
    Make a standard normalization layer.
    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNormSpecific(num_groups, channels)


class HybridConditioner(nn.Module):

    def __init__(self, c_concat_config, c_crossattn_config):
        super().__init__()
        self.concat_conditioner = instantiate_from_config(c_concat_config)
        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)

    def forward(self, c_concat, c_crossattn):
        c_concat = self.concat_conditioner(c_concat)
        c_crossattn = self.crossattn_conditioner(c_crossattn)
        return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]}

def exists(val):
    return val is not None


def uniq(arr):
    return {el: True for el in arr}.keys()


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


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# 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)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        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)


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


def Normalize(in_channels, num_groups=32):
    return torch.nn.GroupNorm(
        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
    )


class RelativePosition(nn.Module):
    """https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py"""

    def __init__(self, num_units, max_relative_position):
        super().__init__()
        self.num_units = num_units
        self.max_relative_position = max_relative_position
        self.embeddings_table = nn.Parameter(
            torch.Tensor(max_relative_position * 2 + 1, num_units)
        )
        nn.init.xavier_uniform_(self.embeddings_table)

    def forward(self, length_q, length_k):
        device = self.embeddings_table.device
        range_vec_q = torch.arange(length_q, device=device)
        range_vec_k = torch.arange(length_k, device=device)
        distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
        distance_mat_clipped = torch.clamp(
            distance_mat, -self.max_relative_position, self.max_relative_position
        )
        final_mat = distance_mat_clipped + self.max_relative_position
        # final_mat = torch.LongTensor(final_mat).to(self.embeddings_table.device)
        # final_mat = torch.tensor(final_mat, device=self.embeddings_table.device, dtype=torch.long)
        final_mat = final_mat.long()
        embeddings = self.embeddings_table[final_mat]
        return embeddings


class TemporalCrossAttention(nn.Module):
    def __init__(
        self,
        query_dim,
        context_dim=None,
        heads=8,
        dim_head=64,
        dropout=0.0,
        temporal_length=None,  # For relative positional representation and image-video joint training.
        image_length=None,  # For image-video joint training.
        use_relative_position=False,  # whether use relative positional representation in temporal attention.
        img_video_joint_train=False,  # For image-video joint training.
        use_tempoal_causal_attn=False,
        bidirectional_causal_attn=False,
        tempoal_attn_type=None,
        joint_train_mode="same_batch",
        **kwargs,
    ):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)
        self.context_dim = context_dim

        self.scale = dim_head**-0.5
        self.heads = heads
        self.temporal_length = temporal_length
        self.use_relative_position = use_relative_position
        self.img_video_joint_train = img_video_joint_train
        self.bidirectional_causal_attn = bidirectional_causal_attn
        self.joint_train_mode = joint_train_mode
        assert joint_train_mode in ["same_batch", "diff_batch"]
        self.tempoal_attn_type = tempoal_attn_type

        if bidirectional_causal_attn:
            assert use_tempoal_causal_attn
        if tempoal_attn_type:
            assert tempoal_attn_type in ["sparse_causal", "sparse_causal_first"]
            assert not use_tempoal_causal_attn
            assert not (
                img_video_joint_train and (self.joint_train_mode == "same_batch")
            )
        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)

        assert not (
            img_video_joint_train
            and (self.joint_train_mode == "same_batch")
            and use_tempoal_causal_attn
        )
        if img_video_joint_train:
            if self.joint_train_mode == "same_batch":
                mask = torch.ones(
                    [1, temporal_length + image_length, temporal_length + image_length]
                )
                # mask[:, image_length:, :] = 0
                # mask[:, :, image_length:] = 0
                mask[:, temporal_length:, :] = 0
                mask[:, :, temporal_length:] = 0
                self.mask = mask
            else:
                self.mask = None
        elif use_tempoal_causal_attn:
            # normal causal attn
            self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
        elif tempoal_attn_type == "sparse_causal":
            # all frames interact with only the `prev` & self frame
            mask1 = torch.tril(
                torch.ones([1, temporal_length, temporal_length])
            ).bool()  # true indicates keeping
            mask2 = torch.zeros(
                [1, temporal_length, temporal_length]
            )  # initialize to same shape with mask1
            mask2[:, 2:temporal_length, : temporal_length - 2] = torch.tril(
                torch.ones([1, temporal_length - 2, temporal_length - 2])
            )
            mask2 = (1 - mask2).bool()  # false indicates masking
            self.mask = mask1 & mask2
        elif tempoal_attn_type == "sparse_causal_first":
            # all frames interact with only the `first` & self frame
            mask1 = torch.tril(
                torch.ones([1, temporal_length, temporal_length])
            ).bool()  # true indicates keeping
            mask2 = torch.zeros([1, temporal_length, temporal_length])
            mask2[:, 2:temporal_length, 1 : temporal_length - 1] = torch.tril(
                torch.ones([1, temporal_length - 2, temporal_length - 2])
            )
            mask2 = (1 - mask2).bool()  # false indicates masking
            self.mask = mask1 & mask2
        else:
            self.mask = None

        if use_relative_position:
            assert temporal_length is not None
            self.relative_position_k = RelativePosition(
                num_units=dim_head, max_relative_position=temporal_length
            )
            self.relative_position_v = RelativePosition(
                num_units=dim_head, max_relative_position=temporal_length
            )

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

        nn.init.constant_(self.to_q.weight, 0)
        nn.init.constant_(self.to_k.weight, 0)
        nn.init.constant_(self.to_v.weight, 0)
        nn.init.constant_(self.to_out[0].weight, 0)
        nn.init.constant_(self.to_out[0].bias, 0)

    def forward(self, x, context=None, mask=None):
        # if context is None:
        #     print(f'[Temp Attn] x={x.shape},context=None')
        # else:
        #     print(f'[Temp Attn] x={x.shape},context={context.shape}')

        nh = self.heads
        out = x
        q = self.to_q(out)
        # if context is not None:
        #     print(f'temporal context 1 ={context.shape}')
        # print(f'x={x.shape}')
        context = default(context, x)
        # print(f'temporal context 2 ={context.shape}')
        k = self.to_k(context)
        v = self.to_v(context)
        # print(f'q ={q.shape},k={k.shape}')

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=nh), (q, k, v))
        sim = einsum("b i d, b j d -> b i j", q, k) * self.scale

        if self.use_relative_position:
            len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
            k2 = self.relative_position_k(len_q, len_k)
            sim2 = einsum("b t d, t s d -> b t s", q, k2) * self.scale  # TODO check
            sim += sim2
        # print('mask',mask)
        if exists(self.mask):
            if mask is None:
                mask = self.mask.to(sim.device)
            else:
                mask = self.mask.to(sim.device).bool() & mask  # .to(sim.device)
        else:
            mask = mask
            # if self.img_video_joint_train:
            #     # process mask (make mask same shape with sim)
            #     c, h, w = mask.shape
            #     c, t, s = sim.shape
            #     # assert(h == w and t == s),f"mask={mask.shape}, sim={sim.shape}, h={h}, w={w}, t={t}, s={s}"

            #     if h > t:
            #         mask = mask[:, :t, :]
            #     elif h < t: # pad zeros to mask (no attention) only initial mask =1 area compute weights
            #         mask_ = torch.zeros([c,t,w]).to(mask.device)
            #         mask_[:, :h, :] = mask
            #         mask = mask_
            #     c, h, w = mask.shape
            #     if w > s:
            #         mask = mask[:, :, :s]
            #     elif w < s: # pad zeros to mask
            #         mask_ = torch.zeros([c,h,s]).to(mask.device)
            #         mask_[:, :, :w] = mask
            #         mask = mask_

            # max_neg_value = -torch.finfo(sim.dtype).max
            # sim = sim.float().masked_fill(mask == 0, max_neg_value)
        if mask is not None:
            max_neg_value = -1e9
            sim = sim + (1 - mask.float()) * max_neg_value  # 1=masking,0=no masking
            # print('sim after masking: ', sim)

            # if torch.isnan(sim).any() or torch.isinf(sim).any() or (not sim.any()):
            #     print(f'sim [after masking], isnan={torch.isnan(sim).any()}, isinf={torch.isinf(sim).any()}, allzero={not sim.any()}')

        attn = sim.softmax(dim=-1)
        # print('attn after softmax: ', attn)
        # if torch.isnan(attn).any() or torch.isinf(attn).any() or (not attn.any()):
        #     print(f'attn [after softmax], isnan={torch.isnan(attn).any()}, isinf={torch.isinf(attn).any()}, allzero={not attn.any()}')

        # attn = torch.where(torch.isnan(attn), torch.full_like(attn,0), attn)
        # if torch.isinf(attn.detach()).any():
        #     import pdb;pdb.set_trace()
        # if torch.isnan(attn.detach()).any():
        #     import pdb;pdb.set_trace()
        out = einsum("b i j, b j d -> b i d", attn, v)

        if self.bidirectional_causal_attn:
            mask_reverse = torch.triu(
                torch.ones(
                    [1, self.temporal_length, self.temporal_length], device=sim.device
                )
            )
            sim_reverse = sim.float().masked_fill(mask_reverse == 0, max_neg_value)
            attn_reverse = sim_reverse.softmax(dim=-1)
            out_reverse = einsum("b i j, b j d -> b i d", attn_reverse, v)
            out += out_reverse

        if self.use_relative_position:
            v2 = self.relative_position_v(len_q, len_v)
            out2 = einsum("b t s, t s d -> b t d", attn, v2)  # TODO check
            out += out2  # TODO check：先add还是先merge head？先计算rpr，on split head之后的数据，然后再merge。
        out = rearrange(out, "(b h) n d -> b n (h d)", h=nh)  # merge head
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b (h w) c")
        k = rearrange(k, "b c h w -> b c (h w)")
        w_ = torch.einsum("bij,bjk->bik", q, k)

        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, "b c h w -> b c (h w)")
        w_ = rearrange(w_, "b i j -> b j i")
        h_ = torch.einsum("bij,bjk->bik", v, w_)
        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
        h_ = self.proj_out(h_)

        return x + h_


class CrossAttention(nn.Module):
    def __init__(
        self,
        query_dim,
        context_dim=None,
        heads=8,
        dim_head=64,
        dropout=0.0,
        sa_shared_kv=False,
        shared_type="only_first",
        **kwargs,
    ):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)
        self.sa_shared_kv = sa_shared_kv
        assert shared_type in [
            "only_first",
            "all_frames",
            "first_and_prev",
            "only_prev",
            "full",
            "causal",
            "full_qkv",
        ]
        self.shared_type = shared_type

        self.scale = dim_head**-0.5
        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] = None

    def forward(self, x, context=None, mask=None):
        h = self.heads
        b = x.shape[0]

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)
        if self.sa_shared_kv:
            if self.shared_type == "only_first":
                k, v = map(
                    lambda xx: rearrange(xx[0].unsqueeze(0), "b n c -> (b n) c")
                    .unsqueeze(0)
                    .repeat(b, 1, 1),
                    (k, v),
                )
            else:
                raise NotImplementedError

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))

        sim = einsum("b i d, b j d -> b i j", q, k) * self.scale

        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
        attn = sim.softmax(dim=-1)

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

    def efficient_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=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 VideoSpatialCrossAttention(CrossAttention):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0):
        super().__init__(query_dim, context_dim, heads, dim_head, dropout)

    def forward(self, x, context=None, mask=None):
        b, c, t, h, w = x.shape
        if context is not None:
            context = context.repeat(t, 1, 1)
        x = super.forward(spatial_attn_reshape(x), context=context) + x
        return spatial_attn_reshape_back(x, b, h)


def spatial_attn_reshape(x):
    return rearrange(x, "b c t h w -> (b t) (h w) c")


def spatial_attn_reshape_back(x, b, h):
    return rearrange(x, "(b t) (h w) c -> b c t h w", b=b, h=h)


def temporal_attn_reshape(x):
    return rearrange(x, "b c t h w -> (b h w) t c")


def temporal_attn_reshape_back(x, b, h, w):
    return rearrange(x, "(b h w) t c -> b c t h w", b=b, h=h, w=w)


def local_spatial_temporal_attn_reshape(x, window_size):
    B, C, T, H, W = x.shape
    NH = H // window_size
    NW = W // window_size
    # x = x.view(B, C, T, NH, window_size, NW, window_size)
    # tokens = x.permute(0, 1, 2, 3, 5, 4, 6).contiguous()
    # tokens = tokens.view(-1, window_size, window_size, C)
    x = rearrange(
        x,
        "b c t (nh wh) (nw ww) -> b c t nh wh nw ww",
        nh=NH,
        nw=NW,
        wh=window_size,
        ww=window_size,
    ).contiguous()  # # B, C, T, NH, NW, window_size, window_size
    x = rearrange(
        x, "b c t nh wh nw ww -> (b nh nw) (t wh ww) c"
    )  # (B, NH, NW) (T, window_size, window_size) C
    return x


def local_spatial_temporal_attn_reshape_back(x, window_size, b, h, w, t):
    B, L, C = x.shape
    NH = h // window_size
    NW = w // window_size
    x = rearrange(
        x,
        "(b nh nw) (t wh ww) c -> b c t nh wh nw ww",
        b=b,
        nh=NH,
        nw=NW,
        t=t,
        wh=window_size,
        ww=window_size,
    )
    x = rearrange(x, "b c t nh wh nw ww -> b c t (nh wh) (nw ww)")
    return x


class SpatialTemporalTransformer(nn.Module):
    """
    Transformer block for video-like data (5D tensor).
    First, project the input (aka embedding) with NO reshape.
    Then apply standard transformer action.
    The 5D -> 3D reshape operation will be done in the specific attention module.
    """

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        context_dim=None,
        # Temporal stuff
        temporal_length=None,
        image_length=None,
        use_relative_position=True,
        img_video_joint_train=False,
        cross_attn_on_tempoal=False,
        temporal_crossattn_type="selfattn",
        order="stst",
        temporalcrossfirst=False,
        split_stcontext=False,
        temporal_context_dim=None,
        **kwargs,
    ):
        super().__init__()

        self.in_channels = in_channels
        inner_dim = n_heads * d_head

        self.norm = Normalize(in_channels)
        self.proj_in = nn.Conv3d(
            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
        )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlockST(
                    inner_dim,
                    n_heads,
                    d_head,
                    dropout=dropout,
                    # cross attn
                    context_dim=context_dim,
                    # temporal attn
                    temporal_length=temporal_length,
                    image_length=image_length,
                    use_relative_position=use_relative_position,
                    img_video_joint_train=img_video_joint_train,
                    temporal_crossattn_type=temporal_crossattn_type,
                    order=order,
                    temporalcrossfirst=temporalcrossfirst,
                    split_stcontext=split_stcontext,
                    temporal_context_dim=temporal_context_dim,
                    **kwargs,
                )
                for d in range(depth)
            ]
        )

        self.proj_out = zero_module(
            nn.Conv3d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
        )

    def forward(self, x, context=None, temporal_context=None, **kwargs):
        # note: if no context is given, cross-attention defaults to self-attention
        assert x.dim() == 5, f"x shape = {x.shape}"
        b, c, t, h, w = x.shape
        x_in = x

        x = self.norm(x)
        x = self.proj_in(x)

        for block in self.transformer_blocks:
            x = block(x, context=context, temporal_context=temporal_context, **kwargs)

        x = self.proj_out(x)
        return x + x_in


class STAttentionBlock2(nn.Module):
    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,  # not used, only used in ResBlock
        use_new_attention_order=False,  # QKVAttention or QKVAttentionLegacy
        temporal_length=16,  # used in relative positional representation.
        image_length=8,  # used for image-video joint training.
        use_relative_position=False,  # whether use relative positional representation in temporal attention.
        img_video_joint_train=False,
        # norm_type="groupnorm",
        attn_norm_type="group",
        use_tempoal_causal_attn=False,
    ):
        """
        version 1: guided_diffusion implemented version
        version 2: remove args input argument
        """
        super().__init__()

        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint

        self.temporal_length = temporal_length
        self.image_length = image_length
        self.use_relative_position = use_relative_position
        self.img_video_joint_train = img_video_joint_train
        self.attn_norm_type = attn_norm_type
        assert self.attn_norm_type in ["group", "no_norm"]
        self.use_tempoal_causal_attn = use_tempoal_causal_attn

        if self.attn_norm_type == "group":
            self.norm_s = normalization(channels)
            self.norm_t = normalization(channels)

        self.qkv_s = conv_nd(1, channels, channels * 3, 1)
        self.qkv_t = conv_nd(1, channels, channels * 3, 1)

        if self.img_video_joint_train:
            mask = torch.ones(
                [1, temporal_length + image_length, temporal_length + image_length]
            )
            mask[:, temporal_length:, :] = 0
            mask[:, :, temporal_length:] = 0
            self.register_buffer("mask", mask)
        else:
            self.mask = None

        if use_new_attention_order:
            # split qkv before split heads
            self.attention_s = QKVAttention(self.num_heads)
            self.attention_t = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention_s = QKVAttentionLegacy(self.num_heads)
            self.attention_t = QKVAttentionLegacy(self.num_heads)

        if use_relative_position:
            self.relative_position_k = RelativePosition(
                num_units=channels // self.num_heads,
                max_relative_position=temporal_length,
            )
            self.relative_position_v = RelativePosition(
                num_units=channels // self.num_heads,
                max_relative_position=temporal_length,
            )

        self.proj_out_s = zero_module(
            conv_nd(1, channels, channels, 1)
        )  # conv_dim, in_channels, out_channels, kernel_size
        self.proj_out_t = zero_module(
            conv_nd(1, channels, channels, 1)
        )  # conv_dim, in_channels, out_channels, kernel_size

    def forward(self, x, mask=None):
        b, c, t, h, w = x.shape

        # spatial
        out = rearrange(x, "b c t h w -> (b t) c (h w)")
        if self.attn_norm_type == "no_norm":
            qkv = self.qkv_s(out)
        else:
            qkv = self.qkv_s(self.norm_s(out))
        out = self.attention_s(qkv)
        out = self.proj_out_s(out)
        out = rearrange(out, "(b t) c (h w) -> b c t h w", b=b, h=h)
        x += out

        # temporal
        out = rearrange(x, "b c t h w -> (b h w) c t")
        if self.attn_norm_type == "no_norm":
            qkv = self.qkv_t(out)
        else:
            qkv = self.qkv_t(self.norm_t(out))

        # relative positional embedding
        if self.use_relative_position:
            len_q = qkv.size()[-1]
            len_k, len_v = len_q, len_q
            k_rp = self.relative_position_k(len_q, len_k)
            v_rp = self.relative_position_v(len_q, len_v)  # [T,T,head_dim]
            out = self.attention_t(
                qkv,
                rp=(k_rp, v_rp),
                mask=self.mask,
                use_tempoal_causal_attn=self.use_tempoal_causal_attn,
            )
        else:
            out = self.attention_t(
                qkv,
                rp=None,
                mask=self.mask,
                use_tempoal_causal_attn=self.use_tempoal_causal_attn,
            )

        out = self.proj_out_t(out)
        out = rearrange(out, "(b h w) c t -> b c t h w", b=b, h=h, w=w)

        return x + out


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv, rp=None, mask=None):
        """
        Apply QKV attention.

        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        if rp is not None or mask is not None:
            raise NotImplementedError
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = torch.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = torch.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv, rp=None, mask=None, use_tempoal_causal_attn=False):
        """
        Apply QKV attention.

        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        # print('qkv', qkv.size())
        qkv=qkv.contiguous()
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        # print('bs, self.n_heads, ch, length', bs, self.n_heads, ch, length)

        weight = torch.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        # weight:[b,t,s] b=bs*n_heads*T

        if rp is not None:
            k_rp, v_rp = rp  # [length, length, head_dim] [8, 8, 48]
            weight2 = torch.einsum(
                "bct,tsc->bst", (q * scale).view(bs * self.n_heads, ch, length), k_rp
            )
            weight += weight2

        if use_tempoal_causal_attn:
            # weight = torch.tril(weight)
            assert mask is None, f"Not implemented for merging two masks!"
            mask = torch.tril(torch.ones(weight.shape))
        else:
            if mask is not None:  # only keep upper-left matrix
                # process mask
                c, t, _ = weight.shape

                if mask.shape[-1] > t:
                    mask = mask[:, :t, :t]
                elif mask.shape[-1] < t:  # pad ones
                    mask_ = torch.zeros([c, t, t]).to(mask.device)
                    t_ = mask.shape[-1]
                    mask_[:, :t_, :t_] = mask
                    mask = mask_
                else:
                    assert (
                        weight.shape[-1] == mask.shape[-1]
                    ), f"weight={weight.shape}, mask={mask.shape}"

        if mask is not None:
            INF = -1e8  # float('-inf')
            weight = weight.float().masked_fill(mask == 0, INF)

        weight = F.softmax(weight.float(), dim=-1).type(
            weight.dtype
        )  # [256, 8, 8] [b, t, t] b=bs*n_heads*h*w,t=nframes
        # weight = F.softmax(weight, dim=-1)#[256, 8, 8] [b, t, t] b=bs*n_heads*h*w,t=nframes
        a = torch.einsum(
            "bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)
        )  # [256, 48, 8] [b, head_dim, t]

        if rp is not None:
            a2 = torch.einsum("bts,tsc->btc", weight, v_rp).transpose(1, 2)  # btc->bct
            a += a2

        return a.reshape(bs, -1, length)


def silu(x):
    # swish
    return x * torch.sigmoid(x)


class SiLU(nn.Module):
    def __init__(self):
        super(SiLU, self).__init__()

    def forward(self, x):
        return silu(x)


def Normalize(in_channels, norm_type="group"):
    assert norm_type in ["group", "batch",'layer']
    if norm_type == "group":
        return torch.nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
        )
    elif norm_type == "batch":
        return torch.nn.SyncBatchNorm(in_channels)
    elif norm_type == "layer":
        return nn.LayerNorm(in_channels)
        
class SamePadConv3d(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        bias=True,
        padding_type="replicate",
    ):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3

        # assumes that the input shape is divisible by stride
        total_pad = tuple([k - s for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]:  # reverse since F.pad starts from last dim
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        
        self.pad_input = pad_input
        self.padding_type = padding_type

        self.conv = nn.Conv3d(
            in_channels, out_channels, kernel_size, stride=stride, padding=0, bias=bias
        )

    def forward(self, x):
        tp=x.dtype
        x = x.float()

        # 执行填充操作
        x_padded = F.pad(x, self.pad_input, mode=self.padding_type)

        # 如果需要，将结果转换回 BFloat16
        x_padded = x_padded.to(tp)
        
        return self.conv(x_padded)

class TemporalAttention(nn.Module):
    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        max_temporal_length=64,
    ):
        """
        a clean multi-head temporal attention
        """
        super().__init__()

        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels

        self.norm = Normalize(channels)
        self.qkv = zero_module(conv_nd(1, channels, channels * 3, 1))
        self.attention = QKVAttention(self.num_heads)
        self.relative_position_k = RelativePosition(
            num_units=channels // self.num_heads,
            max_relative_position=max_temporal_length,
        )
        self.relative_position_v = RelativePosition(
            num_units=channels // self.num_heads,
            max_relative_position=max_temporal_length,
        )
        self.proj_out = zero_module(
            conv_nd(1, channels, channels, 1)
        )  # conv_dim, in_channels, out_channels, kernel_size

    def forward(self, x, mask=None):
        b, c, t, h, w = x.shape
        out = rearrange(x, "b c t h w -> (b h w) c t")
        # torch.Size([4608, 1152, 2])1
        # torch.Size([4608, 3456, 2])2
        # torch.Size([4608, 1152, 2])3
        # torch.Size([4608, 1152, 2])4
        #print(out.shape,end='1\n')
        qkv = self.qkv(self.norm(out))
        #print(qkv.shape,end='2\n')
        
        len_q = qkv.size()[-1]
        len_k, len_v = len_q, len_q

        k_rp = self.relative_position_k(len_q, len_k)
        v_rp = self.relative_position_v(len_q, len_v)  # [T,T,head_dim]
        out = self.attention(qkv, rp=(k_rp, v_rp))
        #print(out.shape,end='3\n')
        out = self.proj_out(out)
        #print(out.shape,end='4\n')
        out = rearrange(out, "(b h w) c t -> b c t h w", b=b, h=h, w=w)

        return x + out
class TemporalAttention_lin(nn.Module):
    def __init__(
        self,
        channels,
        num_heads=8,
        num_head_channels=-1,
        max_temporal_length=64,
    ):
        """
        a clean multi-head temporal attention
        """
        super().__init__()

        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        
        self.norm = nn.LayerNorm(channels)
        #self.norm = Normalize(channels)
        #self.qkv = zero_module(conv_nd(1, channels, channels * 3, 1))
        self.qkv = nn.Linear(channels, channels * 3)
        self.attention = QKVAttention(self.num_heads)
        self.relative_position_k = RelativePosition(
            num_units=channels // self.num_heads,
            max_relative_position=max_temporal_length,
        )
        self.relative_position_v = RelativePosition(
            num_units=channels // self.num_heads,
            max_relative_position=max_temporal_length,
        )
        self.proj_out = nn.Linear(channels, channels)

    def forward(self, x, mask=None):
        b, c, t, h, w = x.shape
        out = rearrange(x, "b c t h w -> (b h w) t c")
        # torch.Size([4608, 1152, 2])1
        # torch.Size([4608, 3456, 2])2
        # torch.Size([4608, 1152, 2])3
        # torch.Size([4608, 1152, 2])4
        #print(out.shape,end='1\n')
        qkv = self.qkv(self.norm(out)).transpose(-1, -2)
        #print(qkv.shape,end='2\n')
        
        len_q = qkv.size()[-1]
        len_k, len_v = len_q, len_q

        k_rp = self.relative_position_k(len_q, len_k)
        v_rp = self.relative_position_v(len_q, len_v)  # [T,T,head_dim]
        
        out = self.attention(qkv, rp=(k_rp, v_rp))
        
        out = self.proj_out(out.transpose(-1, -2)).transpose(-1, -2)
        
        #print(out.shape,end='4\n')
        out = rearrange(out, "(b h w) c t -> b c t h w", b=b, h=h, w=w)

        return x + out
    
class AttnBlock3D(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv3d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv3d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv3d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv3d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        # self.norm.to(x.device)
        # self.norm.to(x.dtype)
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        b, c, t, h, w = q.shape
        # q = q.reshape(b,c,h*w) # bcl
        # q = q.permute(0,2,1)   # bcl -> blc l=hw
        # k = k.reshape(b,c,h*w) # bcl
        q = rearrange(q, "b c t h w -> (b t) (h w) c")  # blc
        k = rearrange(k, "b c t h w -> (b t) c (h w)")  # bcl

        w_ = torch.bmm(q, k)  # b,l,l
        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # v = v.reshape(b,c,h*w)
        v = rearrange(v, "b c t h w -> (b t) c (h w)")  # bcl

        # attend to values
        w_ = w_.permute(0, 2, 1)  # bll
        h_ = torch.bmm(v, w_)  # bcl

        # h_ = h_.reshape(b,c,h,w)
        h_ = rearrange(h_, "(b t) c (h w) -> b c t h w", b=b, h=h)

        h_ = self.proj_out(h_)

        return x + h_
    
class MultiHeadAttention3D(nn.Module):
    def __init__(self, in_channels, num_heads=8):
        super().__init__()
        self.in_channels = in_channels
        self.num_heads = num_heads
        self.head_dim = in_channels // num_heads

        assert self.head_dim * num_heads == in_channels, "in_channels must be divisible by num_heads"

        self.norm = nn.LayerNorm(in_channels)
        self.q_linear = nn.Linear(in_channels, in_channels)
        self.k_linear = nn.Linear(in_channels, in_channels)
        self.v_linear = nn.Linear(in_channels, in_channels)
        self.proj_out = nn.Linear(in_channels, in_channels)

    def forward(self, x):
        b, c, t, h, w = x.shape
        #print(x.shape)
        # Normalize and reshape input
        h_ = rearrange(x, "b c t h w -> (b t) (h w) c")
        h_ = self.norm(h_)

        # Linear projections
        q = self.q_linear(h_)
        k = self.k_linear(h_)
        v = self.v_linear(h_)

        # Reshape to multi-head
        q = rearrange(q, "b l (h d) -> b h l d", h=self.num_heads)
        k = rearrange(k, "b l (h d) -> b h l d", h=self.num_heads)
        v = rearrange(v, "b l (h d) -> b h l d", h=self.num_heads)

        # Scaled Dot-Product Attention
        scores = torch.matmul(q, k.transpose(-2, -1)) / (self.head_dim ** 0.5)
        attn = F.softmax(scores, dim=-1)

        # Apply attention to values
        out = torch.matmul(attn, v)
        out = rearrange(out, "b h l d -> b l (h d)")

        # Project back to original dimension
        out = self.proj_out(out)

        # Reshape back to original shape
        out = rearrange(out, "(b t) (h w) c -> b c t h w", b=b, h=h, t=t)
        #print(out.shape)
        return x + out


class SiglipAE(nn.Module):
    def __init__(self):
        super().__init__()
        temporal_stride=2
        norm_type = "group"
                   
        self.temporal_encoding = nn.Parameter(torch.randn((4,1152)))
        #self.vision_tower=SigLipVisionTower('google/siglip-so400m-patch14-384')
        self.encoder=nn.Sequential(
            AttnBlock3D(1152),
            TemporalAttention(1152),
            
            SamePadConv3d(1152,1152,kernel_size=3,stride=(temporal_stride, 1, 1),padding_type="replicate"),
            
            AttnBlock3D(1152),
            TemporalAttention(1152),
            
            SamePadConv3d(1152,1152,kernel_size=3,stride=(temporal_stride, 1, 1),padding_type="replicate"),
            
        )
    def forward(self, x):
        b_,c_,t_,h_,w_=x.shape

        temporal_encoding = self.temporal_encoding.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
        temporal_encoding = temporal_encoding.expand(b_, -1, -1, h_, w_)  # (B, T, C, H, W)
        temporal_encoding = temporal_encoding.permute(0, 2, 1, 3, 4)  # (B, C, T, H, W)  
        x = x + temporal_encoding
        
        x=self.encoder(x)
        return x