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import typing as tp

import torch

from einops import rearrange
from torch import nn
from torch.nn import functional as F
from x_transformers import ContinuousTransformerWrapper, Encoder

from functools import reduce, partial
from packaging import version

from einops import rearrange, repeat
from einops.layers.torch import Rearrange
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.cuda.amp import autocast
from typing import Callable, Literal

try:
    from flash_attn import flash_attn_func, flash_attn_kvpacked_func
except ImportError as e:
    print(e)
    print('flash_attn not installed, disabling Flash Attention')
    flash_attn_kvpacked_func = None
    flash_attn_func = None

try:
    import natten
except ImportError:
    natten = None
from functools import reduce
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F

from torch.backends.cuda import sdp_kernel
from packaging import version

from dac.nn.layers import Snake1d


class ResidualBlock(nn.Module):
    def __init__(self, main, skip=None):
        super().__init__()
        self.main = nn.Sequential(*main)
        self.skip = skip if skip else nn.Identity()

    def forward(self, input):
        return self.main(input) + self.skip(input)


class ResConvBlock(ResidualBlock):
    def __init__(self, c_in, c_mid, c_out, is_last=False, kernel_size=5, conv_bias=True, use_snake=False):
        skip = None if c_in == c_out else nn.Conv1d(c_in, c_out, 1, bias=False)
        super().__init__([
            nn.Conv1d(c_in, c_mid, kernel_size, padding=kernel_size // 2, bias=conv_bias),
            nn.GroupNorm(1, c_mid),
            Snake1d(c_mid) if use_snake else nn.GELU(),
            nn.Conv1d(c_mid, c_out, kernel_size, padding=kernel_size // 2, bias=conv_bias),
            nn.GroupNorm(1, c_out) if not is_last else nn.Identity(),
            (Snake1d(c_out) if use_snake else nn.GELU()) if not is_last else nn.Identity(),
        ], skip)


class SelfAttention1d(nn.Module):
    def __init__(self, c_in, n_head=1, dropout_rate=0.):
        super().__init__()
        assert c_in % n_head == 0
        self.norm = nn.GroupNorm(1, c_in)
        self.n_head = n_head
        self.qkv_proj = nn.Conv1d(c_in, c_in * 3, 1)
        self.out_proj = nn.Conv1d(c_in, c_in, 1)
        self.dropout = nn.Dropout(dropout_rate, inplace=True)

        self.use_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')

        if not self.use_flash:
            return

        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))

        if device_properties.major == 8 and device_properties.minor == 0:
            # Use flash attention for A100 GPUs
            self.sdp_kernel_config = (True, False, False)
        else:
            # Don't use flash attention for other GPUs
            self.sdp_kernel_config = (False, True, True)

    def forward(self, input):
        n, c, s = input.shape
        qkv = self.qkv_proj(self.norm(input))
        qkv = qkv.view(
            [n, self.n_head * 3, c // self.n_head, s]).transpose(2, 3)
        q, k, v = qkv.chunk(3, dim=1)
        scale = k.shape[3] ** -0.25

        if self.use_flash:
            with sdp_kernel(*self.sdp_kernel_config):
                y = F.scaled_dot_product_attention(q, k, v, is_causal=False).contiguous().view([n, c, s])
        else:
            att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3)
            y = (att @ v).transpose(2, 3).contiguous().view([n, c, s])

        return input + self.dropout(self.out_proj(y))


class SkipBlock(nn.Module):
    def __init__(self, *main):
        super().__init__()
        self.main = nn.Sequential(*main)

    def forward(self, input):
        return torch.cat([self.main(input), input], dim=1)


class FourierFeatures(nn.Module):
    def __init__(self, in_features, out_features, std=1.):
        super().__init__()
        assert out_features % 2 == 0
        self.weight = nn.Parameter(torch.randn(
            [out_features // 2, in_features]) * std)

    def forward(self, input):
        f = 2 * math.pi * input @ self.weight.T
        return torch.cat([f.cos(), f.sin()], dim=-1)


def expand_to_planes(input, shape):
    return input[..., None].repeat([1, 1, shape[2]])


_kernels = {
    'linear':
        [1 / 8, 3 / 8, 3 / 8, 1 / 8],
    'cubic':
        [-0.01171875, -0.03515625, 0.11328125, 0.43359375,
         0.43359375, 0.11328125, -0.03515625, -0.01171875],
    'lanczos3':
        [0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
         -0.066637322306633, 0.13550527393817902, 0.44638532400131226,
         0.44638532400131226, 0.13550527393817902, -0.066637322306633,
         -0.03399861603975296, 0.015056144446134567, 0.003689131001010537]
}


class Downsample1d(nn.Module):
    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor(_kernels[kernel])
        self.pad = kernel_1d.shape[0] // 2 - 1
        self.register_buffer('kernel', kernel_1d)
        self.channels_last = channels_last

    def forward(self, x):
        if self.channels_last:
            x = x.permute(0, 2, 1)
        x = F.pad(x, (self.pad,) * 2, self.pad_mode)
        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
        indices = torch.arange(x.shape[1], device=x.device)
        weight[indices, indices] = self.kernel.to(weight)
        x = F.conv1d(x, weight, stride=2)
        if self.channels_last:
            x = x.permute(0, 2, 1)
        return x


class Upsample1d(nn.Module):
    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor(_kernels[kernel]) * 2
        self.pad = kernel_1d.shape[0] // 2 - 1
        self.register_buffer('kernel', kernel_1d)
        self.channels_last = channels_last

    def forward(self, x):
        if self.channels_last:
            x = x.permute(0, 2, 1)
        x = F.pad(x, ((self.pad + 1) // 2,) * 2, self.pad_mode)
        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
        indices = torch.arange(x.shape[1], device=x.device)
        weight[indices, indices] = self.kernel.to(weight)
        x = F.conv_transpose1d(x, weight, stride=2, padding=self.pad * 2 + 1)
        if self.channels_last:
            x = x.permute(0, 2, 1)
        return x


def Downsample1d_2(
        in_channels: int, out_channels: int, factor: int, kernel_multiplier: int = 2
) -> nn.Module:
    assert kernel_multiplier % 2 == 0, "Kernel multiplier must be even"

    return nn.Conv1d(
        in_channels=in_channels,
        out_channels=out_channels,
        kernel_size=factor * kernel_multiplier + 1,
        stride=factor,
        padding=factor * (kernel_multiplier // 2),
    )


def Upsample1d_2(
        in_channels: int, out_channels: int, factor: int, use_nearest: bool = False
) -> nn.Module:
    if factor == 1:
        return nn.Conv1d(
            in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1
        )

    if use_nearest:
        return nn.Sequential(
            nn.Upsample(scale_factor=factor, mode="nearest"),
            nn.Conv1d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=3,
                padding=1,
            ),
        )
    else:
        return nn.ConvTranspose1d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=factor * 2,
            stride=factor,
            padding=factor // 2 + factor % 2,
            output_padding=factor % 2,
        )


def zero_init(layer):
    nn.init.zeros_(layer.weight)
    if layer.bias is not None:
        nn.init.zeros_(layer.bias)
    return layer


def rms_norm(x, scale, eps):
    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
    mean_sq = torch.mean(x.to(dtype) ** 2, dim=-1, keepdim=True)
    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
    return x * scale.to(x.dtype)


# rms_norm = torch.compile(rms_norm)

class AdaRMSNorm(nn.Module):
    def __init__(self, features, cond_features, eps=1e-6):
        super().__init__()
        self.eps = eps
        self.linear = zero_init(nn.Linear(cond_features, features, bias=False))

    def extra_repr(self):
        return f"eps={self.eps},"

    def forward(self, x, cond):
        return rms_norm(x, self.linear(cond)[:, None, :] + 1, self.eps)


def normalize(x, eps=1e-4):
    dim = list(range(1, x.ndim))
    n = torch.linalg.vector_norm(x, dim=dim, keepdim=True)
    alpha = np.sqrt(n.numel() / x.numel())
    return x / torch.add(eps, n, alpha=alpha)


class ForcedWNConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1):
        super().__init__()
        self.weight = nn.Parameter(torch.randn([out_channels, in_channels, kernel_size]))

    def forward(self, x):
        if self.training:
            with torch.no_grad():
                self.weight.copy_(normalize(self.weight))

        fan_in = self.weight[0].numel()

        w = normalize(self.weight) / math.sqrt(fan_in)

        return F.conv1d(x, w, padding='same')


# Kernels

use_compile = True


def compile(function, *args, **kwargs):
    if not use_compile:
        return function
    try:
        return torch.compile(function, *args, **kwargs)
    except RuntimeError:
        return function


@compile
def linear_geglu(x, weight, bias=None):
    x = x @ weight.mT
    if bias is not None:
        x = x + bias
    x, gate = x.chunk(2, dim=-1)
    return x * F.gelu(gate)


@compile
def rms_norm(x, scale, eps):
    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
    mean_sq = torch.mean(x.to(dtype) ** 2, dim=-1, keepdim=True)
    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
    return x * scale.to(x.dtype)


# Layers

class LinearGEGLU(nn.Linear):
    def __init__(self, in_features, out_features, bias=True):
        super().__init__(in_features, out_features * 2, bias=bias)
        self.out_features = out_features

    def forward(self, x):
        return linear_geglu(x, self.weight, self.bias)


class RMSNorm(nn.Module):
    def __init__(self, shape, fix_scale=False, eps=1e-6):
        super().__init__()
        self.eps = eps

        if fix_scale:
            self.register_buffer("scale", torch.ones(shape))
        else:
            self.scale = nn.Parameter(torch.ones(shape))

    def extra_repr(self):
        return f"shape={tuple(self.scale.shape)}, eps={self.eps}"

    def forward(self, x):
        return rms_norm(x, self.scale, self.eps)


def snake_beta(x, alpha, beta):
    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)


# try:
#     snake_beta = torch.compile(snake_beta)
# except RuntimeError:
#     pass

# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
# License available in LICENSES/LICENSE_NVIDIA.txt
class SnakeBeta(nn.Module):

    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
        super(SnakeBeta, self).__init__()
        self.in_features = in_features

        # initialize alpha
        self.alpha_logscale = alpha_logscale
        if self.alpha_logscale:  # log scale alphas initialized to zeros
            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
        else:  # linear scale alphas initialized to ones
            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
            self.beta = nn.Parameter(torch.ones(in_features) * alpha)

        self.alpha.requires_grad = alpha_trainable
        self.beta.requires_grad = alpha_trainable

        self.no_div_by_zero = 0.000000001

    def forward(self, x):
        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
        beta = self.beta.unsqueeze(0).unsqueeze(-1)
        if self.alpha_logscale:
            alpha = torch.exp(alpha)
            beta = torch.exp(beta)
        x = snake_beta(x, alpha, beta)

        return x

def checkpoint(function, *args, **kwargs):
    kwargs.setdefault("use_reentrant", False)
    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)


# Copied and modified from https://github.com/lucidrains/x-transformers/blob/main/x_transformers/attend.py under MIT License
# License can be found in LICENSES/LICENSE_XTRANSFORMERS.txt

def create_causal_mask(i, j, device):
    return torch.ones((i, j), device=device, dtype=torch.bool).triu(j - i + 1)


def or_reduce(masks):
    head, *body = masks
    for rest in body:
        head = head | rest
    return head


# positional embeddings

class AbsolutePositionalEmbedding(nn.Module):
    def __init__(self, dim, max_seq_len):
        super().__init__()
        self.scale = dim ** -0.5
        self.max_seq_len = max_seq_len
        self.emb = nn.Embedding(max_seq_len, dim)

    def forward(self, x, pos=None, seq_start_pos=None):
        seq_len, device = x.shape[1], x.device
        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'

        if pos is None:
            pos = torch.arange(seq_len, device=device)

        if seq_start_pos is not None:
            pos = (pos - seq_start_pos[..., None]).clamp(min=0)

        pos_emb = self.emb(pos)
        pos_emb = pos_emb * self.scale
        return pos_emb


class ScaledSinusoidalEmbedding(nn.Module):
    def __init__(self, dim, theta=10000):
        super().__init__()
        assert (dim % 2) == 0, 'dimension must be divisible by 2'
        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)

        half_dim = dim // 2
        freq_seq = torch.arange(half_dim).float() / half_dim
        inv_freq = theta ** -freq_seq
        self.register_buffer('inv_freq', inv_freq, persistent=False)

    def forward(self, x, pos=None, seq_start_pos=None):
        seq_len, device = x.shape[1], x.device

        if pos is None:
            pos = torch.arange(seq_len, device=device)

        if seq_start_pos is not None:
            pos = pos - seq_start_pos[..., None]

        emb = einsum('i, j -> i j', pos, self.inv_freq)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb * self.scale


class RotaryEmbedding(nn.Module):
    def __init__(
            self,
            dim,
            use_xpos=False,
            scale_base=512,
            interpolation_factor=1.,
            base=10000,
            base_rescale_factor=1.
    ):
        super().__init__()
        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
        # has some connection to NTK literature
        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
        base *= base_rescale_factor ** (dim / (dim - 2))

        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)

        assert interpolation_factor >= 1.
        self.interpolation_factor = interpolation_factor

        if not use_xpos:
            self.register_buffer('scale', None)
            return

        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)

        self.scale_base = scale_base
        self.register_buffer('scale', scale)

    def forward_from_seq_len(self, seq_len):
        device = self.inv_freq.device

        t = torch.arange(seq_len, device=device)
        return self.forward(t)

    @autocast(enabled=False)
    def forward(self, t):
        device = self.inv_freq.device

        t = t.to(torch.float32)

        t = t / self.interpolation_factor

        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
        freqs = torch.cat((freqs, freqs), dim=-1)

        if self.scale is None:
            return freqs, 1.

        power = (torch.arange(seq_len, device=device) - (seq_len // 2)) / self.scale_base
        scale = self.scale ** rearrange(power, 'n -> n 1')
        scale = torch.cat((scale, scale), dim=-1)

        return freqs, scale


def rotate_half(x):
    x = rearrange(x, '... (j d) -> ... j d', j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


@autocast(enabled=False)
def apply_rotary_pos_emb(t, freqs, scale=1):
    out_dtype = t.dtype

    # cast to float32 if necessary for numerical stability
    dtype = reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
    freqs, t = freqs.to(dtype), t.to(dtype)
    freqs = freqs[-seq_len:, :]

    if t.ndim == 4 and freqs.ndim == 3:
        freqs = rearrange(freqs, 'b n d -> b 1 n d')

    # partial rotary embeddings, Wang et al. GPT-J
    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)

    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)

    return torch.cat((t, t_unrotated), dim=-1)


# norms
class LayerNorm(nn.Module):
    def __init__(self, dim, bias=False, fix_scale=False):
        """
        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
        """
        super().__init__()

        if fix_scale:
            self.register_buffer("gamma", torch.ones(dim))
        else:
            self.gamma = nn.Parameter(torch.ones(dim))

        if bias:
            self.beta = nn.Parameter(torch.zeros(dim))
        else:
            self.register_buffer("beta", torch.zeros(dim))

    def forward(self, x):
        return F.layer_norm(x, x.shape[-1:], weight=self.gamma, bias=self.beta)


# feedforward

class GLU(nn.Module):
    def __init__(
            self,
            dim_in,
            dim_out,
            activation: Callable,
            use_conv=False,
            conv_kernel_size=3,
    ):
        super().__init__()
        self.act = activation
        self.proj = nn.Linear(dim_in, dim_out * 2) if not use_conv else nn.Conv1d(dim_in, dim_out * 2, conv_kernel_size,
                                                                                  padding=(conv_kernel_size // 2))
        self.use_conv = use_conv

    def forward(self, x):
        if self.use_conv:
            x = rearrange(x, 'b n d -> b d n')
            x = self.proj(x)
            x = rearrange(x, 'b d n -> b n d')
        else:
            x = self.proj(x)

        x, gate = x.chunk(2, dim=-1)
        return x * self.act(gate)


class FeedForward(nn.Module):
    def __init__(
            self,
            dim,
            dim_out=None,
            mult=4,
            no_bias=False,
            glu=True,
            use_conv=False,
            conv_kernel_size=3,
            zero_init_output=True,
    ):
        super().__init__()
        inner_dim = int(dim * mult)

        # Default to SwiGLU

        activation = nn.SiLU()

        dim_out = dim if dim_out is None else dim_out

        if glu:
            linear_in = GLU(dim, inner_dim, activation)
        else:
            linear_in = nn.Sequential(
                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
                nn.Linear(dim, inner_dim, bias=not no_bias) if not use_conv else nn.Conv1d(dim, inner_dim,
                                                                                           conv_kernel_size, padding=(
                                conv_kernel_size // 2), bias=not no_bias),
                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
                activation
            )

        linear_out = nn.Linear(inner_dim, dim_out, bias=not no_bias) if not use_conv else nn.Conv1d(inner_dim, dim_out,
                                                                                                    conv_kernel_size,
                                                                                                    padding=(
                                                                                                                conv_kernel_size // 2),
                                                                                                    bias=not no_bias)

        # init last linear layer to 0
        if zero_init_output:
            nn.init.zeros_(linear_out.weight)
            if not no_bias:
                nn.init.zeros_(linear_out.bias)

        self.ff = nn.Sequential(
            linear_in,
            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
            linear_out,
            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
        )

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


class Attention(nn.Module):
    def __init__(
            self,
            dim,
            dim_heads=64,
            dim_context=None,
            causal=False,
            zero_init_output=True,
            qk_norm: Literal['l2', 'ln', 'none'] = 'none',
            natten_kernel_size=None
    ):
        super().__init__()
        self.dim = dim
        self.dim_heads = dim_heads
        self.causal = causal

        dim_kv = dim_context if dim_context is not None else dim

        self.num_heads = dim // dim_heads
        self.kv_heads = dim_kv // dim_heads

        if dim_context is not None:
            self.to_q = nn.Linear(dim, dim, bias=False)
            self.to_kv = nn.Linear(dim_kv, dim_kv * 2, bias=False)
        else:
            self.to_qkv = nn.Linear(dim, dim * 3, bias=False)

        self.to_out = nn.Linear(dim, dim, bias=False)

        if zero_init_output:
            nn.init.zeros_(self.to_out.weight)

        self.qk_norm = qk_norm

        if self.qk_norm == "ln":
            self.q_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
            self.k_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)

        # Using 1d neighborhood attention
        self.natten_kernel_size = natten_kernel_size
        if natten_kernel_size is not None:
            return

        self.use_pt_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')

        self.use_fa_flash = torch.cuda.is_available() and flash_attn_func is not None

        self.sdp_kwargs = dict(
            enable_flash=True,
            enable_math=True,
            enable_mem_efficient=True
        )

    def flash_attn(
            self,
            q,
            k,
            v,
            mask=None,
            causal=None
    ):
        batch, heads, q_len, _, k_len, device = *q.shape, k.shape[-2], q.device
        kv_heads = k.shape[1]
        # Recommended for multi-query single-key-value attention by Tri Dao
        # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])

        if heads != kv_heads:
            # Repeat interleave kv_heads to match q_heads
            heads_per_kv_head = heads // kv_heads
            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim=1), (k, v))

        if k.ndim == 3:
            k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)

        if v.ndim == 3:
            v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)

        causal = self.causal if causal is None else causal

        if q_len == 1 and causal:
            causal = False

        if mask is not None:
            assert mask.ndim == 4
            mask = mask.expand(batch, heads, q_len, k_len)

        # handle kv cache - this should be bypassable in updated flash attention 2

        if k_len > q_len and causal:
            causal_mask = self.create_causal_mask(q_len, k_len, device=device)
            if mask is None:
                mask = ~causal_mask
            else:
                mask = mask & ~causal_mask
            causal = False

        # manually handle causal mask, if another mask was given

        row_is_entirely_masked = None

        if mask is not None and causal:
            causal_mask = self.create_causal_mask(q_len, k_len, device=device)
            mask = mask & ~causal_mask

            # protect against an entire row being masked out

            row_is_entirely_masked = ~mask.any(dim=-1)
            mask[..., 0] = mask[..., 0] | row_is_entirely_masked

            causal = False

        with torch.backends.cuda.sdp_kernel(**self.sdp_kwargs):
            out = F.scaled_dot_product_attention(
                q, k, v,
                attn_mask=mask,
                is_causal=causal
            )

        # for a row that is entirely masked out, should zero out the output of that row token

        if row_is_entirely_masked is not None:
            out = out.masked_fill(row_is_entirely_masked[..., None], 0.)

        return out

    def forward(
            self,
            x,
            context=None,
            mask=None,
            context_mask=None,
            rotary_pos_emb=None,
            causal=None
    ):
        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None

        kv_input = context if has_context else x

        if hasattr(self, 'to_q'):
            # Use separate linear projections for q and k/v
            q = self.to_q(x)
            q = rearrange(q, 'b n (h d) -> b h n d', h=h)

            k, v = self.to_kv(kv_input).chunk(2, dim=-1)

            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=kv_h), (k, v))
        else:
            # Use fused linear projection
            q, k, v = 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), (q, k, v))

        # Normalize q and k for cosine sim attention
        if self.qk_norm == "l2":
            q = F.normalize(q, dim=-1)
            k = F.normalize(k, dim=-1)
        elif self.qk_norm == "ln":
            q = self.q_norm(q)
            k = self.k_norm(k)

        if rotary_pos_emb is not None and not has_context:
            freqs, _ = rotary_pos_emb

            q_dtype = q.dtype
            k_dtype = k.dtype

            q = q.to(torch.float32)
            k = k.to(torch.float32)
            freqs = freqs.to(torch.float32)

            q = apply_rotary_pos_emb(q, freqs)
            k = apply_rotary_pos_emb(k, freqs)

            q = q.to(q_dtype)
            k = k.to(k_dtype)

        input_mask = context_mask

        if input_mask is None and not has_context:
            input_mask = mask

        # determine masking
        masks = []
        final_attn_mask = None  # The mask that will be applied to the attention matrix, taking all masks into account

        if input_mask is not None:
            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
            masks.append(~input_mask)

        # Other masks will be added here later

        if len(masks) > 0:
            final_attn_mask = ~or_reduce(masks)

        n, device = q.shape[-2], q.device

        causal = self.causal if causal is None else causal

        if n == 1 and causal:
            causal = False

        if self.natten_kernel_size is not None:
            if natten is None:
                raise ImportError('natten not installed, please install natten to use neighborhood attention')

            dtype_in = q.dtype
            q, k, v = map(lambda t: t.to(torch.float32), (q, k, v))

            attn = natten.functional.natten1dqk(q, k, kernel_size=self.natten_kernel_size, dilation=1)

            if final_attn_mask is not None:
                attn = attn.masked_fill(final_attn_mask, -torch.finfo(attn.dtype).max)

            attn = F.softmax(attn, dim=-1, dtype=torch.float32)

            out = natten.functional.natten1dav(attn, v, kernel_size=self.natten_kernel_size, dilation=1).to(dtype_in)

        # Prioritize Flash Attention 2
        elif self.use_fa_flash:
            assert final_attn_mask is None, 'masking not yet supported for Flash Attention 2'
            # Flash Attention 2 requires FP16 inputs
            fa_dtype_in = q.dtype
            q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d').to(torch.float16), (q, k, v))

            out = flash_attn_func(q, k, v, causal=causal)

            out = rearrange(out.to(fa_dtype_in), 'b n h d -> b h n d')

        # Fall back to PyTorch implementation
        elif self.use_pt_flash:
            out = self.flash_attn(q, k, v, causal=causal, mask=final_attn_mask)

        else:
            # Fall back to custom implementation

            if h != kv_h:
                # Repeat interleave kv_heads to match q_heads
                heads_per_kv_head = h // kv_h
                k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim=1), (k, v))

            scale = 1. / (q.shape[-1] ** 0.5)

            kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'

            dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale

            i, j, dtype = *dots.shape[-2:], dots.dtype

            mask_value = -torch.finfo(dots.dtype).max

            if final_attn_mask is not None:
                dots = dots.masked_fill(~final_attn_mask, mask_value)

            if causal:
                causal_mask = self.create_causal_mask(i, j, device=device)
                dots = dots.masked_fill(causal_mask, mask_value)

            attn = F.softmax(dots, dim=-1, dtype=torch.float32)
            attn = attn.type(dtype)

            out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)

        # merge heads
        out = rearrange(out, ' b h n d -> b n (h d)')

        # Communicate between heads

        # with autocast(enabled = False):
        #     out_dtype = out.dtype
        #     out = out.to(torch.float32)
        #     out = self.to_out(out).to(out_dtype)
        out = self.to_out(out)

        if mask is not None:
            mask = rearrange(mask, 'b n -> b n 1')
            out = out.masked_fill(~mask, 0.)

        return out


class ConformerModule(nn.Module):
    def __init__(
            self,
            dim,
            norm_kwargs={},
    ):
        super().__init__()

        self.dim = dim

        self.in_norm = LayerNorm(dim, **norm_kwargs)
        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
        self.glu = GLU(dim, dim, nn.SiLU())
        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
        self.mid_norm = LayerNorm(dim,
                                  **norm_kwargs)  # This is a batch norm in the original but I don't like batch norm
        self.swish = nn.SiLU()
        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)

    def forward(self, x):
        x = self.in_norm(x)
        x = rearrange(x, 'b n d -> b d n')
        x = self.pointwise_conv(x)
        x = rearrange(x, 'b d n -> b n d')
        x = self.glu(x)
        x = rearrange(x, 'b n d -> b d n')
        x = self.depthwise_conv(x)
        x = rearrange(x, 'b d n -> b n d')
        x = self.mid_norm(x)
        x = self.swish(x)
        x = rearrange(x, 'b n d -> b d n')
        x = self.pointwise_conv_2(x)
        x = rearrange(x, 'b d n -> b n d')

        return x


class TransformerBlock(nn.Module):
    def __init__(
            self,
            dim,
            dim_heads=64,
            cross_attend=False,
            dim_context=None,
            global_cond_dim=None,
            causal=False,
            zero_init_branch_outputs=True,
            conformer=False,
            layer_ix=-1,
            remove_norms=False,
            attn_kwargs={},
            ff_kwargs={},
            norm_kwargs={}
    ):

        super().__init__()
        self.dim = dim
        self.dim_heads = dim_heads
        self.cross_attend = cross_attend
        self.dim_context = dim_context
        self.causal = causal

        self.pre_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()

        self.self_attn = Attention(
            dim,
            dim_heads=dim_heads,
            causal=causal,
            zero_init_output=zero_init_branch_outputs,
            **attn_kwargs
        )

        if cross_attend:
            self.cross_attend_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
            self.cross_attn = Attention(
                dim,
                dim_heads=dim_heads,
                dim_context=dim_context,
                causal=causal,
                zero_init_output=zero_init_branch_outputs,
                **attn_kwargs
            )

        self.ff_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, **ff_kwargs)

        self.layer_ix = layer_ix

        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None

        self.global_cond_dim = global_cond_dim

        if global_cond_dim is not None:
            self.to_scale_shift_gate = nn.Sequential(
                nn.SiLU(),
                nn.Linear(global_cond_dim, dim * 6, bias=False)
            )

            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
            # nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)

    def forward(
            self,
            x,
            context=None,
            global_cond=None,
            mask=None,
            context_mask=None,
            rotary_pos_emb=None
    ):
        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:

            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(
                global_cond).unsqueeze(1).chunk(6, dim=-1)

            # self-attention with adaLN
            residual = x
            x = self.pre_norm(x)
            x = x * (1 + scale_self) + shift_self
            x = self.self_attn(x, mask=mask, rotary_pos_emb=rotary_pos_emb)
            x = x * torch.sigmoid(1 - gate_self)
            x = x + residual

            if context is not None:
                x = x + self.cross_attn(self.cross_attend_norm(x), context=context, context_mask=context_mask)

            if self.conformer is not None:
                x = x + self.conformer(x)

            # feedforward with adaLN
            residual = x
            x = self.ff_norm(x)
            x = x * (1 + scale_ff) + shift_ff
            x = self.ff(x)
            x = x * torch.sigmoid(1 - gate_ff)
            x = x + residual

        else:
            x = x + self.self_attn(self.pre_norm(x), mask=mask, rotary_pos_emb=rotary_pos_emb)

            if context is not None:
                x = x + self.cross_attn(self.cross_attend_norm(x), context=context, context_mask=context_mask)

            if self.conformer is not None:
                x = x + self.conformer(x)

            x = x + self.ff(self.ff_norm(x))

        return x


class ContinuousTransformer(nn.Module):
    def __init__(
            self,
            dim,
            depth,
            *,
            dim_in=None,
            dim_out=None,
            dim_heads=64,
            cross_attend=False,
            cond_token_dim=None,
            global_cond_dim=None,
            causal=False,
            rotary_pos_emb=True,
            zero_init_branch_outputs=True,
            conformer=False,
            use_sinusoidal_emb=False,
            use_abs_pos_emb=False,
            abs_pos_emb_max_length=10000,
            **kwargs
    ):

        super().__init__()

        self.dim = dim
        self.depth = depth
        self.causal = causal
        self.layers = nn.ModuleList([])

        self.project_in = nn.Linear(dim_in, dim, bias=False) if dim_in is not None else nn.Identity()
        self.project_out = nn.Linear(dim, dim_out, bias=False) if dim_out is not None else nn.Identity()

        if rotary_pos_emb:
            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
        else:
            self.rotary_pos_emb = None

        self.use_sinusoidal_emb = use_sinusoidal_emb
        if use_sinusoidal_emb:
            self.pos_emb = ScaledSinusoidalEmbedding(dim)

        self.use_abs_pos_emb = use_abs_pos_emb
        if use_abs_pos_emb:
            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)

        for i in range(depth):
            self.layers.append(
                TransformerBlock(
                    dim,
                    dim_heads=dim_heads,
                    cross_attend=cross_attend,
                    dim_context=cond_token_dim,
                    global_cond_dim=global_cond_dim,
                    causal=causal,
                    zero_init_branch_outputs=zero_init_branch_outputs,
                    conformer=conformer,
                    layer_ix=i,
                    **kwargs
                )
            )

    def forward(
            self,
            x,
            mask=None,
            prepend_embeds=None,
            prepend_mask=None,
            global_cond=None,
            return_info=False,
            **kwargs
    ):
        batch, seq, device = *x.shape[:2], x.device

        info = {
            "hidden_states": [],
        }

        x = self.project_in(x)

        if prepend_embeds is not None:
            prepend_length, prepend_dim = prepend_embeds.shape[1:]

            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'

            x = torch.cat((prepend_embeds, x), dim=-2)

            if prepend_mask is not None or mask is not None:
                mask = mask if mask is not None else torch.ones((batch, seq), device=device, dtype=torch.bool)
                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length),
                                                                                        device=device, dtype=torch.bool)

                mask = torch.cat((prepend_mask, mask), dim=-1)

        # Attention layers

        if self.rotary_pos_emb is not None:
            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
        else:
            rotary_pos_emb = None

        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
            x = x + self.pos_emb(x)

        # Iterate over the transformer layers
        for layer in self.layers:
            # x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
            x = checkpoint(layer, x, rotary_pos_emb=rotary_pos_emb, global_cond=global_cond, **kwargs)

            if return_info:
                info["hidden_states"].append(x)

        x = self.project_out(x)

        if return_info:
            return x, info

        return x

class DiffusionTransformer(nn.Module):
    def __init__(self,
                 io_channels=32,
                 patch_size=1,
                 embed_dim=768,
                 cond_token_dim=0,
                 project_cond_tokens=True,
                 global_cond_dim=0,
                 project_global_cond=True,
                 input_concat_dim=0,
                 prepend_cond_dim=0,
                 depth=12,
                 num_heads=8,
                 transformer_type: tp.Literal["x-transformers", "continuous_transformer"] = "x-transformers",
                 global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
                 **kwargs):

        super().__init__()

        self.cond_token_dim = cond_token_dim

        # Timestep embeddings
        timestep_features_dim = 256

        self.timestep_features = FourierFeatures(1, timestep_features_dim)

        self.to_timestep_embed = nn.Sequential(
            nn.Linear(timestep_features_dim, embed_dim, bias=True),
            nn.SiLU(),
            nn.Linear(embed_dim, embed_dim, bias=True),
        )

        if cond_token_dim > 0:
            # Conditioning tokens

            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
            self.to_cond_embed = nn.Sequential(
                nn.Linear(cond_token_dim, cond_embed_dim, bias=False),
                nn.SiLU(),
                nn.Linear(cond_embed_dim, cond_embed_dim, bias=False)
            )
        else:
            cond_embed_dim = 0

        if global_cond_dim > 0:
            # Global conditioning
            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
            self.to_global_embed = nn.Sequential(
                nn.Linear(global_cond_dim, global_embed_dim, bias=False),
                nn.SiLU(),
                nn.Linear(global_embed_dim, global_embed_dim, bias=False)
            )

        if prepend_cond_dim > 0:
            # Prepend conditioning
            self.to_prepend_embed = nn.Sequential(
                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
                nn.SiLU(),
                nn.Linear(embed_dim, embed_dim, bias=False)
            )

        self.input_concat_dim = input_concat_dim

        dim_in = io_channels + self.input_concat_dim

        self.patch_size = patch_size

        # Transformer

        self.transformer_type = transformer_type

        self.global_cond_type = global_cond_type

        if self.transformer_type == "x-transformers":
            self.transformer = ContinuousTransformerWrapper(
                dim_in=dim_in * patch_size,
                dim_out=io_channels * patch_size,
                max_seq_len=0,  # Not relevant without absolute positional embeds
                attn_layers=Encoder(
                    dim=embed_dim,
                    depth=depth,
                    heads=num_heads,
                    attn_flash=True,
                    cross_attend=cond_token_dim > 0,
                    dim_context=None if cond_embed_dim == 0 else cond_embed_dim,
                    zero_init_branch_output=True,
                    use_abs_pos_emb=False,
                    rotary_pos_emb=True,
                    ff_swish=True,
                    ff_glu=True,
                    **kwargs
                )
            )

        elif self.transformer_type == "continuous_transformer":

            global_dim = None

            if self.global_cond_type == "adaLN":
                # The global conditioning is projected to the embed_dim already at this point
                global_dim = embed_dim

            self.transformer = ContinuousTransformer(
                dim=embed_dim,
                depth=depth,
                dim_heads=embed_dim // num_heads,
                dim_in=dim_in * patch_size,
                dim_out=io_channels * patch_size,
                cross_attend=cond_token_dim > 0,
                cond_token_dim=cond_embed_dim,
                global_cond_dim=global_dim,
                **kwargs
            )

        else:
            raise ValueError(f"Unknown transformer type: {self.transformer_type}")

        self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False)
        nn.init.zeros_(self.preprocess_conv.weight)
        self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False)
        nn.init.zeros_(self.postprocess_conv.weight)

    def _forward(
            self,
            x,
            t,
            mask=None,
            cross_attn_cond=None,
            cross_attn_cond_mask=None,
            input_concat_cond=None,
            global_embed=None,
            prepend_cond=None,
            prepend_cond_mask=None,
            return_info=False,
            **kwargs):

        if cross_attn_cond is not None:
            cross_attn_cond = self.to_cond_embed(cross_attn_cond)

        if global_embed is not None:
            # Project the global conditioning to the embedding dimension
            global_embed = self.to_global_embed(global_embed)

        prepend_inputs = None
        prepend_mask = None
        prepend_length = 0
        if prepend_cond is not None:
            # Project the prepend conditioning to the embedding dimension
            prepend_cond = self.to_prepend_embed(prepend_cond)

            prepend_inputs = prepend_cond
            if prepend_cond_mask is not None:
                prepend_mask = prepend_cond_mask

        if input_concat_cond is not None:

            # Interpolate input_concat_cond to the same length as x
            if input_concat_cond.shape[2] != x.shape[2]:
                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2],), mode='nearest')

            x = torch.cat([x, input_concat_cond], dim=1)

        # Get the batch of timestep embeddings
        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]))  # (b, embed_dim)

        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
        if global_embed is not None:
            global_embed = global_embed + timestep_embed
        else:
            global_embed = timestep_embed

        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
        if self.global_cond_type == "prepend":
            if prepend_inputs is None:
                # Prepend inputs are just the global embed, and the mask is all ones
                prepend_inputs = global_embed.unsqueeze(1)
                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
            else:
                # Prepend inputs are the prepend conditioning + the global embed
                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)],
                                         dim=1)

            prepend_length = prepend_inputs.shape[1]

        x = self.preprocess_conv(x) + x

        x = rearrange(x, "b c t -> b t c")

        extra_args = {}

        if self.global_cond_type == "adaLN":
            extra_args["global_cond"] = global_embed

        if self.patch_size > 1:
            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)

        if self.transformer_type == "x-transformers":
            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond,
                                      context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask,
                                      **extra_args, **kwargs)
        elif self.transformer_type == "continuous_transformer":
            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond,
                                      context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask,
                                      return_info=return_info, **extra_args, **kwargs)

            if return_info:
                output, info = output
        elif self.transformer_type == "mm_transformer":
            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask,
                                      **extra_args, **kwargs)

        output = rearrange(output, "b t c -> b c t")[:, :, prepend_length:]

        if self.patch_size > 1:
            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)

        output = self.postprocess_conv(output) + output

        if return_info:
            return output, info

        return output

    def forward(
            self,
            x,
            t,
            cross_attn_cond=None,
            cross_attn_cond_mask=None,
            negative_cross_attn_cond=None,
            negative_cross_attn_mask=None,
            input_concat_cond=None,
            global_embed=None,
            negative_global_embed=None,
            prepend_cond=None,
            prepend_cond_mask=None,
            cfg_scale=1.0,
            cfg_dropout_prob=0.0,
            causal=False,
            scale_phi=0.0,
            mask=None,
            return_info=False,
            **kwargs):

        assert causal == False, "Causal mode is not supported for DiffusionTransformer"

        if cross_attn_cond_mask is not None:
            cross_attn_cond_mask = cross_attn_cond_mask.bool()

            cross_attn_cond_mask = None  # Temporarily disabling conditioning masks due to kernel issue for flash attention

        if prepend_cond_mask is not None:
            prepend_cond_mask = prepend_cond_mask.bool()

        # CFG dropout
        if cfg_dropout_prob > 0.0:
            if cross_attn_cond is not None:
                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
                dropout_mask = torch.bernoulli(
                    torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(
                    torch.bool)
                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)

            if prepend_cond is not None:
                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
                dropout_mask = torch.bernoulli(
                    torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(
                    torch.bool)
                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)

        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None):
            # Classifier-free guidance
            # Concatenate conditioned and unconditioned inputs on the batch dimension
            batch_inputs = torch.cat([x, x], dim=0)
            batch_timestep = torch.cat([t, t], dim=0)

            if global_embed is not None:
                batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
            else:
                batch_global_cond = None

            if input_concat_cond is not None:
                batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
            else:
                batch_input_concat_cond = None

            batch_cond = None
            batch_cond_masks = None

            # Handle CFG for cross-attention conditioning
            if cross_attn_cond is not None:

                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)

                # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
                if negative_cross_attn_cond is not None:

                    # If there's a negative cross-attention mask, set the masked tokens to the null embed
                    if negative_cross_attn_mask is not None:
                        negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)

                        negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond,
                                                               null_embed)

                    batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)

                else:
                    batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)

                if cross_attn_cond_mask is not None:
                    batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)

            batch_prepend_cond = None
            batch_prepend_cond_mask = None

            if prepend_cond is not None:

                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)

                batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)

                if prepend_cond_mask is not None:
                    batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)

            if mask is not None:
                batch_masks = torch.cat([mask, mask], dim=0)
            else:
                batch_masks = None

            batch_output = self._forward(
                batch_inputs,
                batch_timestep,
                cross_attn_cond=batch_cond,
                cross_attn_cond_mask=batch_cond_masks,
                mask=batch_masks,
                input_concat_cond=batch_input_concat_cond,
                global_embed=batch_global_cond,
                prepend_cond=batch_prepend_cond,
                prepend_cond_mask=batch_prepend_cond_mask,
                return_info=return_info,
                **kwargs)

            if return_info:
                batch_output, info = batch_output

            cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0)
            cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale

            # CFG Rescale
            if scale_phi != 0.0:
                cond_out_std = cond_output.std(dim=1, keepdim=True)
                out_cfg_std = cfg_output.std(dim=1, keepdim=True)
                output = scale_phi * (cfg_output * (cond_out_std / out_cfg_std)) + (1 - scale_phi) * cfg_output
            else:
                output = cfg_output

            if return_info:
                return output, info

            return output

        else:
            return self._forward(
                x,
                t,
                cross_attn_cond=cross_attn_cond,
                cross_attn_cond_mask=cross_attn_cond_mask,
                input_concat_cond=input_concat_cond,
                global_embed=global_embed,
                prepend_cond=prepend_cond,
                prepend_cond_mask=prepend_cond_mask,
                mask=mask,
                return_info=return_info,
                **kwargs
            )



import math

class SinePositionalEmbedding(nn.Module):
    def __init__(
            self,
            embedding_dim: int,
            dropout: float=0.0,
            scale: bool=False,
            alpha: bool=False, ):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
        self.dropout = torch.nn.Dropout(p=dropout)

        self.reverse = False
        self.pe = None
        self.extend_pe(torch.tensor(0.0).expand(1, 4000))

    def extend_pe(self, x):
        """Reset the positional encodings."""
        if self.pe is not None:
            if self.pe.size(1) >= x.size(1):
                if self.pe.dtype != x.dtype or self.pe.device != x.device:
                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                return
        pe = torch.zeros(x.size(1), self.embedding_dim)
        if self.reverse:
            position = torch.arange(
                x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
        else:
            position = torch.arange(
                0, x.size(1), dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) *
            -(math.log(10000.0) / self.embedding_dim))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.pe = pe.to(device=x.device, dtype=x.dtype).detach()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        self.extend_pe(x)
        output = x.unsqueeze(-1) if x.ndim == 2 else x
        output = output * self.x_scale + self.alpha * self.pe[:, :x.size(1)]
        return self.dropout(output)


class DiT1D(nn.Module):
    def __init__(self, io_channels, cond_channels):
        super(DiT1D, self).__init__()
        self.model = DiffusionTransformer(
            io_channels=1,
            embed_dim=768,
            depth=12,
            num_heads=16,
            cond_token_dim=0,
            input_concat_dim=192+cond_channels,
            transformer_type="continuous_transformer",
            patch_size=1
        )
        self.context_dim = cond_channels

        self.band_emb = nn.Embedding(8, 96)
        self.l_position = SinePositionalEmbedding(96, dropout=0.1, scale=False, alpha=True)

    def forward(self, x, t, cond=None,**kwargs):
        x = x.transpose(1,2).unsqueeze(1)
        # cond = F.interpolate(cond, size=cond.shape[-1]//2, mode='linear')
        # cond = F.interpolate(cond, size=cond.shape[-1]*2, mode='linear')

        # cond_shape [N, L, 384]

        bs, ch, l, n_band = x.shape
        x_new = torch.zeros(bs, ch, l* n_band).to(x.device)
        cond_new = torch.zeros(bs, self.context_dim, l* n_band).to(x.device)
        for i in range(n_band):
            x_new[..., i::n_band] = x[..., :, :, i]
            cond_new[..., i::n_band] = cond
        pos = torch.zeros(bs, n_band).to(x.device)
        for i in range(n_band):
            pos[..., i] = i
        pos = self.band_emb(pos.long())
        # pos [N, n_band, 96]
        pos = pos.unsqueeze(2).expand(-1, -1, l, -1)
        # pos [N, n_band, L, 96]

        pos_l =torch.zeros(bs, l).to(x.device)
        pos_l = self.l_position(pos_l)
        # pos [N, L, 96]
        pos_l = pos_l.unsqueeze(1).expand(-1, n_band, -1, -1)
        # pos [N, n_band, L, 96]
        pos = torch.cat([pos, pos_l], dim=-1)
        # pos [N, n_band, L, 192]

        pos_new = torch.zeros(bs, 192, l * n_band).to(x.device)
        for i in range(n_band):
            pos_new[..., i::n_band] = pos[:, i, :, :].transpose(1, 2)
        
        pos_new = torch.cat([pos_new, cond_new], dim=1)
        out= self.model(x_new, t, input_concat_cond=pos_new, **kwargs)
        out_new = torch.zeros(bs, ch, l, n_band).to(x.device)
        for i in range(n_band):
            out_new[..., :, :, i] = out[..., i::n_band]
        out_new = out_new.squeeze(1).transpose(1,2)
        return out_new

if __name__ == '__main__':  
    model = DiT1D(8,192).cuda()
    print(model)
    print('n parameters:', sum(p.numel() for p in model.parameters())/1e6, 'M')
    x = torch.randn(2, 8, 111).cuda()
    t = torch.rand(2).cuda()
    cond = torch.randn(2, 192, 111).cuda()
    out = model(x, t, cond)
    print(out.shape)  # [2, 64, 111]