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


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=1.0,
        base=10000,
        base_rescale_factor=1.0,
    ):
        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 = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

        assert interpolation_factor >= 1.0
        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, 1.0

        seq_len = t.shape[0]
        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=False,
        natten_kernel_size=None,
        window_size=-1,
    ):
        super().__init__()
        self.dim = dim
        self.dim_heads = dim_heads
        self.causal = causal
        self.window_size = window_size

        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 qk_norm:
            self.q_ln = nn.LayerNorm(dim_heads)
            self.k_ln = nn.LayerNorm(dim_heads)

        # 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

        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], 0.0)

        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:
            # original normalization method in stable audio tools
            # q = F.normalize(q, dim=-1)
            # k = F.normalize(k, dim=-1)

            # from sunoGPT using LayerNorm
            # # applying per head as per: 2309.14322 Figure E.8
            q = self.q_ln(q)
            k = self.k_ln(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.window_size > 0:
            assert (
                self.use_fa_flash
            ), "Flash Attention 2 is required for windowed attention"

        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, window_size=(self.window_size, self.window_size)
            )

            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 = 1.0 / (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

        out = self.to_out(out)

        if mask is not None:
            mask = rearrange(mask, "b n -> b n 1")
            out = out.masked_fill(~mask, 0.0)

        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,
        window_size=-1,
        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,
            window_size=window_size,
            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,
        use_checkpointing=True,
        chunk_size=1,
        window_size=-1,
        **kwargs,
    ):
        super().__init__()

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

        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(dim_heads)
        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,
                    window_size=window_size,
                    **kwargs,
                )
            )

    def chunk_forward(self, x, start, end, rotary_pos_emb, global_cond, **kwargs):
        for layer in self.layers[start:end]:
            x = layer(
                x, rotary_pos_emb=rotary_pos_emb, global_cond=global_cond, **kwargs
            )
        return x

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

        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:
            x = x + self.pos_emb(x)

        for i in range(0, len(self.layers), self.chunk_size):
            end = min(i + self.chunk_size, len(self.layers))
            if self.use_checkpointing:
                x = checkpoint(
                    self.chunk_forward, x, i, end, rotary_pos_emb, global_cond, **kwargs
                )
            else:
                x = self.chunk_forward(x, i, end, rotary_pos_emb, global_cond, **kwargs)

        if False:
            # Iterate over the transformer layers
            # old checkpoint method
            for layer in self.layers:
                #
                if self.use_checkpointing:
                    x = checkpoint(
                        layer,
                        x,
                        rotary_pos_emb=rotary_pos_emb,
                        global_cond=global_cond,
                        **kwargs,
                    )
                else:
                    x = layer(
                        x,
                        rotary_pos_emb=rotary_pos_emb,
                        global_cond=global_cond,
                        **kwargs,
                    )

        x = self.project_out(x)

        return x