from __future__ import annotations
import math
import psutil

import torch
from torch import einsum

from ldm.util import default
from einops import rearrange

from modules import shared, errors, devices, sub_quadratic_attention
from modules.hypernetworks import hypernetwork

import ldm.modules.attention
import ldm.modules.diffusionmodules.model

import sgm.modules.attention
import sgm.modules.diffusionmodules.model

diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
sgm_diffusionmodules_model_AttnBlock_forward = sgm.modules.diffusionmodules.model.AttnBlock.forward


class SdOptimization:
    name: str = None
    label: str | None = None
    cmd_opt: str | None = None
    priority: int = 0

    def title(self):
        if self.label is None:
            return self.name

        return f"{self.name} - {self.label}"

    def is_available(self):
        return True

    def apply(self):
        pass

    def undo(self):
        ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward

        sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = sgm_diffusionmodules_model_AttnBlock_forward


class SdOptimizationXformers(SdOptimization):
    name = "xformers"
    cmd_opt = "xformers"
    priority = 100

    def is_available(self):
        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
        sgm.modules.attention.CrossAttention.forward = xformers_attention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward


class SdOptimizationSdpNoMem(SdOptimization):
    name = "sdp-no-mem"
    label = "scaled dot product without memory efficient attention"
    cmd_opt = "opt_sdp_no_mem_attention"
    priority = 80

    def is_available(self):
        return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
        sgm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward


class SdOptimizationSdp(SdOptimizationSdpNoMem):
    name = "sdp"
    label = "scaled dot product"
    cmd_opt = "opt_sdp_attention"
    priority = 70

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
        sgm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward


class SdOptimizationSubQuad(SdOptimization):
    name = "sub-quadratic"
    cmd_opt = "opt_sub_quad_attention"
    priority = 10

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
        sgm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward


class SdOptimizationV1(SdOptimization):
    name = "V1"
    label = "original v1"
    cmd_opt = "opt_split_attention_v1"
    priority = 10

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1


class SdOptimizationInvokeAI(SdOptimization):
    name = "InvokeAI"
    cmd_opt = "opt_split_attention_invokeai"

    @property
    def priority(self):
        return 1000 if not torch.cuda.is_available() else 10

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI


class SdOptimizationDoggettx(SdOptimization):
    name = "Doggettx"
    cmd_opt = "opt_split_attention"
    priority = 90

    def apply(self):
        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward
        sgm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward


def list_optimizers(res):
    res.extend([
        SdOptimizationXformers(),
        SdOptimizationSdpNoMem(),
        SdOptimizationSdp(),
        SdOptimizationSubQuad(),
        SdOptimizationV1(),
        SdOptimizationInvokeAI(),
        SdOptimizationDoggettx(),
    ])


if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
    try:
        import xformers.ops
        shared.xformers_available = True
    except Exception:
        errors.report("Cannot import xformers", exc_info=True)


def get_available_vram():
    if shared.device.type == 'cuda':
        stats = torch.cuda.memory_stats(shared.device)
        mem_active = stats['active_bytes.all.current']
        mem_reserved = stats['reserved_bytes.all.current']
        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
        mem_free_torch = mem_reserved - mem_active
        mem_free_total = mem_free_cuda + mem_free_torch
        return mem_free_total
    else:
        return psutil.virtual_memory().available


# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
def split_cross_attention_forward_v1(self, x, context=None, mask=None, **kwargs):
    h = self.heads

    q_in = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)
    del context, context_k, context_v, x

    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
    del q_in, k_in, v_in

    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k, v = q.float(), k.float(), v.float()

    with devices.without_autocast(disable=not shared.opts.upcast_attn):
        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
        for i in range(0, q.shape[0], 2):
            end = i + 2
            s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
            s1 *= self.scale

            s2 = s1.softmax(dim=-1)
            del s1

            r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
            del s2
        del q, k, v

    r1 = r1.to(dtype)

    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
    del r1

    return self.to_out(r2)


# taken from https://github.com/Doggettx/stable-diffusion and modified
def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
    h = self.heads

    q_in = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)

    dtype = q_in.dtype
    if shared.opts.upcast_attn:
        q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()

    with devices.without_autocast(disable=not shared.opts.upcast_attn):
        k_in = k_in * self.scale

        del context, x

        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
        del q_in, k_in, v_in

        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

        mem_free_total = get_available_vram()

        gb = 1024 ** 3
        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
        modifier = 3 if q.element_size() == 2 else 2.5
        mem_required = tensor_size * modifier
        steps = 1

        if mem_required > mem_free_total:
            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
            # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
            #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")

        if steps > 64:
            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                               f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')

        slice_size = q.shape[1] // steps
        for i in range(0, q.shape[1], slice_size):
            end = min(i + slice_size, q.shape[1])
            s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)

            s2 = s1.softmax(dim=-1, dtype=q.dtype)
            del s1

            r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
            del s2

        del q, k, v

    r1 = r1.to(dtype)

    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
    del r1

    return self.to_out(r2)


# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
mem_total_gb = psutil.virtual_memory().total // (1 << 30)


def einsum_op_compvis(q, k, v):
    s = einsum('b i d, b j d -> b i j', q, k)
    s = s.softmax(dim=-1, dtype=s.dtype)
    return einsum('b i j, b j d -> b i d', s, v)


def einsum_op_slice_0(q, k, v, slice_size):
    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
    for i in range(0, q.shape[0], slice_size):
        end = i + slice_size
        r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
    return r


def einsum_op_slice_1(q, k, v, slice_size):
    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
    for i in range(0, q.shape[1], slice_size):
        end = i + slice_size
        r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
    return r


def einsum_op_mps_v1(q, k, v):
    if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
        return einsum_op_compvis(q, k, v)
    else:
        slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
        if slice_size % 4096 == 0:
            slice_size -= 1
        return einsum_op_slice_1(q, k, v, slice_size)


def einsum_op_mps_v2(q, k, v):
    if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
        return einsum_op_compvis(q, k, v)
    else:
        return einsum_op_slice_0(q, k, v, 1)


def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
    size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
    if size_mb <= max_tensor_mb:
        return einsum_op_compvis(q, k, v)
    div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
    if div <= q.shape[0]:
        return einsum_op_slice_0(q, k, v, q.shape[0] // div)
    return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))


def einsum_op_cuda(q, k, v):
    stats = torch.cuda.memory_stats(q.device)
    mem_active = stats['active_bytes.all.current']
    mem_reserved = stats['reserved_bytes.all.current']
    mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
    mem_free_torch = mem_reserved - mem_active
    mem_free_total = mem_free_cuda + mem_free_torch
    # Divide factor of safety as there's copying and fragmentation
    return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))


def einsum_op(q, k, v):
    if q.device.type == 'cuda':
        return einsum_op_cuda(q, k, v)

    if q.device.type == 'mps':
        if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
            return einsum_op_mps_v1(q, k, v)
        return einsum_op_mps_v2(q, k, v)

    # Smaller slices are faster due to L2/L3/SLC caches.
    # Tested on i7 with 8MB L3 cache.
    return einsum_op_tensor_mem(q, k, v, 32)


def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None, **kwargs):
    h = self.heads

    q = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k = self.to_k(context_k)
    v = self.to_v(context_v)
    del context, context_k, context_v, x

    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()

    with devices.without_autocast(disable=not shared.opts.upcast_attn):
        k = k * self.scale

        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
        r = einsum_op(q, k, v)
    r = r.to(dtype)
    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))

# -- End of code from https://github.com/invoke-ai/InvokeAI --


# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
def sub_quad_attention_forward(self, x, context=None, mask=None, **kwargs):
    assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."

    h = self.heads

    q = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k = self.to_k(context_k)
    v = self.to_v(context_v)
    del context, context_k, context_v, x

    q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
    k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
    v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)

    if q.device.type == 'mps':
        q, k, v = q.contiguous(), k.contiguous(), v.contiguous()

    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k = q.float(), k.float()

    x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)

    x = x.to(dtype)

    x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)

    out_proj, dropout = self.to_out
    x = out_proj(x)
    x = dropout(x)

    return x


def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
    bytes_per_token = torch.finfo(q.dtype).bits//8
    batch_x_heads, q_tokens, _ = q.shape
    _, k_tokens, _ = k.shape
    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens

    if chunk_threshold is None:
        chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
    elif chunk_threshold == 0:
        chunk_threshold_bytes = None
    else:
        chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())

    if kv_chunk_size_min is None and chunk_threshold_bytes is not None:
        kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
    elif kv_chunk_size_min == 0:
        kv_chunk_size_min = None

    if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
        # the big matmul fits into our memory limit; do everything in 1 chunk,
        # i.e. send it down the unchunked fast-path
        kv_chunk_size = k_tokens

    with devices.without_autocast(disable=q.dtype == v.dtype):
        return sub_quadratic_attention.efficient_dot_product_attention(
            q,
            k,
            v,
            query_chunk_size=q_chunk_size,
            kv_chunk_size=kv_chunk_size,
            kv_chunk_size_min = kv_chunk_size_min,
            use_checkpoint=use_checkpoint,
        )


def get_xformers_flash_attention_op(q, k, v):
    if not shared.cmd_opts.xformers_flash_attention:
        return None

    try:
        flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
        fw, bw = flash_attention_op
        if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
            return flash_attention_op
    except Exception as e:
        errors.display_once(e, "enabling flash attention")

    return None


def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
    h = self.heads
    q_in = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)

    q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
    del q_in, k_in, v_in

    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k, v = q.float(), k.float(), v.float()

    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))

    out = out.to(dtype)

    out = rearrange(out, 'b n h d -> b n (h d)', h=h)
    return self.to_out(out)


# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
def scaled_dot_product_attention_forward(self, x, context=None, mask=None, **kwargs):
    batch_size, sequence_length, inner_dim = x.shape

    if mask is not None:
        mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
        mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])

    h = self.heads
    q_in = self.to_q(x)
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)

    head_dim = inner_dim // h
    q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
    k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
    v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)

    del q_in, k_in, v_in

    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k, v = q.float(), k.float(), v.float()

    # the output of sdp = (batch, num_heads, seq_len, head_dim)
    hidden_states = torch.nn.functional.scaled_dot_product_attention(
        q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
    )

    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
    hidden_states = hidden_states.to(dtype)

    # linear proj
    hidden_states = self.to_out[0](hidden_states)
    # dropout
    hidden_states = self.to_out[1](hidden_states)
    return hidden_states


def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None, **kwargs):
    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
        return scaled_dot_product_attention_forward(self, x, context, mask)


def cross_attention_attnblock_forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q1 = self.q(h_)
        k1 = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q1.shape

        q2 = q1.reshape(b, c, h*w)
        del q1

        q = q2.permute(0, 2, 1)   # b,hw,c
        del q2

        k = k1.reshape(b, c, h*w) # b,c,hw
        del k1

        h_ = torch.zeros_like(k, device=q.device)

        mem_free_total = get_available_vram()

        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
        mem_required = tensor_size * 2.5
        steps = 1

        if mem_required > mem_free_total:
            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))

        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
        for i in range(0, q.shape[1], slice_size):
            end = i + slice_size

            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
            w2 = w1 * (int(c)**(-0.5))
            del w1
            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
            del w2

            # attend to values
            v1 = v.reshape(b, c, h*w)
            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
            del w3

            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
            del v1, w4

        h2 = h_.reshape(b, c, h, w)
        del h_

        h3 = self.proj_out(h2)
        del h2

        h3 += x

        return h3


def xformers_attnblock_forward(self, x):
    try:
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)
        b, c, h, w = q.shape
        q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
        dtype = q.dtype
        if shared.opts.upcast_attn:
            q, k = q.float(), k.float()
        q = q.contiguous()
        k = k.contiguous()
        v = v.contiguous()
        out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
        out = out.to(dtype)
        out = rearrange(out, 'b (h w) c -> b c h w', h=h)
        out = self.proj_out(out)
        return x + out
    except NotImplementedError:
        return cross_attention_attnblock_forward(self, x)


def sdp_attnblock_forward(self, x):
    h_ = x
    h_ = self.norm(h_)
    q = self.q(h_)
    k = self.k(h_)
    v = self.v(h_)
    b, c, h, w = q.shape
    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
    dtype = q.dtype
    if shared.opts.upcast_attn:
        q, k, v = q.float(), k.float(), v.float()
    q = q.contiguous()
    k = k.contiguous()
    v = v.contiguous()
    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
    out = out.to(dtype)
    out = rearrange(out, 'b (h w) c -> b c h w', h=h)
    out = self.proj_out(out)
    return x + out


def sdp_no_mem_attnblock_forward(self, x):
    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
        return sdp_attnblock_forward(self, x)


def sub_quad_attnblock_forward(self, x):
    h_ = x
    h_ = self.norm(h_)
    q = self.q(h_)
    k = self.k(h_)
    v = self.v(h_)
    b, c, h, w = q.shape
    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
    q = q.contiguous()
    k = k.contiguous()
    v = v.contiguous()
    out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
    out = rearrange(out, 'b (h w) c -> b c h w', h=h)
    out = self.proj_out(out)
    return x + out
