import torch
from torch import nn
import torch.nn.functional as nnf
from typing import Optional


class MlpTransformer(nn.Module):

    def __init__(self, in_dim, h_dim, out_d: 'Optional[int]'=None, act=nnf.
        relu, dropout=0.0):
        super().__init__()
        out_d = out_d if out_d is not None else in_dim
        self.fc1 = nn.Linear(in_dim, h_dim)
        self.act = act
        self.fc2 = nn.Linear(h_dim, out_d)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x


class MultiHeadAttention(nn.Module):

    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim_self // num_heads
        self.scale = head_dim ** -0.5
        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
        self.project = nn.Linear(dim_self, dim_self)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, y=None, mask=None):
        y = y if y is not None else x
        b, n, c = x.shape
        _, m, _d = y.shape
        queries = self.to_queries(x).reshape(b, n, self.num_heads, c //
            self.num_heads)
        keys_values = self.to_keys_values(y).reshape(b, m, 2, self.
            num_heads, c // self.num_heads)
        keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
        attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
        if mask is not None:
            if mask.dim() == 2:
                mask = mask.unsqueeze(1)
            attention = attention.masked_fill(mask.unsqueeze(3), float('-inf'))
        attention = attention.softmax(dim=2)
        out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b,
            n, c)
        out = self.project(out)
        return out, attention


class TransformerLayer(nn.Module):

    def forward_with_attention(self, x, y=None, mask=None):
        x_, attention = self.attn(self.norm1(x), y, mask)
        x = x + x_
        x = x + self.mlp(self.norm2(x))
        return x, attention

    def forward(self, x, y=None, mask=None):
        x = x + self.attn(self.norm1(x), y, mask)[0]
        x = x + self.mlp(self.norm2(x))
        return x

    def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4.0, bias=
        False, dropout=0.0, act=nnf.relu, norm_layer: 'nn.Module'=nn.LayerNorm
        ):
        super().__init__()
        self.norm1 = norm_layer(dim_self)
        self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=
            bias, dropout=dropout)
        self.norm2 = norm_layer(dim_self)
        self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=
            act, dropout=dropout)


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'dim_self': 4, 'dim_ref': 4, 'num_heads': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
import torch.nn.functional as nnf
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (8 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr1 + (16 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (24 + x0 + 32 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp31, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 8 * x0 + 32 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 8 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_8(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 8), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf3, buf4, buf5, buf6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_3[grid(256)](buf3, buf4, buf5, buf6, buf7, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf6
        triton_poi_fused_clone_4[grid(16, 4)](buf4, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_5[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf11)
        buf12 = buf1
        del buf1
        buf13 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_6[grid(16)](primals_3, buf11,
            primals_7, buf12, buf13, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_7[grid(64)](primals_3, buf11,
            primals_7, buf12, buf13, primals_8, primals_9, buf14, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf12
        del buf13
        del primals_9
        buf15 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 16), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf15, (4, 4, 16), (64, 16, 1), 0)
        del buf15
        buf19 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_8[grid(256)](buf16,
            primals_11, buf19, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf16, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf17)
        buf18 = reinterpret_tensor(buf17, (4, 4, 4), (16, 4, 1), 0)
        del buf17
        triton_poi_fused_add_9[grid(64)](buf18, primals_3, buf11, primals_7,
            primals_13, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
    return buf18, primals_3, primals_7, primals_8, reinterpret_tensor(buf2,
        (16, 4), (4, 1), 0), buf3, reinterpret_tensor(buf4, (4, 1, 4, 4, 1),
        (32, 1, 8, 1, 1), 0), reinterpret_tensor(buf10, (16, 4), (4, 1), 0
        ), buf11, reinterpret_tensor(buf14, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf16, (16, 16), (16, 1), 0
        ), primals_12, buf19, primals_10, primals_6, reinterpret_tensor(buf7,
        (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf8, (16, 1, 4), (4,
        1, 1), 0), primals_5, primals_4


class MlpTransformer(nn.Module):

    def __init__(self, in_dim, h_dim, out_d: 'Optional[int]'=None, act=nnf.
        relu, dropout=0.0):
        super().__init__()
        out_d = out_d if out_d is not None else in_dim
        self.fc1 = nn.Linear(in_dim, h_dim)
        self.act = act
        self.fc2 = nn.Linear(h_dim, out_d)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x


class MultiHeadAttention(nn.Module):

    def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim_self // num_heads
        self.scale = head_dim ** -0.5
        self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
        self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
        self.project = nn.Linear(dim_self, dim_self)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, y=None, mask=None):
        y = y if y is not None else x
        b, n, c = x.shape
        _, m, _d = y.shape
        queries = self.to_queries(x).reshape(b, n, self.num_heads, c //
            self.num_heads)
        keys_values = self.to_keys_values(y).reshape(b, m, 2, self.
            num_heads, c // self.num_heads)
        keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
        attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
        if mask is not None:
            if mask.dim() == 2:
                mask = mask.unsqueeze(1)
            attention = attention.masked_fill(mask.unsqueeze(3), float('-inf'))
        attention = attention.softmax(dim=2)
        out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b,
            n, c)
        out = self.project(out)
        return out, attention


class TransformerLayerNew(nn.Module):

    def forward_with_attention(self, x, y=None, mask=None):
        x_, attention = self.attn(self.norm1(x), y, mask)
        x = x + x_
        x = x + self.mlp(self.norm2(x))
        return x, attention

    def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4.0, bias=
        False, dropout=0.0, act=nnf.relu, norm_layer: 'nn.Module'=nn.LayerNorm
        ):
        super().__init__()
        self.norm1 = norm_layer(dim_self)
        self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=
            bias, dropout=dropout)
        self.norm2 = norm_layer(dim_self)
        self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=
            act, dropout=dropout)

    def forward(self, input_0):
        primals_1 = self.norm1.weight
        primals_2 = self.norm1.bias
        primals_4 = self.attn.to_queries.weight
        primals_5 = self.attn.to_keys_values.weight
        primals_6 = self.attn.project.weight
        primals_7 = self.attn.project.bias
        primals_8 = self.norm2.weight
        primals_9 = self.norm2.bias
        primals_10 = self.mlp.fc1.weight
        primals_11 = self.mlp.fc1.bias
        primals_12 = self.mlp.fc2.weight
        primals_13 = self.mlp.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
