import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed


def matmul(x, y):
    if x.dim() == y.dim():
        return x @ y
    if x.dim() == y.dim() - 1:
        return (x.unsqueeze(-2) @ y).squeeze(-2)
    return (x @ y.unsqueeze(-2)).squeeze(-2)


class Linear(nn.Linear):

    def forward(self, x):
        size = x.size()
        return super().forward(x.contiguous().view(-1, size[-1])).view(*
            size[:-1], -1)


class FeedForward(nn.Module):

    def __init__(self, d_model, d_hidden):
        super().__init__()
        self.linear1 = Linear(d_model, d_hidden)
        self.linear2 = Linear(d_hidden, d_model)

    def forward(self, x):
        return self.linear2(F.relu(self.linear1(x)))


class Attention(nn.Module):

    def __init__(self, d_key, drop_ratio, causal):
        super().__init__()
        self.scale = math.sqrt(d_key)
        self.dropout = nn.Dropout(drop_ratio)
        self.causal = causal

    def forward(self, query, key, value):
        dot_products = matmul(query, key.transpose(1, 2))
        if query.dim() == 3 and (self is None or self.causal):
            tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF
            if key.is_cuda:
                tri = tri
            dot_products.data.sub_(tri.unsqueeze(0))
        return matmul(self.dropout(F.softmax(dot_products / self.scale, dim
            =2)), value)


class MultiHead(nn.Module):

    def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False):
        super().__init__()
        self.attention = Attention(d_key, drop_ratio, causal=causal)
        self.wq = Linear(d_key, d_key, bias=False)
        self.wk = Linear(d_key, d_key, bias=False)
        self.wv = Linear(d_value, d_value, bias=False)
        self.wo = Linear(d_value, d_key, bias=False)
        self.n_heads = n_heads

    def forward(self, query, key, value):
        query, key, value = self.wq(query), self.wk(key), self.wv(value)
        query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key,
            value))
        return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in
            zip(query, key, value)], -1))


class LayerNorm(nn.Module):

    def __init__(self, d_model, eps=1e-06):
        super().__init__()
        self.gamma = nn.Parameter(torch.ones(d_model))
        self.beta = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.gamma * (x - mean) / (std + self.eps) + self.beta


class ResidualBlock(nn.Module):

    def __init__(self, layer, d_model, drop_ratio):
        super().__init__()
        self.layer = layer
        self.dropout = nn.Dropout(drop_ratio)
        self.layernorm = LayerNorm(d_model)

    def forward(self, *x):
        return self.layernorm(x[0] + self.dropout(self.layer(*x)))


class EncoderLayer(nn.Module):

    def __init__(self, d_model, d_hidden, n_heads, drop_ratio):
        super().__init__()
        self.selfattn = ResidualBlock(MultiHead(d_model, d_model, n_heads,
            drop_ratio), d_model, drop_ratio)
        self.feedforward = ResidualBlock(FeedForward(d_model, d_hidden),
            d_model, drop_ratio)

    def forward(self, x):
        return self.feedforward(self.selfattn(x, x, x))


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


def get_init_inputs():
    return [[], {'d_model': 4, 'd_hidden': 4, 'n_heads': 4, 'drop_ratio': 0.5}]

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
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
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__softmax_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + x1, tmp16 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_add_mean_std_3(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
    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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tl.store(in_out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr0 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_4(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp4 = 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')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tmp0 * tmp5
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = 1e-06
    tmp10 = tmp8 + tmp9
    tmp11 = tmp6 / tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_ptr0,
    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
    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_6(in_out_ptr0, in_ptr0, in_ptr1, 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_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_7(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp0 * tmp11
    tmp13 = tmp2 - tmp10
    tmp14 = tmp13 * tmp13
    tmp15 = tmp3 - tmp10
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tmp5 - tmp10
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = tmp7 - tmp10
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = 3.0
    tmp25 = tmp23 / tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tmp29 = tmp12 / tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x2, tmp31, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4,
            1), 0), out=buf6)
        buf7 = buf4
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = buf7
        del buf7
        triton_poi_fused__softmax_1[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4,
            1), 1), out=buf10)
        buf11 = buf8
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf11, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = buf11
        del buf11
        triton_poi_fused__softmax_1[grid(64)](buf12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 
            4, 1), 2), out=buf14)
        buf15 = buf12
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf15, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 
            4, 1), 3), out=buf18)
        buf19 = buf16
        del buf16
        triton_poi_fused_cat_2[grid(64)](buf6, buf10, buf14, buf18, buf19, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf10
        del buf14
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf20)
        buf21 = reinterpret_tensor(buf6, (4, 4, 1), (4, 1, 16), 0)
        del buf6
        buf22 = buf21
        del buf21
        buf23 = reinterpret_tensor(buf18, (4, 4, 1), (4, 1, 16), 0)
        del buf18
        triton_poi_fused_add_mean_std_3[grid(16)](buf22, primals_1, buf20,
            buf23, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf24 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_4[grid(64)](primals_6,
            primals_1, buf20, buf23, buf22, primals_7, buf24, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf22
        del buf23
        del primals_7
        buf25 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf24, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf25)
        buf26 = reinterpret_tensor(buf25, (4, 4, 4), (16, 4, 1), 0)
        del buf25
        buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_5[grid(64)](buf26,
            primals_9, buf30, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf27 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf26, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf27)
        buf28 = reinterpret_tensor(buf27, (4, 4, 4), (16, 4, 1), 0)
        del buf27
        triton_poi_fused_add_6[grid(64)](buf28, buf24, primals_11, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_11
        buf29 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_7[grid(64)](primals_12,
            buf28, primals_13, buf29, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
    return (buf29, primals_1, primals_6, primals_12, buf5, buf9, buf13,
        buf17, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), buf20,
        reinterpret_tensor(buf24, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf26, (16, 4), (4, 1), 0), buf28, primals_10, buf30, primals_8,
        primals_5, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3),
        reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3),
        reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3),
        reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2),
        reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2),
        reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2),
        reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1),
        reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1),
        reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1),
        reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0))


def matmul(x, y):
    if x.dim() == y.dim():
        return x @ y
    if x.dim() == y.dim() - 1:
        return (x.unsqueeze(-2) @ y).squeeze(-2)
    return (x @ y.unsqueeze(-2)).squeeze(-2)


class Linear(nn.Linear):

    def forward(self, x):
        size = x.size()
        return super().forward(x.contiguous().view(-1, size[-1])).view(*
            size[:-1], -1)


class FeedForward(nn.Module):

    def __init__(self, d_model, d_hidden):
        super().__init__()
        self.linear1 = Linear(d_model, d_hidden)
        self.linear2 = Linear(d_hidden, d_model)

    def forward(self, x):
        return self.linear2(F.relu(self.linear1(x)))


class Attention(nn.Module):

    def __init__(self, d_key, drop_ratio, causal):
        super().__init__()
        self.scale = math.sqrt(d_key)
        self.dropout = nn.Dropout(drop_ratio)
        self.causal = causal

    def forward(self, query, key, value):
        dot_products = matmul(query, key.transpose(1, 2))
        if query.dim() == 3 and (self is None or self.causal):
            tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF
            if key.is_cuda:
                tri = tri
            dot_products.data.sub_(tri.unsqueeze(0))
        return matmul(self.dropout(F.softmax(dot_products / self.scale, dim
            =2)), value)


class MultiHead(nn.Module):

    def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False):
        super().__init__()
        self.attention = Attention(d_key, drop_ratio, causal=causal)
        self.wq = Linear(d_key, d_key, bias=False)
        self.wk = Linear(d_key, d_key, bias=False)
        self.wv = Linear(d_value, d_value, bias=False)
        self.wo = Linear(d_value, d_key, bias=False)
        self.n_heads = n_heads

    def forward(self, query, key, value):
        query, key, value = self.wq(query), self.wk(key), self.wv(value)
        query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key,
            value))
        return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in
            zip(query, key, value)], -1))


class LayerNorm(nn.Module):

    def __init__(self, d_model, eps=1e-06):
        super().__init__()
        self.gamma = nn.Parameter(torch.ones(d_model))
        self.beta = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.gamma * (x - mean) / (std + self.eps) + self.beta


class ResidualBlock(nn.Module):

    def __init__(self, layer, d_model, drop_ratio):
        super().__init__()
        self.layer = layer
        self.dropout = nn.Dropout(drop_ratio)
        self.layernorm = LayerNorm(d_model)

    def forward(self, *x):
        return self.layernorm(x[0] + self.dropout(self.layer(*x)))


class EncoderLayerNew(nn.Module):

    def __init__(self, d_model, d_hidden, n_heads, drop_ratio):
        super().__init__()
        self.selfattn = ResidualBlock(MultiHead(d_model, d_model, n_heads,
            drop_ratio), d_model, drop_ratio)
        self.feedforward = ResidualBlock(FeedForward(d_model, d_hidden),
            d_model, drop_ratio)

    def forward(self, input_0):
        primals_2 = self.selfattn.layer.wq.weight
        primals_3 = self.selfattn.layer.wk.weight
        primals_4 = self.selfattn.layer.wv.weight
        primals_5 = self.selfattn.layer.wo.weight
        primals_6 = self.selfattn.layernorm.gamma
        primals_7 = self.selfattn.layernorm.beta
        primals_8 = self.feedforward.layer.linear1.weight
        primals_9 = self.feedforward.layer.linear1.bias
        primals_10 = self.feedforward.layer.linear2.weight
        primals_11 = self.feedforward.layer.linear2.bias
        primals_12 = self.feedforward.layernorm.gamma
        primals_13 = self.feedforward.layernorm.beta
        primals_1 = 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]
