import math
import torch
import torch.nn as nn
import torch.nn.functional as F


def attention(q, k, v, d_k, mask=None, dropout=None):
    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
    if mask is not None:
        mask = mask.unsqueeze(1)
        scores = scores.masked_fill(mask == 0, -1000000000.0)
    scores = F.softmax(scores, dim=-1)
    if dropout is not None:
        scores = dropout(scores)
    output = torch.matmul(scores, v)
    return output


class FeedForward(nn.Module):

    def __init__(self, d_model, d_ff=2048, dropout=0.1):
        super().__init__()
        self.linear_1 = nn.Linear(d_model, d_ff)
        self.dropout = nn.Dropout(dropout)
        self.linear_2 = nn.Linear(d_ff, d_model)

    def forward(self, x):
        x = self.dropout(F.relu(self.linear_1(x)))
        x = self.linear_2(x)
        return x


class MultiHeadAttention(nn.Module):

    def __init__(self, heads, d_model, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.d_k = d_model // heads
        self.h = heads
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
        k = k.transpose(1, 2)
        q = q.transpose(1, 2)
        v = v.transpose(1, 2)
        scores = attention(q, k, v, self.d_k, mask, self.dropout)
        concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
        output = self.out(concat)
        return output


class Norm(nn.Module):

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

    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
            =-1, keepdim=True) + self.eps) + self.bias
        return norm


class DecoderLayer(nn.Module):

    def __init__(self, d_model, heads, dropout=0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.norm_3 = Norm(d_model)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)
        self.dropout_3 = nn.Dropout(dropout)
        self.attn_1 = MultiHeadAttention(heads, d_model, dropout=dropout)
        self.attn_2 = MultiHeadAttention(heads, d_model, dropout=dropout)
        self.ff = FeedForward(d_model, dropout=dropout)

    def forward(self, x, e_outputs, src_mask, trg_mask):
        x2 = self.norm_1(x)
        x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
        x2 = self.norm_2(x)
        x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs, src_mask))
        x2 = self.norm_3(x)
        x = x + self.dropout_3(self.ff(x2))
        return x


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


def get_init_inputs():
    return [[], {'d_model': 4, '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
import math
import torch.nn as nn
import torch.nn.functional as F
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_add_div_mean_mul_std_sub_0(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)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, 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')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_eq_2(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_3(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
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last').to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last').to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last').to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x3, tmp20, xmask)
    tl.store(out_ptr1 + x3, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_4(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 64
    x4 = xindex % 16
    x5 = xindex
    x6 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x4 + 16 * x3), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x5, xmask)
    tmp6 = tl.load(in_ptr1 + x6, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x5, tmp10, xmask)


@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_mean_std_6(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(out_ptr0 + x0, tmp16, xmask)
    tl.store(in_out_ptr0 + x0, tmp29, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_7(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_add_8(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_9(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_add_10(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)


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, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (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), (16, 4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4, 4), (4, 1))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (4, 4), (4, 1))
    assert_size_stride(primals_21, (4,), (1,))
    assert_size_stride(primals_22, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_23, (4, 4), (4, 1))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4,), (1,))
    assert_size_stride(primals_26, (4,), (1,))
    assert_size_stride(primals_27, (2048, 4), (4, 1))
    assert_size_stride(primals_28, (2048,), (1,))
    assert_size_stride(primals_29, (4, 2048), (2048, 1))
    assert_size_stride(primals_30, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_std_sub_0[grid(64)](primals_1,
            primals_2, primals_3, buf0, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf2, primals_7, buf4, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_7
        buf5 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf2
        triton_poi_fused_clone_1[grid(16, 4)](buf1, primals_5, buf5, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        triton_poi_fused_eq_2[grid(64)](primals_10, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_10
        buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
        del buf1
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_3[grid(64)](buf7, buf6,
            buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused__softmax_div_masked_fill_4[grid(256)](buf10, buf7,
            buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_1[grid(16, 4)](buf3, primals_9, buf11, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_9
        buf12 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
        buf13 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_5[grid(16, 4)](buf12, buf13, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
        del buf12
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf13, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf14)
        del primals_12
        buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf17 = buf16
        del buf16
        triton_poi_fused_add_mean_std_6[grid(16)](buf17, primals_2, buf14,
            buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf18)
        del primals_15
        buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_7[grid(64)](primals_13,
            primals_2, buf14, buf15, buf17, primals_14, buf19, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del buf15
        del buf17
        del primals_14
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_18, (4, 4), (1, 4), 0), out=buf20)
        buf21 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_20, (4, 4), (1, 4), 0), out=buf21)
        del primals_20
        buf22 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf20, primals_19, buf22, 16,
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_19
        buf23 = reinterpret_tensor(buf20, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf20
        triton_poi_fused_clone_1[grid(16, 4)](buf18, primals_16, buf23, 16,
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_16
        buf24 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf22, (16, 4, 1), (4, 1, 0),
            0), reinterpret_tensor(buf23, (16, 1, 4), (4, 0, 1), 0), out=buf24)
        buf25 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        triton_poi_fused_eq_2[grid(64)](primals_22, buf25, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_22
        buf26 = reinterpret_tensor(buf18, (4, 4, 4, 1), (16, 4, 1, 64), 0)
        del buf18
        buf27 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_3[grid(64)](buf25, buf24,
            buf26, buf27, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf28 = reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf24
        triton_poi_fused__softmax_div_masked_fill_4[grid(256)](buf28, buf25,
            buf26, buf27, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf29 = reinterpret_tensor(buf27, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf27
        triton_poi_fused_clone_1[grid(16, 4)](buf21, primals_21, buf29, 16,
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_21
        buf30 = reinterpret_tensor(buf21, (16, 4, 1), (4, 1, 1), 0)
        del buf21
        extern_kernels.bmm(reinterpret_tensor(buf28, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf29, (16, 4, 1), (4, 1, 0), 0), out=buf30)
        buf31 = reinterpret_tensor(buf26, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf26
        triton_poi_fused_clone_5[grid(16, 4)](buf30, buf31, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf32 = reinterpret_tensor(buf30, (16, 4), (4, 1), 0)
        del buf30
        extern_kernels.mm(reinterpret_tensor(buf31, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_23, (4, 4), (1, 4), 0), out=buf32)
        buf33 = reinterpret_tensor(buf32, (4, 4, 4), (16, 4, 1), 0)
        del buf32
        triton_poi_fused_add_8[grid(64)](buf33, primals_2, buf14,
            primals_24, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_24
        buf34 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_0[grid(64)](primals_25,
            buf33, primals_26, buf34, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_26
        buf35 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf34, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_27, (4, 2048), (1, 4), 0), out=buf35)
        buf36 = reinterpret_tensor(buf35, (4, 4, 2048), (8192, 2048, 1), 0)
        del buf35
        buf39 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_9[grid(32768)](buf36,
            primals_28, buf39, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_28
        buf37 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf36, (16, 2048), (2048, 1), 
            0), reinterpret_tensor(primals_29, (2048, 4), (1, 2048), 0),
            out=buf37)
        buf38 = reinterpret_tensor(buf37, (4, 4, 4), (16, 4, 1), 0)
        del buf37
        triton_poi_fused_add_10[grid(64)](buf38, buf33, primals_30, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_30
    return buf38, primals_2, primals_13, primals_25, reinterpret_tensor(buf0,
        (16, 4), (4, 1), 0), buf7, buf10, reinterpret_tensor(buf13, (16, 4),
        (4, 1), 0), buf14, reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf19, (16, 4), (4, 1), 0
        ), buf25, buf28, reinterpret_tensor(buf31, (16, 4), (4, 1), 0
        ), buf33, reinterpret_tensor(buf34, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf36, (16, 2048), (2048, 1), 0
        ), primals_29, buf39, primals_27, primals_23, reinterpret_tensor(buf29,
        (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf22, (16, 1, 4), (4,
        1, 1), 0), reinterpret_tensor(buf23, (16, 4, 1), (4, 1, 4), 0
        ), primals_18, primals_11, reinterpret_tensor(buf11, (16, 1, 4), (4,
        1, 1), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 4), 0
        ), primals_8, primals_6, primals_4


def attention(q, k, v, d_k, mask=None, dropout=None):
    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
    if mask is not None:
        mask = mask.unsqueeze(1)
        scores = scores.masked_fill(mask == 0, -1000000000.0)
    scores = F.softmax(scores, dim=-1)
    if dropout is not None:
        scores = dropout(scores)
    output = torch.matmul(scores, v)
    return output


class FeedForward(nn.Module):

    def __init__(self, d_model, d_ff=2048, dropout=0.1):
        super().__init__()
        self.linear_1 = nn.Linear(d_model, d_ff)
        self.dropout = nn.Dropout(dropout)
        self.linear_2 = nn.Linear(d_ff, d_model)

    def forward(self, x):
        x = self.dropout(F.relu(self.linear_1(x)))
        x = self.linear_2(x)
        return x


class MultiHeadAttention(nn.Module):

    def __init__(self, heads, d_model, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.d_k = d_model // heads
        self.h = heads
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
        k = k.transpose(1, 2)
        q = q.transpose(1, 2)
        v = v.transpose(1, 2)
        scores = attention(q, k, v, self.d_k, mask, self.dropout)
        concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
        output = self.out(concat)
        return output


class Norm(nn.Module):

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

    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
            =-1, keepdim=True) + self.eps) + self.bias
        return norm


class DecoderLayerNew(nn.Module):

    def __init__(self, d_model, heads, dropout=0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.norm_3 = Norm(d_model)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)
        self.dropout_3 = nn.Dropout(dropout)
        self.attn_1 = MultiHeadAttention(heads, d_model, dropout=dropout)
        self.attn_2 = MultiHeadAttention(heads, d_model, dropout=dropout)
        self.ff = FeedForward(d_model, dropout=dropout)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.norm_1.alpha
        primals_3 = self.norm_1.bias
        primals_5 = self.norm_2.alpha
        primals_7 = self.norm_2.bias
        primals_9 = self.norm_3.alpha
        primals_12 = self.norm_3.bias
        primals_4 = self.attn_1.q_linear.weight
        primals_13 = self.attn_1.q_linear.bias
        primals_6 = self.attn_1.v_linear.weight
        primals_14 = self.attn_1.v_linear.bias
        primals_8 = self.attn_1.k_linear.weight
        primals_16 = self.attn_1.k_linear.bias
        primals_11 = self.attn_1.out.weight
        primals_19 = self.attn_1.out.bias
        primals_15 = self.attn_2.q_linear.weight
        primals_21 = self.attn_2.q_linear.bias
        primals_18 = self.attn_2.v_linear.weight
        primals_24 = self.attn_2.v_linear.bias
        primals_20 = self.attn_2.k_linear.weight
        primals_25 = self.attn_2.k_linear.bias
        primals_23 = self.attn_2.out.weight
        primals_26 = self.attn_2.out.bias
        primals_27 = self.ff.linear_1.weight
        primals_28 = self.ff.linear_1.bias
        primals_29 = self.ff.linear_2.weight
        primals_30 = self.ff.linear_2.bias
        primals_2 = input_0
        primals_10 = input_1
        primals_17 = input_2
        primals_22 = input_3
        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, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30])
        return output[0]
