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 torch.nn.functional as F
import torch.nn as nn
import torch.autograd
import torch.nn
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    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 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_tanh_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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_add_tanh_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (1, 4), (4, 1))
    assert_size_stride(primals_12, (1,), (1,))
    assert_size_stride(primals_13, (4, 4), (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, (1, 4), (4, 1))
    assert_size_stride(primals_18, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha
            =1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused__softmax_1[grid(4)](buf3, buf4, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, reinterpret_tensor(primals_1, (4, 1, 4), (
            4, 4, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0)
        del buf6
        triton_poi_fused_add_tanh_2[grid(64)](buf8, primals_8, buf7,
            primals_10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
        del primals_8
        buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0)
        del buf7
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf8, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_11, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_12
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_3[grid(16)](buf10, buf11, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(16)](buf11, buf12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (4, 1, 4), (4, 4, 1), 0)
        del buf11
        extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 0, 1), 
            0), primals_6, out=buf13)
        buf14 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_13, (4, 4),
            (1, 4), 0), out=buf14)
        del primals_13
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf14, (4, 1, 4), (4, 4, 1), 0)
        del buf14
        triton_poi_fused_add_tanh_5[grid(16)](buf16, primals_14, buf15,
            primals_16, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_14
        del primals_16
        buf18 = reinterpret_tensor(buf4, (4, 1), (1, 1), 0)
        del buf4
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf16, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_17, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf18)
        del primals_18
        buf19 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused__softmax_1[grid(4)](buf18, buf19, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf20 = reinterpret_tensor(buf15, (4, 1, 4), (4, 4, 1), 0)
        del buf15
        extern_kernels.bmm(buf19, reinterpret_tensor(primals_1, (4, 1, 4),
            (4, 4, 1), 0), out=buf20)
        del buf19
    return reinterpret_tensor(buf20, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf13, (4, 4), (4, 1), 0
        ), primals_1, primals_6, buf1, buf3, reinterpret_tensor(buf5, (4, 4
        ), (4, 1), 0), buf8, buf10, reinterpret_tensor(buf13, (4, 4), (4, 1), 0
        ), buf16, buf18, primals_17, primals_15, primals_11, primals_9, primals_4


class Attn(nn.Module):
    """
    Unit attention operation for alternating co-attention.
    ``https://arxiv.org/pdf/1606.00061.pdf``
    
    .. math::
        \\begin{array}{ll}
        H = \\tanh(W_x * X + (W_g * g)) \\\\
        a = softmax(w_{hx}^T * H}) \\\\
        output = sum a_i * x_i
        \\end{array}

    Args:
        num_hidden: Number of output hidden size
        input_feat_size: Feature size of input image
        guidance_size:  Feature size of attention guidance [default: 0]
        dropout: Dropout rate of attention operation [default: 0.5]

    Inputs:
        - **X** (batch, input_seq_size, input_feat_size): Input image feature
        - **g** (batch, guidance_size): Attention guidance
    """

    def __init__(self, num_hidden, input_feat_size, guidance_size=0,
        dropout=0.5):
        super(Attn, self).__init__()
        self.num_hidden = num_hidden
        self.input_feat_size = input_feat_size
        self.guidance_size = guidance_size
        self.dropout = nn.Dropout(dropout) if dropout > 0 else None
        self.W_x = nn.Linear(input_feat_size, num_hidden)
        if guidance_size > 0:
            self.W_g = nn.Linear(guidance_size, num_hidden)
        self.W_hx = nn.Linear(num_hidden, 1)

    def forward(self, X, g=None):
        _batch_size, input_seq_size, input_feat_size = X.size()
        feat = self.W_x(X)
        if g is not None:
            g_emb = self.W_g(g).view(-1, 1, self.num_hidden)
            feat = feat + g_emb.expand_as(feat)
        hidden_feat = torch.tanh(feat)
        if self.dropout is not None:
            hidden_feat = self.dropout(hidden_feat)
        attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1)
        attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view(
            -1, input_seq_size, input_feat_size))
        return attn_X.view(-1, input_feat_size)


class AlterCoAttnNew(nn.Module):
    """
    Alternatiing Co-Attention mechanism according to ``https://arxiv.org/pdf/1606.00061.pdf``

    .. math::
        \\begin{array}{ll}
            \\hat{s} = Attn(Q, 0) \\\\
            \\hat{v} = Attn(\\hat{s}, V) \\\\
            \\hat{q} = Attn(Q, \\hat{v})
        \\end{array}
    """

    def __init__(self, num_hidden, img_feat_size, ques_feat_size, dropout=0.5):
        super(AlterCoAttnNew, self).__init__()
        self.num_hidden = num_hidden
        self.img_feat_size = img_feat_size
        self.ques_feat_size = ques_feat_size
        self.attn1 = Attn(num_hidden, ques_feat_size, guidance_size=0,
            dropout=dropout)
        self.attn2 = Attn(num_hidden, img_feat_size, ques_feat_size,
            dropout=dropout)
        self.attn3 = Attn(num_hidden, ques_feat_size, img_feat_size,
            dropout=dropout)

    def forward(self, input_0, input_1):
        primals_1 = self.attn1.W_x.weight
        primals_3 = self.attn1.W_x.bias
        primals_4 = self.attn1.W_hx.weight
        primals_5 = self.attn1.W_hx.bias
        primals_2 = self.attn2.W_x.weight
        primals_8 = self.attn2.W_x.bias
        primals_7 = self.attn2.W_g.weight
        primals_10 = self.attn2.W_g.bias
        primals_11 = self.attn2.W_hx.weight
        primals_12 = self.attn2.W_hx.bias
        primals_9 = self.attn3.W_x.weight
        primals_14 = self.attn3.W_x.bias
        primals_13 = self.attn3.W_g.weight
        primals_16 = self.attn3.W_g.bias
        primals_17 = self.attn3.W_hx.weight
        primals_18 = self.attn3.W_hx.bias
        primals_15 = input_0
        primals_6 = input_1
        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])
        return output[0], output[1]


Measured latency_ms: 0.071043