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 F
import torch.optim
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16 % 8
    x1 = xindex // 4 % 4
    x3 = xindex // 128
    x0 = xindex % 4
    x4 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x3 + 16 * x1 + x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x3 + 16 * x0 + (-4 + x2)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_tanh_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, 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
    x4 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    x3 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x4, 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')
    tmp10 = tl.load(in_ptr0 + (x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (4 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (8 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (12 + x0 + 16 * x3), 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)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tmp0 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tl.store(out_ptr0 + x4, tmp9, xmask)
    tl.store(out_ptr1 + x4, tmp18, xmask)


@triton.jit
def triton_poi_fused__softmax_3(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_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = xindex // 8
    x1 = xindex // 8 % 4
    x2 = xindex // 32
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x2 + 4 * (-4 + x0) + 16 * x1), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_5(in_out_ptr0, in_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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_6(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 8, 1, 1), (8, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (4, 8), (8, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_tanh_1[grid(256)](buf2, primals_4, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf3, buf4, buf10, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 4), (4, 1, 
            16), 0), buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_4[grid(128)](primals_2, buf6, buf7, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (16, 4), (4, 1), 0)
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_6, (8, 4), (1, 8), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0)
        del buf8
        triton_poi_fused_tanh_5[grid(64)](buf9, primals_7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
        buf11 = buf5
        del buf5
        triton_poi_fused__softmax_6[grid(64)](buf10, buf11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf12 = buf10
        del buf10
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (4, 1, 
            16), 0), reinterpret_tensor(buf11, (4, 4, 4), (16, 1, 4), 0),
            out=buf12)
        buf13 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_4[grid(128)](primals_1, buf12, buf13, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
        del buf14
        triton_poi_fused_tanh_5[grid(64)](buf15, primals_9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_9
    return (buf15, buf9, primals_3, primals_5, buf0, buf2, buf3,
        reinterpret_tensor(buf7, (16, 8), (8, 1), 0), buf9, buf11,
        reinterpret_tensor(buf13, (16, 8), (8, 1), 0), buf15, primals_8,
        reinterpret_tensor(primals_2, (4, 4, 4), (4, 16, 1), 0), primals_6,
        reinterpret_tensor(primals_1, (4, 4, 4), (4, 16, 1), 0))


def get_activation_fn(name):
    """Returns a callable activation function from torch."""
    if name in (None, 'linear'):
        return lambda x: x
    elif name in ('sigmoid', 'tanh'):
        return getattr(torch, name)
    else:
        return getattr(F, name)


class CoAttentionNew(nn.Module):
    """Co-attention between two sequences.

    Uses one hidden layer to compute an affinity matrix between two sequences.
    This can be then normalized in two direction which gives us 1->2 and 2->1
    attentions.

    The co-attention is computed using a single feed-forward layer as in
    Bahdanau's attention.
    """

    def __init__(self, ctx_1_dim, ctx_2_dim, bottleneck, att_activ='tanh',
        mlp_bias=False):
        super().__init__()
        self.mlp_hid = nn.Conv2d(ctx_1_dim + ctx_2_dim, bottleneck, 1)
        self.mlp_out = nn.Conv2d(bottleneck, 1, 1, bias=mlp_bias)
        self.activ = get_activation_fn(att_activ)
        self.project_1_to_2 = nn.Linear(ctx_1_dim + ctx_2_dim, bottleneck)
        self.project_2_to_1 = nn.Linear(ctx_1_dim + ctx_2_dim, bottleneck)

    def forward(self, input_0, input_1):
        primals_3 = self.mlp_hid.weight
        primals_4 = self.mlp_hid.bias
        primals_5 = self.mlp_out.weight
        primals_6 = self.project_1_to_2.weight
        primals_7 = self.project_1_to_2.bias
        primals_8 = self.project_2_to_1.weight
        primals_9 = self.project_2_to_1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.171514