import torch
import torch.nn as nn


class Attention(nn.Module):

    def __init__(self, num_channels, embed_size, dropout=True):
        """Stacked attention Module
        """
        super(Attention, self).__init__()
        self.ff_image = nn.Linear(embed_size, num_channels)
        self.ff_questions = nn.Linear(embed_size, num_channels)
        self.dropout = nn.Dropout(p=0.5)
        self.ff_attention = nn.Linear(num_channels, 1)

    def forward(self, vi, vq):
        """Extract feature vector from image vector.
        """
        hi = self.ff_image(vi)
        hq = self.ff_questions(vq).unsqueeze(dim=1)
        ha = torch.tanh(hi + hq)
        if self.dropout:
            ha = self.dropout(ha)
        ha = self.ff_attention(ha)
        pi = torch.softmax(ha, dim=1)
        self.pi = pi
        vi_attended = (pi * vi).sum(dim=1)
        u = vi_attended + vq
        return u


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


def get_init_inputs():
    return [[], {'num_channels': 4, 'embed_size': 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 torch.nn as 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_add_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 256
    x0 = xindex % 4
    x3 = xindex // 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(out_ptr0 + x6, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * 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)


@triton.jit
def triton_poi_fused_add_mul_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x2 = xindex // 64
    x3 = xindex % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + x4, xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp16 = tmp14 + tmp15
    tl.store(out_ptr0 + x4, tmp16, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(1024)](buf0, primals_2, buf1,
            primals_5, buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        del primals_5
        buf4 = reinterpret_tensor(buf1, (256, 1), (1, 1), 0)
        del buf1
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = reinterpret_tensor(buf0, (4, 4, 4, 4, 1), (64, 16, 4, 1, 256), 0
            )
        del buf0
        triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_add_mul_sum_3[grid(256)](buf6, primals_3,
            primals_6, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf7, buf6, primals_3, reinterpret_tensor(primals_6, (64, 4), (4,
        1), 0), buf2, buf6, primals_7


class AttentionNew(nn.Module):

    def __init__(self, num_channels, embed_size, dropout=True):
        """Stacked attention Module
        """
        super(AttentionNew, self).__init__()
        self.ff_image = nn.Linear(embed_size, num_channels)
        self.ff_questions = nn.Linear(embed_size, num_channels)
        self.dropout = nn.Dropout(p=0.5)
        self.ff_attention = nn.Linear(num_channels, 1)

    def forward(self, input_0, input_1):
        primals_1 = self.ff_image.weight
        primals_2 = self.ff_image.bias
        primals_4 = self.ff_questions.weight
        primals_5 = self.ff_questions.bias
        primals_7 = self.ff_attention.weight
        primals_8 = self.ff_attention.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
