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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x4 = xindex // 8192
    x5 = xindex % 2048
    x0 = xindex % 128
    x6 = xindex % 512
    x7 = xindex // 2048
    x8 = xindex
    tmp0 = tl.load(in_ptr0 + (x5 + 2048 * x4), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x6 + 512 * x7), None, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(out_ptr0 + x8, tmp7, None)


@triton.jit
def triton_poi_fused_max_1(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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp32 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tmp15 = tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp18 = tmp15 > tmp17
    tmp19 = tmp15 == tmp17
    tmp20 = tmp15 != tmp15
    tmp21 = tmp17 != tmp17
    tmp22 = tmp20 > tmp21
    tmp23 = tmp18 | tmp22
    tmp24 = tmp20 & tmp21
    tmp25 = tmp19 | tmp24
    tmp26 = tl.full([1], 2, tl.int64)
    tmp27 = tmp16 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tmp23 | tmp28
    tmp30 = tl.where(tmp29, tmp15, tmp17)
    tmp31 = tl.where(tmp29, tmp16, tmp26)
    tmp33 = tmp30 > tmp32
    tmp34 = tmp30 == tmp32
    tmp35 = tmp30 != tmp30
    tmp36 = tmp32 != tmp32
    tmp37 = tmp35 > tmp36
    tmp38 = tmp33 | tmp37
    tmp39 = tmp35 & tmp36
    tmp40 = tmp34 | tmp39
    tmp41 = tl.full([1], 3, tl.int64)
    tmp42 = tmp31 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tmp38 | tmp43
    tl.where(tmp44, tmp30, tmp32)
    tmp46 = tl.where(tmp44, tmp31, tmp41)
    tmp47 = triton_helpers.maximum(tmp0, tmp1)
    tmp48 = triton_helpers.maximum(tmp47, tmp17)
    tmp49 = triton_helpers.maximum(tmp48, tmp32)
    tl.store(out_ptr0 + x2, tmp46, xmask)
    tl.store(out_ptr1 + x2, tmp49, 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 = 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_3(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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x4, tmp8, 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, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (128, 4), (4, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 128), (128, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 128), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4, 128), (8192, 2048, 512, 128,
            1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(32768)](buf0, primals_2, buf1,
            primals_5, buf2, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_2
        del primals_5
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 128),
            (128, 1), 0), reinterpret_tensor(primals_7, (128, 1), (1, 128),
            0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.int64)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_max_1[grid(64)](buf4, buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 4, 16, 1, 256),
            torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf4, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf4, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf4
        triton_poi_fused__softmax_3[grid(256)](buf7, buf8, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf7
    return buf8, reinterpret_tensor(buf6, (4, 1, 4, 4, 1), (16, 16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), buf2, buf8, reinterpret_tensor(buf5, (4, 4, 1, 4, 1), (16, 4, 4,
        1, 1), 0), primals_7


class AttBahdanauNew(torch.nn.Module):
    """
    AttBahdanau: Attention according to Bahdanau that can be used by the 
    Alignment module.
    """

    def __init__(self, q_dim, y_dim, att_dim=128):
        super().__init__()
        self.q_dim = q_dim
        self.y_dim = y_dim
        self.att_dim = att_dim
        self.Wq = nn.Linear(self.q_dim, self.att_dim)
        self.Wy = nn.Linear(self.y_dim, self.att_dim)
        self.v = nn.Linear(self.att_dim, 1)

    def forward(self, input_0, input_1):
        primals_1 = self.Wq.weight
        primals_2 = self.Wq.bias
        primals_4 = self.Wy.weight
        primals_5 = self.Wy.bias
        primals_7 = self.v.weight
        primals_8 = self.v.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], output[1]


Measured latency_ms: 0.052309