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 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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 2, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + x0, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + x0, tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp11, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp7, tmp15, tmp16)
    tmp18 = tmp0 >= tmp5
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr2 + x0, tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tl.where(tmp6, tmp17, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp27 = tl.load(in_ptr3 + x0, tmp24 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp28 = tl.where(tmp4, tmp23, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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_2(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)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (4, 1), 0),
            primals_1, out=buf0)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (4, 1), 16),
            primals_1, out=buf1)
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (4, 1), 32),
            primals_1, out=buf2)
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (4, 1), 48),
            primals_1, out=buf3)
        del primals_1
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(16)](buf0, buf1, buf2, buf3, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf4, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        triton_poi_fused__softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 1, 4), (4, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 4), (4, 4, 1), 0
            ), primals_2, out=buf7)
    return buf7, buf6, buf6, reinterpret_tensor(primals_2, (4, 4, 4), (16, 
        1, 4), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 48
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 32
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 16
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


def batch_product(iput, mat2):
    result = None
    for i in range(iput.size()[0]):
        op = torch.mm(iput[i], mat2)
        op = op.unsqueeze(0)
        if result is None:
            result = op
        else:
            result = torch.cat((result, op), 0)
    return result.squeeze(2)


class rec_attentionNew(nn.Module):

    def __init__(self, hm, args):
        super(rec_attentionNew, self).__init__()
        self.num_directions = 2 if args.bidirectional else 1
        if hm is False:
            self.bin_rep_size = args.bin_rnn_size * self.num_directions
        else:
            self.bin_rep_size = args.bin_rnn_size
        self.bin_context_vector = nn.Parameter(torch.Tensor(self.
            bin_rep_size, 1), requires_grad=True)
        self.softmax = nn.Softmax(dim=1)
        self.bin_context_vector.data.uniform_(-0.1, 0.1)

    def forward(self, input_0):
        primals_1 = self.bin_context_vector
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]


Measured latency_ms: 0.014287