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
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__softmax_0(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_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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_binary_cross_entropy_clamp_log_mean_mul_neg_sub_sum_2(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr0 + (4 + r0), None)
    tmp3 = tl.load(in_ptr0 + (8 + r0), None)
    tmp5 = tl.load(in_ptr0 + (12 + r0), None)
    tmp16 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = 1e-08
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp10 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = -tmp16
    tmp18 = libdevice.log1p(tmp17)
    tmp19 = -100.0
    tmp20 = triton_helpers.maximum(tmp18, tmp19)
    tmp21 = 0.0
    tmp22 = tmp21 * tmp20
    tmp23 = tl_math.log(tmp16)
    tmp24 = triton_helpers.maximum(tmp23, tmp19)
    tmp25 = tmp22 - tmp24
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp28 = tl.sum(tmp26, 1)[:, None]
    tmp29 = tmp28 / tmp7
    tmp30 = -tmp15
    tmp31 = 2.0
    tmp32 = tmp30 * tmp31
    tmp33 = tmp29 - tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp29, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 16), 0)
        del buf0
        triton_poi_fused__softmax_0[grid(16)](arg1_1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf4 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 1, 4), (4, 4, 1), 0
            ), buf3, out=buf4)
        del buf3
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_binary_cross_entropy_clamp_log_mean_mul_neg_sub_sum_2[
            grid(1)](buf6, buf8, buf1, buf4, buf9, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf1
        del buf4
    return buf9, buf6, buf8


def entropy(x, input_as_probabilities):
    """ 
    Helper function to compute the entropy over the batch 
    input: batch w/ shape [b, num_classes]
    output: entropy value [is ideally -log(num_classes)]
    """
    if input_as_probabilities:
        x_ = torch.clamp(x, min=1e-08)
        b = x_ * torch.log(x_)
    else:
        b = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
    if len(b.size()) == 2:
        return -b.sum(dim=1).mean()
    elif len(b.size()) == 1:
        return -b.sum()
    else:
        raise ValueError('Input tensor is %d-Dimensional' % len(b.size()))


class SCANLossNew(nn.Module):

    def __init__(self, entropy_weight=2.0):
        super(SCANLossNew, self).__init__()
        self.softmax = nn.Softmax(dim=1)
        self.bce = nn.BCELoss()
        self.entropy_weight = entropy_weight

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1], output[2]


Measured latency_ms: 0.075921