import torch
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


@triton.jit
def triton_per_fused_log_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 16 * x0), xmask, other=0.0)
    tmp2 = tl_math.log(tmp1)
    tmp3 = tmp0 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_per_fused_exp_index_put_lift_fresh_mean_mul_neg_reciprocal_1(
    in_out_ptr1, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    r1 = rindex % 16
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp14 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tl.full([1], 1, tl.int32)
    tmp3 = tmp2 / tmp1
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = 10.0
    tmp7 = tmp5 * tmp6
    tmp8 = 7.0
    tmp9 = tmp7 < tmp8
    tmp10 = 0.05000000074505806
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tmp12 = tmp11 > tmp8
    tmp13 = tl.where(tmp12, tmp10, tmp11)
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = 256.0
    tmp20 = tmp18 / tmp19
    tmp21 = -tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp21, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_log_mul_sum_0[grid(16)](arg2_1, arg1_1, buf2, 16, 
            16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del arg2_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_exp_index_put_lift_fresh_mean_mul_neg_reciprocal_1[
            grid(1)](buf4, arg0_1, buf2, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del buf2
    return buf4,


class my_BinaryCrossNew(nn.Module):

    def __init__(self, args):
        super(my_BinaryCrossNew, self).__init__()
        self.args = args

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.015313