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
from torch.nn import Module
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_per_fused_max_min_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(triton_helpers.min2(tmp1, 0))
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp1, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp5, None)
    tl.store(out_ptr2 + tl.full([1], 0, tl.int32), tmp3, None)


@triton.jit
def triton_per_fused_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, 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.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.load(in_ptr3 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp7 = tl.load(in_ptr4 + 0)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp11 = tl.load(in_ptr5 + (r1 + 16 * x0), xmask, other=0.0)
    tmp4 = tmp1 - tmp3
    tmp9 = tmp6 - tmp8
    tmp10 = tmp4 / tmp9
    tmp12 = 1e-07
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp10 * tmp14
    tmp16 = 1.0
    tmp17 = tmp16 - tmp10
    tmp18 = tmp16 - tmp11
    tmp19 = triton_helpers.maximum(tmp18, tmp12)
    tmp20 = tl_math.log(tmp19)
    tmp21 = tmp17 * tmp20
    tmp22 = tmp15 + tmp21
    tmp23 = tmp0 * tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.where(xmask, tmp24, 0)
    tmp27 = tl.sum(tmp26, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp27, xmask)


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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_max_min_0[grid(1)](arg0_1, buf0, buf1, buf2, 1, 
            256, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_sum_1[grid(16)](arg2_1, arg0_1, buf0, buf1, buf2,
            arg1_1, buf3, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del buf0
        del buf1
        del buf2
    return reinterpret_tensor(buf3, (16,), (1,), 0),


class BinaryCrossEntropyLossNew(Module):

    def __init__(self):
        super().__init__()

    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: 0.014352