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_poi_fused_sum_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tmp6 + tmp13
    tmp17 = tmp15 + tmp16
    tmp19 = tmp17 + tmp18
    tmp21 = tmp19 + tmp20
    tmp22 = tmp14 + tmp21
    tmp25 = tmp23 + tmp24
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tmp22 + tmp29
    tl.store(out_ptr0 + x0, tmp30, xmask)


@triton.jit
def triton_poi_fused_div_index_put_lift_fresh_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 / tmp1
    tmp3 = tmp2 != tmp2
    tmp4 = 0.0
    tmp5 = tl.where(tmp3, tmp4, tmp2)
    tl.store(out_ptr0 + x2, tmp5, 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_per_fused__softmax_add_div_log_max_min_mul_sub_3(in_ptr0,
    in_ptr1, in_ptr2, out_ptr4, 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 % 16
    r2 = rindex // 64
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr1 + r3, None)
    tmp21 = tl.load(in_ptr2 + r3, None)
    tmp22 = tl.load(in_ptr2 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr2 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr2 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr2 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp11 = triton_helpers.promote_to_tensor(triton_helpers.min2(tmp9, 0))
    tmp13 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp9, 0))
    tmp14 = tmp8 - tmp11
    tmp15 = 99.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 - tmp11
    tmp18 = tmp16 / tmp17
    tmp19 = 1.0
    tmp20 = tmp18 + tmp19
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = tmp21 / tmp28
    tmp30 = tl_math.log(tmp29)
    tmp31 = tmp20 * tmp30
    tl.store(out_ptr4 + tl.broadcast_to(r3, [RBLOCK]), tmp31, None)


@triton.jit
def triton_per_fused_mean_neg_sum_4(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp9 / tmp10
    tmp12 = -tmp11
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp12, 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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sum_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_index_put_lift_fresh_1[grid(256)](arg0_1, buf0,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](arg2_1, buf6, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg2_1
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__softmax_add_div_log_max_min_mul_sub_3[grid(1)](buf1,
            arg1_1, buf6, buf7, 1, 256, num_warps=2, num_stages=1)
        del arg1_1
        del buf1
        del buf6
        buf8 = empty_strided_cuda((), (), torch.float32)
        buf9 = buf8
        del buf8
        triton_per_fused_mean_neg_sum_4[grid(1)](buf9, buf7, 1, 64, XBLOCK=
            1, num_warps=2, num_stages=1)
        del buf7
    return buf9,


class WCELossNew(nn.Module):

    def __init__(self):
        super(WCELossNew, self).__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: 0.015008