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
import torch.nn
from torch.nn.modules.module import Module
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__softmax_0(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_log_mean_mul_neg_scatter_1(in_out_ptr0,
    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)
    r1 = rindex // 4 % 4
    r0 = rindex % 4
    r4 = rindex
    r3 = rindex // 64
    r5 = rindex % 16
    tmp0 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + r4, None)
    tmp8 = tl.load(in_ptr1 + (r5 + 64 * r3), None, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr1 + (16 + r5 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (32 + r5 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (48 + r5 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp1 = r0
    tmp2 = tmp0 == tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = -tmp5
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp15 = tmp7 / tmp14
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp6 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = 256.0
    tmp22 = tmp20 / tmp21
    tmp23 = 4.0
    tmp24 = tmp22 * tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp24, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4,), (1,))
    assert_size_stride(arg1_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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg1_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__softmax_log_mean_mul_neg_scatter_1[grid(1)](buf2,
            arg0_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf2,


def _assert_no_grad(variable):
    msg = (
        "nn criterions don't compute the gradient w.r.t. targets - please mark these variables as not requiring gradients"
        )
    assert not variable.requires_grad, msg


class CrossEntropyLossTFNew(Module):

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

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


Measured latency_ms: 0.015636