import torch
import torch.nn as nn
import torch.utils.model_zoo


class CE_loss(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss = nn.CrossEntropyLoss()

    def forward(self, predict, target):
        n, _c, h, w = target.data.shape
        predict = predict.permute(0, 2, 3, 1).contiguous().view(n * h * w, -1)
        target = target.permute(0, 2, 3, 1).contiguous().view(n * h * w, -1)
        return self.loss(predict, torch.max(target, 1)[1])


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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
import torch.utils.model_zoo
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__log_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
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 * x1 + 64 * (x0 // 16) + x0 % 16), xmask)
    tmp1 = tl.load(in_ptr0 + (64 * (x0 // 16) + x0 % 16), xmask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + 64 * (x0 // 16) + x0 % 16), xmask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + 64 * (x0 // 16) + x0 % 16), xmask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + 64 * (x0 // 16) + x0 % 16), 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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_max_nll_loss_forward_1(in_out_ptr0, in_ptr0, in_ptr1,
    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
    tmp0 = tl.load(in_ptr0 + (64 * (r0 // 16) + r0 % 16), None)
    tmp1 = tl.load(in_ptr0 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp17 = tl.load(in_ptr0 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp32 = tl.load(in_ptr0 + (48 + 64 * (r0 // 16) + r0 % 16), None)
    tmp56 = tl.load(in_ptr1 + r0, None)
    tmp58 = tl.load(in_ptr1 + (64 + r0), None)
    tmp61 = tl.load(in_ptr1 + (128 + r0), None)
    tmp64 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1, 1], 0, tl.int64)
    tmp11 = tl.full([1, 1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tmp15 = tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp18 = tmp15 > tmp17
    tmp19 = tmp15 == tmp17
    tmp20 = tmp15 != tmp15
    tmp21 = tmp17 != tmp17
    tmp22 = tmp20 > tmp21
    tmp23 = tmp18 | tmp22
    tmp24 = tmp20 & tmp21
    tmp25 = tmp19 | tmp24
    tmp26 = tl.full([1, 1], 2, tl.int64)
    tmp27 = tmp16 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tmp23 | tmp28
    tmp30 = tl.where(tmp29, tmp15, tmp17)
    tmp31 = tl.where(tmp29, tmp16, tmp26)
    tmp33 = tmp30 > tmp32
    tmp34 = tmp30 == tmp32
    tmp35 = tmp30 != tmp30
    tmp36 = tmp32 != tmp32
    tmp37 = tmp35 > tmp36
    tmp38 = tmp33 | tmp37
    tmp39 = tmp35 & tmp36
    tmp40 = tmp34 | tmp39
    tmp41 = tl.full([1, 1], 3, tl.int64)
    tmp42 = tmp31 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tmp38 | tmp43
    tl.where(tmp44, tmp30, tmp32)
    tmp46 = tl.where(tmp44, tmp31, tmp41)
    tmp47 = tl.full([1, 1], -100, tl.int64)
    tmp48 = tmp46 != tmp47
    tmp49 = tl.where(tmp48, tmp46, tmp10)
    tmp50 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp51 = tmp49 + tmp50
    tmp52 = tmp49 < 0
    tmp53 = tl.where(tmp52, tmp51, tmp49)
    tl.device_assert((0 <= tmp53) & (tmp53 < 4),
        'index out of bounds: 0 <= tmp53 < 4')
    tmp55 = tl.load(in_ptr1 + (r0 + 64 * tmp53), None)
    tmp57 = tl_math.exp(tmp56)
    tmp59 = tl_math.exp(tmp58)
    tmp60 = tmp57 + tmp59
    tmp62 = tl_math.exp(tmp61)
    tmp63 = tmp60 + tmp62
    tmp65 = tl_math.exp(tmp64)
    tmp66 = tmp63 + tmp65
    tmp67 = tl_math.log(tmp66)
    tmp68 = tmp55 - tmp67
    tmp69 = -tmp68
    tmp70 = 0.0
    tmp71 = tl.where(tmp48, tmp69, tmp70)
    tmp72 = tl.broadcast_to(tmp71, [XBLOCK, RBLOCK])
    tmp74 = tl.sum(tmp72, 1)[:, None]
    tmp75 = tmp48.to(tl.int64)
    tmp76 = tl.broadcast_to(tmp75, [XBLOCK, RBLOCK])
    tmp78 = tl.sum(tmp76, 1)[:, None]
    tmp79 = tmp78.to(tl.float32)
    tmp80 = tmp74 / tmp79
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp80, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 4), (1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused_max_nll_loss_forward_1[grid(1)](buf4, arg0_1, buf1,
            1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf1
    return buf4,


class CE_lossNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss = nn.CrossEntropyLoss()

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