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
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_max_sub_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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_exp_log_mean_mul_sub_sum_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 + 4 * r0, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp11 - tmp0
    tmp14 = tmp12 * tmp13
    tmp15 = tmp11 - tmp2
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tmp11 - tmp5
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = tmp11 - tmp8
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 64.0
    tmp31 = tmp29 / tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp31, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_exp_log_mean_mul_sub_sum_1[grid(1)](buf2, buf0,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def soft_cross_entropy(logit, label, weight=None, reduce=None, reduction='mean'
    ):
    if weight is not None and weight.requires_grad:
        raise RuntimeError('gradient for weight is not supported')
    losses = SoftCrossEntropyFunction.apply(logit, label, weight)
    reduction = {(True): 'mean', (False): 'none', None: reduction}[reduce]
    if reduction == 'mean':
        return losses.mean()
    elif reduction == 'sum':
        return losses.sum()
    elif reduction == 'none':
        return losses
    else:
        raise ValueError('invalid value for reduction: {}'.format(reduction))


class SoftCrossEntropyFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, logit, label, weight=None):
        assert logit.size() == label.size(), 'logit.size() != label.size()'
        dim = logit.dim()
        max_logit = logit.max(dim - 1, keepdim=True)[0]
        logit = logit - max_logit
        exp_logit = logit.exp()
        exp_sum = exp_logit.sum(dim - 1, keepdim=True)
        prob = exp_logit / exp_sum
        log_exp_sum = exp_sum.log()
        neg_log_prob = log_exp_sum - logit
        if weight is None:
            weighted_label = label
        else:
            if weight.size() != (logit.size(-1),):
                raise ValueError(
                    'since logit.size() = {}, weight.size() should be ({},), but got {}'
                    .format(logit.size(), logit.size(-1), weight.size()))
            size = [1] * label.dim()
            size[-1] = label.size(-1)
            weighted_label = label * weight.view(size)
        ctx.save_for_backward(weighted_label, prob)
        out = (neg_log_prob * weighted_label).sum(dim - 1)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        weighted_label, prob = ctx.saved_tensors
        old_size = weighted_label.size()
        K = old_size[-1]
        B = weighted_label.numel() // K
        grad_output = grad_output.view(B, 1)
        weighted_label = weighted_label.view(B, K)
        prob = prob.view(B, K)
        grad_input = grad_output * (prob * weighted_label.sum(1, True) -
            weighted_label)
        grad_input = grad_input.view(old_size)
        return grad_input, None, None


class SoftCrossEntropyLossNew(torch.nn.Module):

    def __init__(self, weight=None, reduce=None, reduction='mean'):
        super(SoftCrossEntropyLossNew, self).__init__()
        self.weight = weight
        self.reduce = reduce
        self.reduction = reduction

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


Measured latency_ms: 0.027714