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.modules.loss import _Loss
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
    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
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_index_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp11 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp12 = tl.full([XBLOCK], 4, tl.int32)
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp11)
    tl.device_assert((0 <= tmp15) & (tmp15 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp15 < 4')
    tmp17 = tl.load(in_ptr1 + (x0 + 16 * tmp15 + 64 * tmp10), xmask)
    tmp18 = tl.load(in_ptr1 + (x0 + 64 * tmp10), xmask)
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tl.load(in_ptr1 + (16 + x0 + 64 * tmp10), xmask)
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.load(in_ptr1 + (32 + x0 + 64 * tmp10), xmask)
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl.load(in_ptr1 + (48 + x0 + 64 * tmp10), xmask)
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tl_math.log(tmp28)
    tmp30 = tmp17 - tmp29
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_per_fused__log_softmax_index_mean_mul_neg_2(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)
    r2 = rindex
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = 256.0
    tmp7 = tmp5 / tmp6
    tmp8 = -tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, 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,), (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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_index_1[grid(64)](arg1_1, buf0, buf1,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__log_softmax_index_mean_mul_neg_2[grid(1)](buf3,
            arg2_1, buf1, 1, 256, num_warps=2, num_stages=1)
        del arg2_1
        del buf1
    return buf3,


class PolicyGradientLossNew(_Loss):

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

    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: 0.039615