import torch
from torch._inductor.select_algorithm import extern_kernels
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 as t
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_log_mul_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp4 = 4.0
    tmp5 = tmp0 * tmp4
    tl.store(out_ptr0 + x0, tmp3, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_per_fused_mean_mul_neg_sub_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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    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 = 0.25
    tmp9 = tmp7 * tmp8
    tmp10 = -tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp10, 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, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_log_mul_0[grid(256)](arg0_1, buf0, buf2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1), 0), out=buf1
            )
        del arg1_1
        buf3 = reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(arg2_1, (16, 4, 4), (16, 4, 1), 0), out=buf3
            )
        del arg2_1
        del buf2
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_mean_mul_neg_sub_1[grid(1)](buf5, buf1, buf3, 1, 
            256, num_warps=2, num_stages=1)
        del buf1
        del buf3
    return buf5,


def indicator(K):
    """
    @K:             number of users
    """
    return t.eye(5 * K)


class LossNew(nn.Module):

    def __init__(self, K, Nt, Vartheta):
        super(LossNew, self).__init__()
        self.K = K
        self.Nt = Nt
        self.Delta = indicator(self.K)
        self.alpha = 1 / self.K
        self.Vartheta = Vartheta
        self.batchsize = 10

    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: 0.045488