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.triton_helpers import libdevice, math as tl_math
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


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_cumsum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tmp0.to(tl.float32)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3, = tl.associative_scan((tmp2,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + 16 * r2 + 64 * x1), tmp3, xmask)


@triton.jit
def triton_per_fused_abs_mean_pow_sqrt_sub_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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp6 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp17 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.abs(tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 4.0
    tmp24 = tmp22 / tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp28 = tl.sum(tmp26, 1)[:, None]
    tmp29 = 64.0
    tmp30 = tmp28 / tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, 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_per_fused_cumsum_0[grid(64)](arg1_1, buf0, 64, 4, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_cumsum_0[grid(64)](arg0_1, buf1, 64, 4, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_abs_mean_pow_sqrt_sub_1[grid(1)](buf3, buf0, buf1,
            1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


class EMDLossNew(nn.Module):
    """EMDLoss class
    """

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

    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: 0.03939