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
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_per_fused_abs_mean_stack_0(in_ptr0, in_ptr1, out_ptr2, 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 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_1(in_ptr0, in_ptr1, out_ptr2, 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), None)
    tmp1 = tl.load(in_ptr1 + (64 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_2(in_ptr0, in_ptr1, out_ptr2, 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 + (128 + r0), None)
    tmp1 = tl.load(in_ptr1 + (128 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_3(in_ptr0, in_ptr1, out_ptr2, 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 + (192 + r0), None)
    tmp1 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_mean_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    RBLOCK: tl.constexpr = 4
    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 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)


@triton.jit
def triton_per_fused_add_mean_mse_loss_5(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)
    tmp7 = tl.load(in_out_ptr0 + 0)
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = 256.0
    tmp12 = tmp6 / tmp11
    tmp13 = tmp10 + tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, 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)
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        buf8 = reinterpret_tensor(buf12, (1,), (1,), 0)
        get_raw_stream(0)
        triton_per_fused_abs_mean_stack_0[grid(1)](arg0_1, arg1_1, buf8, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf9 = reinterpret_tensor(buf12, (1,), (1,), 1)
        triton_per_fused_abs_mean_stack_1[grid(1)](arg0_1, arg1_1, buf9, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf10 = reinterpret_tensor(buf12, (1,), (1,), 2)
        triton_per_fused_abs_mean_stack_2[grid(1)](arg0_1, arg1_1, buf10, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf11 = reinterpret_tensor(buf12, (1,), (1,), 3)
        triton_per_fused_abs_mean_stack_3[grid(1)](arg0_1, arg1_1, buf11, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf13 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_mean_4[grid(1)](buf12, buf13, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf10
        del buf11
        del buf12
        del buf8
        del buf9
        buf15 = buf13
        del buf13
        triton_per_fused_add_mean_mse_loss_5[grid(1)](buf15, arg0_1, arg1_1,
            1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf15,


class DocUnetLoss_DL_batchNew(nn.Module):
    """
    只使用一个unet的loss 目前使用这个loss训练的比较好
    """

    def __init__(self, r=0.0, reduction='mean'):
        super(DocUnetLoss_DL_batchNew, self).__init__()
        assert reduction in ['mean', 'sum'
            ], " reduction must in ['mean','sum']"
        self.r = r
        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: 0.079268