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
import 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_poi_fused_max_pool2d_with_indices_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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp8 & tmp13
    tmp16 = tmp15 & tmp14
    tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=0.0)
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp8 & tmp20
    tmp23 = tmp22 & tmp21
    tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=0.0)
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp8 & tmp27
    tmp30 = tmp29 & tmp28
    tmp31 = tl.load(in_ptr0 + (-2 + x4), tmp30 & xmask, other=0.0)
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = x1
    tmp34 = tmp33 >= tmp1
    tmp35 = tmp33 < tmp3
    tmp36 = tmp34 & tmp35
    tmp37 = tmp36 & tmp6
    tmp38 = tmp37 & tmp7
    tmp39 = tl.load(in_ptr0 + (-1 + x4), tmp38 & xmask, other=0.0)
    tmp40 = triton_helpers.maximum(tmp39, tmp32)
    tmp41 = tmp36 & tmp13
    tmp42 = tmp41 & tmp14
    tmp43 = tl.load(in_ptr0 + x4, tmp42 & xmask, other=0.0)
    tmp44 = triton_helpers.maximum(tmp43, tmp40)
    tmp45 = tmp36 & tmp20
    tmp46 = tmp45 & tmp21
    tmp47 = tl.load(in_ptr0 + (1 + x4), tmp46 & xmask, other=0.0)
    tmp48 = triton_helpers.maximum(tmp47, tmp44)
    tmp49 = tmp36 & tmp27
    tmp50 = tmp49 & tmp28
    tmp51 = tl.load(in_ptr0 + (2 + x4), tmp50 & xmask, other=0.0)
    tmp52 = triton_helpers.maximum(tmp51, tmp48)
    tmp53 = 1 + x1
    tmp54 = tmp53 >= tmp1
    tmp55 = tmp53 < tmp3
    tmp56 = tmp54 & tmp55
    tmp57 = tmp56 & tmp6
    tmp58 = tmp57 & tmp7
    tmp59 = tl.load(in_ptr0 + (3 + x4), tmp58 & xmask, other=0.0)
    tmp60 = triton_helpers.maximum(tmp59, tmp52)
    tmp61 = tmp56 & tmp13
    tmp62 = tmp61 & tmp14
    tmp63 = tl.load(in_ptr0 + (4 + x4), tmp62 & xmask, other=0.0)
    tmp64 = triton_helpers.maximum(tmp63, tmp60)
    tmp65 = tmp56 & tmp20
    tmp66 = tmp65 & tmp21
    tmp67 = tl.load(in_ptr0 + (5 + x4), tmp66 & xmask, other=0.0)
    tmp68 = triton_helpers.maximum(tmp67, tmp64)
    tmp69 = tmp56 & tmp27
    tmp70 = tmp69 & tmp28
    tmp71 = tl.load(in_ptr0 + (6 + x4), tmp70 & xmask, other=0.0)
    tmp72 = triton_helpers.maximum(tmp71, tmp68)
    tmp73 = 2 + x1
    tmp74 = tmp73 >= tmp1
    tmp75 = tmp73 < tmp3
    tmp76 = tmp74 & tmp75
    tmp77 = tmp76 & tmp6
    tmp78 = tmp77 & tmp7
    tmp79 = tl.load(in_ptr0 + (7 + x4), tmp78 & xmask, other=0.0)
    tmp80 = triton_helpers.maximum(tmp79, tmp72)
    tmp81 = tmp76 & tmp13
    tmp82 = tmp81 & tmp14
    tmp83 = tl.load(in_ptr0 + (8 + x4), tmp82 & xmask, other=0.0)
    tmp84 = triton_helpers.maximum(tmp83, tmp80)
    tmp85 = tmp76 & tmp20
    tmp86 = tmp85 & tmp21
    tmp87 = tl.load(in_ptr0 + (9 + x4), tmp86 & xmask, other=0.0)
    tmp88 = triton_helpers.maximum(tmp87, tmp84)
    tmp89 = tmp76 & tmp27
    tmp90 = tmp89 & tmp28
    tmp91 = tl.load(in_ptr0 + (10 + x4), tmp90 & xmask, other=0.0)
    tmp92 = triton_helpers.maximum(tmp91, tmp88)
    tl.store(out_ptr0 + x4, tmp92, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_pool2d_with_indices_0[grid(256)](arg0_1, buf0,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def get_same_padding(in_size, kernel_size, stride):
    """'Same 'same' operation with tensorflow
    notice：padding=(0, 1, 0, 1) and padding=(1, 1, 1, 1) are different

    padding=(1, 1, 1, 1):
        out(H, W) = (in + [2 * padding] − kernel_size) // stride + 1

    'same' padding=(0, 1, 0, 1):
        out(H, W) = (in + [2 * padding] − kernel_size) / stride + 1

    :param in_size: Union[int, tuple(in_h, in_w)]
    :param kernel_size: Union[int, tuple(kernel_h, kernel_w)]
    :param stride: Union[int, tuple(stride_h, stride_w)]
    :return: padding: tuple(left, right, top, bottom)
    """
    in_h, in_w = (in_size, in_size) if isinstance(in_size, int) else in_size
    kernel_h, kernel_w = (kernel_size, kernel_size) if isinstance(kernel_size,
        int) else kernel_size
    stride_h, stride_w = (stride, stride) if isinstance(stride, int
        ) else stride
    out_h, out_w = math.ceil(in_h / stride_h), math.ceil(in_w / stride_w)
    pad_h = max((out_h - 1) * stride_h + kernel_h - in_h, 0)
    pad_w = max((out_w - 1) * stride_w + kernel_w - in_w, 0)
    return pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2


class MaxPool2dSamePaddingNew(nn.MaxPool2d):
    """MaxPool2dDynamicSamePadding

    由于输入大小都是128的倍数，所以动态池化和静态池化的结果是一致的。此处用动态池化代替静态池化，因为实现方便。

    Since the input size is a multiple of 128,
    the results of dynamic maxpool and static maxpool are consistent.
    Here, dynamic maxpool is used instead of static maxpool,
    because it is convenient to implement"""

    def __init__(self, kernel_size, stride):
        self.kernel_size = kernel_size
        self.stride = stride
        super(MaxPool2dSamePaddingNew, self).__init__(kernel_size, stride)

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


Measured latency_ms: 0.014097