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 itertools import product as product
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_copy_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x2 = xindex // 24
    x3 = xindex % 24
    x4 = xindex
    tmp38 = tl.load(in_ptr1 + x4, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = -4 + x1
    tmp4 = tl.full([1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tmp0 >= tmp4
    tmp8 = tmp0 < tmp1
    tmp9 = tmp7 & tmp8
    tmp10 = tmp9 & tmp6
    tmp11 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp10 & xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + x4, tmp6 & xmask, other=0.0)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tmp3 >= tmp4
    tmp17 = tmp3 < tmp1
    tmp18 = tmp16 & tmp17
    tmp19 = tmp18 & tmp2
    tmp20 = tl.load(in_ptr0 + (-20 + x3 + 16 * x2), tmp19 & xmask, other=0.0)
    tmp21 = tl.load(in_ptr1 + (-16 + x4), tmp2 & xmask, other=0.0)
    tmp22 = tl.where(tmp18, tmp20, tmp21)
    tmp23 = tl.where(tmp5, tmp15, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = 4 + x1
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x3 + 16 * x2), tmp31 & xmask, other=0.0)
    tmp33 = tl.load(in_ptr1 + (16 + x4), tmp26 & xmask, other=0.0)
    tmp34 = tl.where(tmp30, tmp32, tmp33)
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp26, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp9 & xmask, other=0.0)
    tmp39 = tl.where(tmp9, tmp37, tmp38)
    tmp40 = tl.where(tmp26, tmp36, tmp39)
    tmp41 = tl.where(tmp2, tmp25, tmp40)
    tl.store(out_ptr0 + x4, tmp41, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (5 * (5 <= x1) + x1 * (x1 < 5)) + 24 * x2 +
        (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (
        0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + 
        (-1 + x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_copy_0[grid(384)](arg0_1, buf0, buf1, 384, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_replication_pad2d_1[grid(576)](buf1, buf2, 576,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return buf2,


class MixPad2dNew(nn.Module):
    """Mixed padding modes for H and W dimensions

    Args:
        padding (tuple): the size of the padding for x and y, ie (pad_x, pad_y)
        modes (tuple): the padding modes for x and y, the values of each can be
            ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
            Default: ``['replicate', 'circular']``

    """
    __constants__ = ['modes', 'padding']

    def __init__(self, padding=[1, 1], modes=['replicate', 'circular']):
        super(MixPad2dNew, self).__init__()
        assert len(padding) == 2
        self.padding = padding
        self.modes = modes

    def extra_repr(self):
        repr_ = (
            'Mixed Padding: \t x axis: mode: {}, padding: {},\n\t y axis mode: {}, padding: {}'
            .format(self.modes[0], self.padding[0], self.modes[1], self.
            padding[1]))
        return repr_

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


Measured latency_ms: 0.028502