import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim as optim
import torchvision.transforms.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.utils.checkpoint


class ZeroPad1d(nn.Module):

    def __init__(self, pad_left, pad_right):
        super().__init__()
        self.pad_left = pad_left
        self.pad_right = pad_right

    def forward(self, x):
        return F.pad(x, (self.pad_left, self.pad_right))


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'pad_left': 4, 'pad_right': 4}]

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
import torch.nn as nn
from torch import optim as optim
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.utils.checkpoint
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 12 % 12
    x0 = xindex % 12
    x2 = xindex // 144
    x4 = xindex
    tmp0 = -4 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -4 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-20 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, 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, 12, 12), (576, 144, 12, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(2304)](arg0_1, buf0, 2304,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class ZeroPad1dNew(nn.Module):

    def __init__(self, pad_left, pad_right):
        super().__init__()
        self.pad_left = pad_left
        self.pad_right = pad_right

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