import torch
from torch._inductor.select_algorithm import extern_kernels
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 math
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import LeakyReLU
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 = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 7 % 7
    x0 = xindex % 7
    x2 = xindex // 49
    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 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 7), (196, 49, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(784)](primals_1, buf0, 784,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_1[grid(256)](buf1,
            primals_3, buf2, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return buf3, primals_2, buf0, buf2


class PadSameConv2d(torch.nn.Module):

    def __init__(self, kernel_size, stride=1):
        """
        Imitates padding_mode="same" from tensorflow.
        :param kernel_size: Kernelsize of the convolution, int or tuple/list
        :param stride: Stride of the convolution, int or tuple/list
        """
        super().__init__()
        if isinstance(kernel_size, (tuple, list)):
            self.kernel_size_y = kernel_size[0]
            self.kernel_size_x = kernel_size[1]
        else:
            self.kernel_size_y = kernel_size
            self.kernel_size_x = kernel_size
        if isinstance(stride, (tuple, list)):
            self.stride_y = stride[0]
            self.stride_x = stride[1]
        else:
            self.stride_y = stride
            self.stride_x = stride

    def forward(self, x: 'torch.Tensor'):
        _, _, height, width = x.shape
        padding_y = (self.stride_y * (math.ceil(height / self.stride_y) - 1
            ) + self.kernel_size_y - height) / 2
        padding_x = (self.stride_x * (math.ceil(width / self.stride_x) - 1) +
            self.kernel_size_x - width) / 2
        padding = [math.floor(padding_x), math.ceil(padding_x), math.floor(
            padding_y), math.ceil(padding_y)]
        return F.pad(input=x, pad=padding)


class ConvReLUNew(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        leaky_relu_neg_slope=0.1):
        """
        Performs two convolutions and a leaky relu. The first operation only convolves in y direction, the second one
        only in x direction.
        :param in_channels: Number of input channels
        :param out_channels: Number of output channels
        :param kernel_size: Kernel size for the convolutions, first in y direction, then in x direction
        :param stride: Stride for the convolutions, first in y direction, then in x direction
        """
        super().__init__()
        self.pad = PadSameConv2d(kernel_size=kernel_size, stride=stride)
        self.conv = Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride)
        self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_3 = self.conv.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.037275