import math
import torch
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import LeakyReLU


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 ConvReLU2(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_0 = PadSameConv2d(kernel_size=(kernel_size, 1), stride=(
            stride, 1))
        self.conv_y = Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(kernel_size, 1), stride=(stride, 1))
        self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope)
        self.pad_1 = PadSameConv2d(kernel_size=(1, kernel_size), stride=(1,
            stride))
        self.conv_x = Conv2d(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, kernel_size), stride=(1, stride))

    def forward(self, x: 'torch.Tensor'):
        t = self.pad_0(x)
        t = self.conv_y(t)
        t = self.leaky_relu(t)
        t = self.pad_1(t)
        t = self.conv_x(t)
        return self.leaky_relu(t)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]

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 = 448
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 7
    x2 = xindex // 28
    x3 = xindex % 28
    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 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    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
    tl.store(out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_leaky_relu_2(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 448
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x4 = xindex // 7
    x2 = xindex // 28 % 4
    x5 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x4), tmp5 & xmask, other=0.0).to(tl
        .int1)
    tmp7 = tl.load(in_ptr1 + (-1 + x0 + 4 * x4), tmp5 & xmask, other=0.0)
    tmp8 = tl.load(in_ptr2 + x2, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 0.1
    tmp11 = tmp9 * tmp10
    tmp12 = tl.where(tmp6, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp5, tmp12, tmp13)
    tl.store(out_ptr0 + x5, tmp14, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(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, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 4), (16, 4, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 4), (112, 28, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(448)](primals_1, buf0, 448,
            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)
        triton_poi_fused_convolution_leaky_relu_1[grid(256)](buf1,
            primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 7), (112, 28, 7, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_leaky_relu_2[grid(448)](
            buf2, buf1, primals_3, buf3, 448, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf6 = buf1
        del buf1
        triton_poi_fused_convolution_leaky_relu_3[grid(256)](buf4,
            primals_5, buf5, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_5
    return buf6, primals_2, primals_4, buf0, buf2, buf3, buf5


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 ConvReLU2New(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_0 = PadSameConv2d(kernel_size=(kernel_size, 1), stride=(
            stride, 1))
        self.conv_y = Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(kernel_size, 1), stride=(stride, 1))
        self.leaky_relu = LeakyReLU(negative_slope=leaky_relu_neg_slope)
        self.pad_1 = PadSameConv2d(kernel_size=(1, kernel_size), stride=(1,
            stride))
        self.conv_x = Conv2d(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, kernel_size), stride=(1, stride))

    def forward(self, input_0):
        primals_2 = self.conv_y.weight
        primals_3 = self.conv_y.bias
        primals_4 = self.conv_x.weight
        primals_5 = self.conv_x.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
