import torch
from torch import Tensor
import torch.nn as nn


class ResidualDenseBlock(nn.Module):
    """Achieves densely connected convolutional layers.
    `Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.

    Args:
            channels (int): The number of channels in the input image.
            growths (int): The number of channels that increase in each layer of convolution.
    """

    def __init__(self, channels: 'int', growths: 'int') ->None:
        super(ResidualDenseBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1,
            1), (1, 1))
        self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1,
            1), (1, 1))
        self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1,
            1), (1, 1))
        self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1,
            1), (1, 1))
        self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1,
            1), (1, 1))
        self.leaky_relu = nn.LeakyReLU(0.2, True)
        self.identity = nn.Identity()

    def forward(self, x: 'Tensor') ->Tensor:
        identity = x
        out1 = self.leaky_relu(self.conv1(x))
        out2 = self.leaky_relu(self.conv2(torch.cat([x, out1], 1)))
        out3 = self.leaky_relu(self.conv3(torch.cat([x, out1, out2], 1)))
        out4 = self.leaky_relu(self.conv4(torch.cat([x, out1, out2, out3], 1)))
        out5 = self.identity(self.conv5(torch.cat([x, out1, out2, out3,
            out4], 1)))
        out = out5 * 0.2 + identity
        return out


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


def get_init_inputs():
    return [[], {'channels': 4, 'growths': 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 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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp11 > tmp12
    tmp14 = 0.2
    tmp15 = tmp11 * tmp14
    tmp16 = tl.where(tmp13, tmp11, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp6, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp5, tmp18)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp23 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp20 &
        xmask, other=0.0)
    tmp24 = tl.load(in_ptr4 + (-8 + x1), tmp20 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 > tmp13
    tmp27 = tmp25 * tmp15
    tmp28 = tl.where(tmp26, tmp25, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp20, tmp28, tmp29)
    tmp31 = tl.where(tmp9, tmp19, tmp30)
    tmp32 = tl.where(tmp4, tmp5, tmp31)
    tl.store(out_ptr0 + x3, tmp32, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-8 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tl.full([1], 16, tl.int64)
    tmp35 = tl.load(in_ptr5 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp32 &
        xmask, other=0.0)
    tmp36 = tl.load(in_ptr6 + (-12 + x1), tmp32 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 > tmp13
    tmp39 = tmp37 * tmp15
    tmp40 = tl.where(tmp38, tmp37, tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp32, tmp40, tmp41)
    tmp43 = tl.where(tmp23, tmp31, tmp42)
    tmp44 = tl.where(tmp9, tmp19, tmp43)
    tmp45 = tl.where(tmp4, tmp5, tmp44)
    tl.store(out_ptr0 + x3, tmp45, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 20
    x0 = xindex % 16
    x2 = xindex // 320
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-8 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr5 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp35 &
        xmask, other=0.0)
    tmp37 = tl.load(in_ptr6 + (-12 + x1), tmp35 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tmp38 > tmp13
    tmp40 = tmp38 * tmp15
    tmp41 = tl.where(tmp39, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp35, tmp41, tmp42)
    tmp44 = tmp0 >= tmp33
    tl.full([1], 20, tl.int64)
    tmp47 = tl.load(in_ptr7 + (x0 + 16 * (-16 + x1) + 64 * x2), tmp44 &
        xmask, other=0.0)
    tmp48 = tl.load(in_ptr8 + (-16 + x1), tmp44 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp49 = tmp47 + tmp48
    tmp50 = tmp49 > tmp13
    tmp51 = tmp49 * tmp15
    tmp52 = tl.where(tmp50, tmp49, tmp51)
    tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype)
    tmp54 = tl.where(tmp44, tmp52, tmp53)
    tmp55 = tl.where(tmp35, tmp43, tmp54)
    tmp56 = tl.where(tmp23, tmp31, tmp55)
    tmp57 = tl.where(tmp9, tmp19, tmp56)
    tmp58 = tl.where(tmp4, tmp5, tmp57)
    tl.store(out_ptr0 + x3, tmp58, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_4(in_out_ptr0, in_ptr0, in_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_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.2
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5(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
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 20, 3, 3), (180, 9, 3, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, buf0, primals_3, buf1,
            512, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_1[grid(768)](primals_1, buf0, primals_3, buf2,
            primals_5, buf3, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), 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, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_2[grid(1024)](primals_1, buf0, primals_3, buf2,
            primals_5, buf4, primals_7, buf5, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 20, 4, 4), (320, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_3[grid(1280)](primals_1, buf0, primals_3, buf2,
            primals_5, buf4, primals_7, buf6, primals_9, buf7, 1280, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_add_convolution_mul_4[grid(256)](buf9, primals_11,
            primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf6, primals_9, buf10, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf6
        del primals_9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf4, primals_7, buf11, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf4
        del primals_7
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf2, primals_5, buf12, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf2
        del primals_5
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf0, primals_3, buf13, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf0
        del primals_3
    return (buf9, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf10, buf11, buf12, buf13)


class ResidualDenseBlockNew(nn.Module):
    """Achieves densely connected convolutional layers.
    `Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.

    Args:
            channels (int): The number of channels in the input image.
            growths (int): The number of channels that increase in each layer of convolution.
    """

    def __init__(self, channels: 'int', growths: 'int') ->None:
        super(ResidualDenseBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1,
            1), (1, 1))
        self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1,
            1), (1, 1))
        self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1,
            1), (1, 1))
        self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1,
            1), (1, 1))
        self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1,
            1), (1, 1))
        self.leaky_relu = nn.LeakyReLU(0.2, True)
        self.identity = nn.Identity()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
