import torch
import torch.nn as nn


def conv3x3(in_ch, out_ch, stride=1):
    """3x3 convolution with padding."""
    return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)


class ResidualBlock(nn.Module):
    """Simple residual block with two 3x3 convolutions.

    Args:
        in_ch (int): number of input channels
        out_ch (int): number of output channels
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv1 = conv3x3(in_ch, out_ch)
        self.leaky_relu = nn.LeakyReLU(inplace=True)
        self.conv2 = conv3x3(out_ch, out_ch)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        out = self.leaky_relu(out)
        out = out + identity
        return out


class Enhancement_Block(nn.Module):

    def __init__(self):
        super().__init__()
        self.RB1 = ResidualBlock(32, 32)
        self.RB2 = ResidualBlock(32, 32)
        self.RB3 = ResidualBlock(32, 32)

    def forward(self, x):
        identity = x
        out = self.RB1(x)
        out = self.RB2(out)
        out = self.RB3(out)
        out = out + identity
        return out


def get_inputs():
    return [torch.rand([4, 32, 64, 64])]


def get_init_inputs():
    return [[], {}]

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_convolution_leaky_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp10 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp9, None)
    tl.store(out_ptr1 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, None)
    tmp10 = tl.load(in_ptr3 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp11, None)
    tl.store(out_ptr1 + x3, tmp12, None)


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, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 32, 64, 64), (131072, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (32,), (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, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
            primals_3, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        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, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        buf14 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1[grid
            (524288)](buf2, primals_5, primals_1, buf3, buf14, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        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, 32, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf5,
            primals_7, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        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, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf2
        del buf2
        buf13 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1[grid
            (524288)](buf6, primals_9, buf3, buf7, buf13, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_9
        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, 32, 64, 64), (131072, 4096, 64, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf9,
            primals_11, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf11 = buf6
        del buf6
        buf12 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2[grid
            (524288)](buf10, primals_13, buf7, primals_1, buf11, buf12, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf10
        del primals_13
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, primals_12, buf1, buf3, buf5, buf7, buf9, buf12, buf13,
        buf14)


def conv3x3(in_ch, out_ch, stride=1):
    """3x3 convolution with padding."""
    return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)


class ResidualBlock(nn.Module):
    """Simple residual block with two 3x3 convolutions.

    Args:
        in_ch (int): number of input channels
        out_ch (int): number of output channels
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv1 = conv3x3(in_ch, out_ch)
        self.leaky_relu = nn.LeakyReLU(inplace=True)
        self.conv2 = conv3x3(out_ch, out_ch)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        out = self.leaky_relu(out)
        out = out + identity
        return out


class Enhancement_BlockNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.RB1 = ResidualBlock(32, 32)
        self.RB2 = ResidualBlock(32, 32)
        self.RB3 = ResidualBlock(32, 32)

    def forward(self, input_0):
        primals_2 = self.RB1.conv1.weight
        primals_3 = self.RB1.conv1.bias
        primals_4 = self.RB1.conv2.weight
        primals_5 = self.RB1.conv2.bias
        primals_6 = self.RB2.conv1.weight
        primals_7 = self.RB2.conv1.bias
        primals_8 = self.RB2.conv2.weight
        primals_9 = self.RB2.conv2.bias
        primals_10 = self.RB3.conv1.weight
        primals_11 = self.RB3.conv1.bias
        primals_12 = self.RB3.conv2.weight
        primals_13 = self.RB3.conv2.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, primals_12, primals_13])
        return output[0]
