import torch
from torch.nn.functional import relu
from torch.nn import Conv2d
from torch.nn import Hardtanh
from torch.nn.functional import hardtanh
import torch.nn.functional


class HardtanhBoundToPOTNet(torch.nn.Module):

    def __init__(self):
        super(HardtanhBoundToPOTNet, self).__init__()
        self.conv1 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh1 = Hardtanh(min_val=0.0, max_val=6.0)
        self.conv2 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv3 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh2 = Hardtanh(min_val=-2.0, max_val=6.0)
        self.conv4 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv5 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv6 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv7 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh3 = Hardtanh(min_val=0.0, max_val=4.0)
        self.conv8 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv9 = Conv2d(3, 3, kernel_size=1, stride=1)

    def forward(self, inp):
        x = self.conv1(inp)
        x = self.hardtanh1(x)
        x = self.conv2(x)
        x = relu(x)
        x = self.conv3(x)
        x = self.hardtanh2(x)
        x = self.conv4(x)
        x = relu(x)
        x = self.conv5(x)
        x = hardtanh(x, min_val=0.0, max_val=6.0)
        x = self.conv6(x)
        x = relu(x)
        x = self.conv7(x)
        x = self.hardtanh3(x)
        x = self.conv8(x)
        x = self.conv9(x)
        x = relu(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 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
from torch._inductor.runtime import triton_helpers
from torch.nn import Conv2d
from torch.nn import Hardtanh
import torch.nn.functional
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_hardtanh_hardtanh_backward_0(in_ptr0,
    in_ptr1, 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 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 <= tmp3
    tmp8 = tmp2 >= tmp5
    tmp9 = tmp7 | tmp8
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(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 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_hardtanh_hardtanh_backward_2(in_ptr0,
    in_ptr1, 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 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -2.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 <= tmp3
    tmp8 = tmp2 >= tmp5
    tmp9 = tmp7 | tmp8
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_hardtanh_hardtanh_backward_3(in_ptr0,
    in_ptr1, 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 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 4.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 <= tmp3
    tmp8 = tmp2 >= tmp5
    tmp9 = tmp7 | tmp8
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_4(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 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_5(in_out_ptr0,
    in_ptr0, out_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 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, 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, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19) = args
    args.clear()
    assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_2, (3,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_5, (3,), (1,))
    assert_size_stride(primals_6, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_7, (3,), (1,))
    assert_size_stride(primals_8, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_9, (3,), (1,))
    assert_size_stride(primals_10, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_11, (3,), (1,))
    assert_size_stride(primals_12, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_13, (3,), (1,))
    assert_size_stride(primals_14, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_15, (3,), (1,))
    assert_size_stride(primals_16, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_17, (3,), (1,))
    assert_size_stride(primals_18, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_19, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        buf22 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_0[grid(49152)](
            buf0, primals_2, buf1, buf22, 49152, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(49152)](buf3, primals_5, 
            49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf5 = buf0
        del buf0
        buf21 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_2[grid(49152)](
            buf4, primals_7, buf5, buf21, 49152, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_1[grid(49152)](buf7, primals_9, 
            49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf9 = buf4
        del buf4
        buf20 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_0[grid(49152)](
            buf8, primals_11, buf9, buf20, 49152, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_1[grid(49152)](buf11, primals_13,
            49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_13
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf13 = buf8
        del buf8
        buf19 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_3[grid(49152)](
            buf12, primals_15, buf13, buf19, 49152, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf12
        del primals_15
        buf14 = extern_kernels.convolution(buf13, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_4[grid(49152)](buf15, primals_17, 
            49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_17
        buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf17 = buf16
        del buf16
        buf18 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_5[grid(49152)](
            buf17, primals_19, buf18, 49152, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_19
    return (buf17, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18, buf1,
        buf3, buf5, buf7, buf9, buf11, buf13, buf15, buf18, buf19, buf20,
        buf21, buf22)


class HardtanhBoundToPOTNetNew(torch.nn.Module):

    def __init__(self):
        super(HardtanhBoundToPOTNetNew, self).__init__()
        self.conv1 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh1 = Hardtanh(min_val=0.0, max_val=6.0)
        self.conv2 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv3 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh2 = Hardtanh(min_val=-2.0, max_val=6.0)
        self.conv4 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv5 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv6 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv7 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.hardtanh3 = Hardtanh(min_val=0.0, max_val=4.0)
        self.conv8 = Conv2d(3, 3, kernel_size=1, stride=1)
        self.conv9 = Conv2d(3, 3, kernel_size=1, stride=1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = 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_12 = self.conv6.weight
        primals_13 = self.conv6.bias
        primals_14 = self.conv7.weight
        primals_15 = self.conv7.bias
        primals_16 = self.conv8.weight
        primals_17 = self.conv8.bias
        primals_18 = self.conv9.weight
        primals_19 = self.conv9.bias
        primals_3 = 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, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19])
        return output[0]
