import torch
import torch.nn as nn


class FeatNet(nn.Module):

    def __init__(self):
        super(FeatNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            (3, 7), stride=1, padding=(1, 3), bias=False)
        self.tanh1 = nn.Tanh()
        self.Pool1 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =(3, 5), stride=1, padding=(1, 2), bias=False)
        self.tanh2 = nn.Tanh()
        self.Upsample2 = nn.ConvTranspose2d(in_channels=32, out_channels=32,
            kernel_size=4, stride=2, padding=1, groups=32)
        self.Pool2 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =(3, 3), stride=1, padding=(1, 1), bias=False)
        self.tanh3 = nn.Tanh()
        self.Upsample3 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
            kernel_size=8, stride=4, padding=2, bias=False, groups=64)
        self.conv4 = nn.Conv2d(in_channels=112, out_channels=1, kernel_size
            =3, stride=1, padding=1, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.tanh1(x)
        stack1 = x
        x = self.Pool1(x)
        x = self.conv2(x)
        x = self.tanh2(x)
        stack2 = self.Upsample2(x)
        x = self.Pool2(x)
        x = self.conv3(x)
        x = self.tanh3(x)
        stack3 = self.Upsample3(x)
        x = torch.concat((stack1, stack2, stack3), 1)
        output = self.conv4(x)
        return output


def get_inputs():
    return [torch.rand([4, 1, 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.triton_helpers import libdevice
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_tanh_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_avg_pool2d_1(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)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused_tanh_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_avg_pool2d_3(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)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused_tanh_4(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 112
    x0 = xindex % 4096
    x2 = xindex // 458752
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 48, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4096 * (-16 + x1) + 131072 * x2), tmp9,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-16 + x1), tmp9, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 112, tl.int64)
    tmp18 = tl.load(in_ptr3 + (x0 + 4096 * (-48 + x1) + 262144 * x2), tmp15,
        other=0.0)
    tmp19 = tl.where(tmp9, tmp14, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x3, tmp20, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 7), (21, 21, 7, 1))
    assert_size_stride(primals_2, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_3, (32, 16, 3, 5), (240, 15, 5, 1))
    assert_size_stride(primals_4, (32, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_8, (1, 112, 3, 3), (1008, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(262144)](buf1, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf2 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(65536)](buf1, buf2, 65536,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1),
            padding=(1, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_tanh_2[grid(131072)](buf4, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf5, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf6 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_3[grid(32768)](buf4, buf6, 32768,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_tanh_4[grid(65536)](buf8, 65536, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_7, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf9, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf10 = empty_strided_cuda((4, 112, 64, 64), (458752, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_5[grid(1835008)](buf1, buf5, primals_5, buf9,
            buf10, 1835008, XBLOCK=512, num_warps=8, num_stages=1)
        del buf5
        del buf9
        del primals_5
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 64, 64), (4096, 4096, 64, 1))
    return (buf11, primals_1, primals_2, primals_3, primals_4, primals_6,
        primals_7, primals_8, buf1, buf2, buf4, buf6, buf8, buf10)


class FeatNetNew(nn.Module):

    def __init__(self):
        super(FeatNetNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            (3, 7), stride=1, padding=(1, 3), bias=False)
        self.tanh1 = nn.Tanh()
        self.Pool1 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =(3, 5), stride=1, padding=(1, 2), bias=False)
        self.tanh2 = nn.Tanh()
        self.Upsample2 = nn.ConvTranspose2d(in_channels=32, out_channels=32,
            kernel_size=4, stride=2, padding=1, groups=32)
        self.Pool2 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =(3, 3), stride=1, padding=(1, 1), bias=False)
        self.tanh3 = nn.Tanh()
        self.Upsample3 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
            kernel_size=8, stride=4, padding=2, bias=False, groups=64)
        self.conv4 = nn.Conv2d(in_channels=112, out_channels=1, kernel_size
            =3, stride=1, padding=1, bias=False)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.Upsample2.weight
        primals_5 = self.Upsample2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.Upsample3.weight
        primals_8 = self.conv4.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
