import torch
import torch.nn as nn


class cSE(nn.Module):

    def __init__(self, in_channels):
        super().__init__()
        reduced_filters = 1 if in_channels // 2 == 0 else in_channels // 2
        self.global_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.pointwise_1 = nn.Conv2d(in_channels=in_channels, out_channels=
            reduced_filters, kernel_size=1)
        self.pointwise_2 = nn.Conv2d(in_channels=reduced_filters,
            out_channels=in_channels, kernel_size=1)
        self.sigmoid = nn.Sigmoid()
        self.relu = nn.ReLU6()

    def forward(self, input_tensor):
        x = self.global_avg_pool(input_tensor)
        x = self.pointwise_1(x)
        x = self.relu(x)
        x = self.pointwise_2(x)
        x = self.sigmoid(x)
        x = torch.mul(input_tensor, x)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 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
from torch._inductor.runtime import triton_helpers
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_hardtanh_hardtanh_backward_1(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, 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 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_3(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
    x2 = xindex
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, 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, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 1, 1), (2, 1, 1, 1))
        buf3 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.bool)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_1[grid(8)](buf2
            , primals_3, buf3, buf7, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del buf2
        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, 1, 1), (4, 1, 1, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(16)](buf5, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_3[grid(256)](primals_1, buf5, buf6, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf6, primals_1, primals_2, primals_4, buf1, buf3, buf5, buf7


class cSENew(nn.Module):

    def __init__(self, in_channels):
        super().__init__()
        reduced_filters = 1 if in_channels // 2 == 0 else in_channels // 2
        self.global_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.pointwise_1 = nn.Conv2d(in_channels=in_channels, out_channels=
            reduced_filters, kernel_size=1)
        self.pointwise_2 = nn.Conv2d(in_channels=reduced_filters,
            out_channels=in_channels, kernel_size=1)
        self.sigmoid = nn.Sigmoid()
        self.relu = nn.ReLU6()

    def forward(self, input_0):
        primals_2 = self.pointwise_1.weight
        primals_3 = self.pointwise_1.bias
        primals_4 = self.pointwise_2.weight
        primals_5 = self.pointwise_2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
