import torch
from torch import nn
import torch.utils.data
import torch.optim


def str2act(txt):
    """Translates text to neural network activation"""
    return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
        Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
        lower()]


class ConvGLU(nn.Module):
    """
    A convGlu operation, used by the Degli paper's model.
    """

    def __init__(self, in_ch, out_ch, kernel_size=(7, 7), padding=None,
        batchnorm=False, act='sigmoid', stride=None):
        super().__init__()
        if not padding:
            padding = kernel_size[0] // 2, kernel_size[1] // 2
        if stride is None:
            self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding=
                padding)
        else:
            self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding=
                padding, stride=stride)
        self.weight = self.conv.weight
        self.bias = self.conv.bias
        if batchnorm:
            self.conv = nn.Sequential(self.conv, nn.BatchNorm2d(out_ch * 2))
        self.sigmoid = str2act(act)

    def forward(self, x):
        x = self.conv(x)
        ch = x.shape[1]
        x = x[:, :ch // 2, ...] * self.sigmoid(x[:, ch // 2:, ...])
        return x


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


def get_init_inputs():
    return [[], {'in_ch': 4, 'out_ch': 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 import nn
import torch.utils.data
import torch.optim
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(512)](buf1, primals_2, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_1[grid(256)](buf1, buf2, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1


def str2act(txt):
    """Translates text to neural network activation"""
    return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
        Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
        lower()]


class ConvGLUNew(nn.Module):
    """
    A convGlu operation, used by the Degli paper's model.
    """

    def __init__(self, in_ch, out_ch, kernel_size=(7, 7), padding=None,
        batchnorm=False, act='sigmoid', stride=None):
        super().__init__()
        if not padding:
            padding = kernel_size[0] // 2, kernel_size[1] // 2
        if stride is None:
            self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding=
                padding)
        else:
            self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding=
                padding, stride=stride)
        self.weight = self.conv.weight
        self.bias = self.conv.bias
        if batchnorm:
            self.conv = nn.Sequential(self.conv, nn.BatchNorm2d(out_ch * 2))
        self.sigmoid = str2act(act)

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