import torch
from torch import nn
import torch.nn.functional as F


class aeEncoder(nn.Module):

    def __init__(self, capacity, channel):
        super(aeEncoder, self).__init__()
        self.c = capacity
        self.channel = channel
        self.conv1 = nn.Conv2d(in_channels=self.channel, out_channels=self.
            c, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(in_channels=self.c, out_channels=self.c * 2,
            kernel_size=4, stride=2, padding=1)
        self.conv3 = nn.Conv2d(in_channels=self.c * 2, out_channels=self.c *
            4, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(in_channels=self.c * 4, out_channels=self.c *
            8, kernel_size=4, stride=2, padding=1)
        self.conv5 = nn.Conv2d(in_channels=self.c * 8, out_channels=self.c *
            16, kernel_size=4, stride=2, padding=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        return x


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


def get_init_inputs():
    return [[], {'capacity': 4, 'channel': 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
from torch import 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_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 // 1024 % 4
    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_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 // 256 % 8
    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_relu_2(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 // 64 % 16
    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_relu_3(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 // 16 % 32
    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_relu_threshold_backward_4(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (16, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (32, 16, 4, 4), (256, 16, 4, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_11, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 32, 32), (4096, 1024, 32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 16, 16), (2048, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(8192)](buf3, primals_5, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 8, 8), (1024, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(4096)](buf5, primals_7, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(2048)](buf7, primals_9, 
            2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 2, 2), (256, 4, 2, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 64, 2, 2), (256, 4, 2, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_4[grid(1024)](buf9
            , primals_11, buf10, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf10)


class aeEncoderNew(nn.Module):

    def __init__(self, capacity, channel):
        super(aeEncoderNew, self).__init__()
        self.c = capacity
        self.channel = channel
        self.conv1 = nn.Conv2d(in_channels=self.channel, out_channels=self.
            c, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(in_channels=self.c, out_channels=self.c * 2,
            kernel_size=4, stride=2, padding=1)
        self.conv3 = nn.Conv2d(in_channels=self.c * 2, out_channels=self.c *
            4, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(in_channels=self.c * 4, out_channels=self.c *
            8, kernel_size=4, stride=2, padding=1)
        self.conv5 = nn.Conv2d(in_channels=self.c * 8, out_channels=self.c *
            16, kernel_size=4, stride=2, padding=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_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])
        return output[0]
