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


class CNN(torch.nn.Module):
    """Basic CNN architecture."""

    def __init__(self, in_channels=1):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
        self.conv2 = nn.Conv2d(64, 128, 6, 2)
        self.conv3 = nn.Conv2d(128, 128, 5, 1)
        self.fc1 = nn.Linear(128 * 4 * 4, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = x.view(-1, 128 * 4 * 4)
        x = self.fc1(x)
        x = self.fc2(x)
        return x


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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 36 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 128 * x2 + 3200 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 3249
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 3249 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (y0 + 64 * x2 + 207936 * y1), tmp4, xmask & ymask)


@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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_4(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 512
    xnumel = 484
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 128
    y1 = yindex // 128
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 61952 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 484 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 128 * x2 + 61952 * y1), tmp6, xmask & ymask)


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, (64, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (128, 64, 6, 6), (2304, 36, 6, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (128, 128, 5, 5), (3200, 25, 5, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 2048), (2048, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (10, 128), (128, 1))
    assert_size_stride(primals_11, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((128, 64, 6, 6), (2304, 1, 384, 64),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(8192, 36)](primals_4, buf0, 8192, 36,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((128, 128, 5, 5), (3200, 1, 640, 128),
            torch.float32)
        triton_poi_fused_1[grid(16384, 25)](primals_6, buf1, 16384, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_6
        buf2 = 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(buf2, (4, 64, 57, 57), (207936, 3249, 57, 1))
        buf3 = empty_strided_cuda((4, 64, 57, 57), (207936, 1, 3648, 64),
            torch.float32)
        triton_poi_fused_convolution_relu_2[grid(256, 3249)](buf2,
            primals_2, buf3, 256, 3249, XBLOCK=256, YBLOCK=16, num_warps=8,
            num_stages=1)
        del buf2
        del primals_2
        buf4 = extern_kernels.convolution(buf3, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 128, 26, 26), (86528, 1, 3328, 128))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_3[grid(346112)](buf5, primals_5, 
            346112, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 22, 22), (61952, 1, 2816, 128))
        buf7 = empty_strided_cuda((4, 128, 22, 22), (61952, 484, 22, 1),
            torch.float32)
        buf10 = empty_strided_cuda((4, 128, 22, 22), (61952, 1, 2816, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_4[grid(512, 484)](
            buf6, primals_7, buf7, buf10, 512, 484, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf6
        del primals_7
        buf8 = empty_strided_cuda((121, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf7, (121, 2048
            ), (2048, 1), 0), reinterpret_tensor(primals_8, (2048, 128), (1,
            2048), 0), alpha=1, beta=1, out=buf8)
        del primals_9
        buf9 = empty_strided_cuda((121, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf8, reinterpret_tensor(
            primals_10, (128, 10), (1, 128), 0), alpha=1, beta=1, out=buf9)
        del primals_11
    return (buf9, primals_1, primals_3, buf0, buf1, buf3, buf5,
        reinterpret_tensor(buf7, (121, 2048), (2048, 1), 0), buf8,
        primals_10, primals_8, buf10)


class CNNNew(torch.nn.Module):
    """Basic CNN architecture."""

    def __init__(self, in_channels=1):
        super(CNNNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
        self.conv2 = nn.Conv2d(64, 128, 6, 2)
        self.conv3 = nn.Conv2d(128, 128, 5, 1)
        self.fc1 = nn.Linear(128 * 4 * 4, 128)
        self.fc2 = nn.Linear(128, 10)

    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.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.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]
