import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *


def conv3x3(in_channels, out_channels):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, bias=True)


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(ConvBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

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


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
from torch.cuda import *
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    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_per_fused__native_batch_norm_legit_1(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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 % 16
    r2 = rindex // 16
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 64.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_3(in_ptr0
    , in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, xmask)
    tl.store(out_ptr1 + x3, tmp17, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf3 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_1[grid(4)](buf1, buf2,
            buf3, buf5, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_2[grid(256)](buf1,
            buf2, buf3, primals_4, primals_5, buf6, 256, XBLOCK=128,
            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, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_0[grid(256)](buf8, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf9 = buf3
        del buf3
        buf10 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf12 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_1[grid(4)](buf8, buf9,
            buf10, buf12, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_2[grid(256)](buf8,
            buf9, buf10, primals_4, primals_5, buf13, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf14 = extern_kernels.convolution(buf13, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 4, 4), (64, 16, 4, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_0[grid(256)](buf15, primals_9, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf16 = buf10
        del buf10
        buf17 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf19 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_1[grid(4)](buf15, buf16,
            buf17, buf19, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_3[
            grid(256)](buf15, buf16, buf17, primals_4, primals_5, buf20,
            buf21, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf17
        del primals_5
    return (buf20, primals_1, primals_3, primals_4, primals_6, primals_8,
        buf1, reinterpret_tensor(buf5, (4,), (1,), 0), buf6, buf8,
        reinterpret_tensor(buf12, (4,), (1,), 0), buf13, buf15,
        reinterpret_tensor(buf19, (4,), (1,), 0), buf21, reinterpret_tensor
        (buf16, (1, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf9, (1,
        4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf2, (1, 4, 1, 1),
        (4, 1, 1, 1), 0))


def conv3x3(in_channels, out_channels):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, bias=True)


class ConvBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(ConvBlockNew, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_4 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_5 = self.conv3.bias
        primals_7 = self.norm.weight
        primals_9 = self.norm.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])
        return output[0]
