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.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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__native_batch_norm_legit_convolution_relu_0(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 16.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp26 = tl.full([1, 1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + 16 * x3), tmp27, xmask)
    tl.store(out_ptr3 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_relu_1(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (r2 + 16 * x3), xmask, other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = tl.full([1, 1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp29 = tmp27 + tmp28
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr1 + (r2 + 16 * x3), tmp29, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (2,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (2, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = 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(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf6 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_convolution_relu_0[grid(8)](
            buf1, primals_2, buf2, buf6, buf5, 8, 16, XBLOCK=8, num_warps=2,
            num_stages=1)
        del primals_2
        buf7 = extern_kernels.convolution(buf6, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 2, 4, 4), (32, 16, 4, 1))
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf13 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_0[grid(8)](
            buf8, primals_5, buf9, buf13, buf12, 8, 16, XBLOCK=8, num_warps
            =2, num_stages=1)
        del primals_5
        buf14 = extern_kernels.convolution(buf13, primals_6, stride=(1, 1),
            padding=(0, 0), 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
        buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf17 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf19 = reinterpret_tensor(buf17, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf17
        buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_relu_1[grid
            (16)](buf15, buf19, primals_7, primals_3, buf16, buf20, 16, 16,
            XBLOCK=8, num_warps=2, num_stages=1)
        del primals_7
    return (buf20, primals_1, primals_3, primals_4, primals_6, buf1,
        reinterpret_tensor(buf5, (8,), (1,), 0), buf6, buf8,
        reinterpret_tensor(buf12, (8,), (1,), 0), buf13, buf15, buf16,
        buf19, reinterpret_tensor(buf9, (1, 8, 1, 1), (8, 1, 1, 1), 0),
        reinterpret_tensor(buf2, (1, 8, 1, 1), (8, 1, 1, 1), 0))


class ConvBnRelu(nn.Module):
    """
        A block of convolution, relu, batchnorm
    """

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=0):
        super(ConvBnRelu, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding)
        self.bn = nn.InstanceNorm2d(out_channels)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class ConvTripleBlock(nn.Module):
    """
        A block of 3 ConvBnRelu blocks.
        This triple block makes up a residual block as described in the paper
        Resolution h x w does not change across this block
    """

    def __init__(self, in_channels, out_channels):
        super(ConvTripleBlock, self).__init__()
        out_channels_half = out_channels // 2
        self.convblock1 = ConvBnRelu(in_channels, out_channels_half)
        self.convblock2 = ConvBnRelu(out_channels_half, out_channels_half,
            kernel_size=3, stride=1, padding=1)
        self.convblock3 = ConvBnRelu(out_channels_half, out_channels)

    def forward(self, x):
        x = self.convblock1(x)
        x = self.convblock2(x)
        x = self.convblock3(x)
        return x


class SkipLayer(nn.Module):
    """
        The skip connections are necessary for transferring global and local context
        Resolution h x w does not change across this block
    """

    def __init__(self, in_channels, out_channels):
        super(SkipLayer, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        if in_channels != out_channels:
            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        if self.in_channels != self.out_channels:
            x = self.conv(x)
        return x


class ResidualNew(nn.Module):
    """
        The highly used Residual block
        Resolution h x w does not change across this block
    """

    def __init__(self, in_channels, out_channels):
        super(ResidualNew, self).__init__()
        self.convblock = ConvTripleBlock(in_channels, out_channels)
        self.skip = SkipLayer(in_channels, out_channels)

    def forward(self, input_0):
        primals_1 = self.convblock.convblock1.conv.weight
        primals_2 = self.convblock.convblock1.conv.bias
        primals_4 = self.convblock.convblock2.conv.weight
        primals_5 = self.convblock.convblock2.conv.bias
        primals_6 = self.convblock.convblock3.conv.weight
        primals_7 = self.convblock.convblock3.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.030221