import torch
import torch.nn as nn


class Conv2d_GN_ReLU(nn.Module):
    """ Implements a module that performs 
            conv2d + groupnorm + ReLU + 

        Assumes kernel size is odd
    """

    def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
        ):
        super(Conv2d_GN_ReLU, self).__init__()
        padding = 0 if ksize < 2 else ksize // 2
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=ksize,
            stride=stride, padding=padding, bias=False)
        self.gn1 = nn.GroupNorm(num_groups, out_channels)
        self.relu1 = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.conv1(x)
        out = self.gn1(out)
        out = self.relu1(out)
        return out


class Conv2d_GN_ReLUx2(nn.Module):
    """ Implements a module that performs 
            conv2d + groupnorm + ReLU + 
            conv2d + groupnorm + ReLU
            (and a possible downsampling operation)

        Assumes kernel size is odd
    """

    def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
        ):
        super(Conv2d_GN_ReLUx2, self).__init__()
        self.layer1 = Conv2d_GN_ReLU(in_channels, out_channels, num_groups,
            ksize=ksize, stride=stride)
        self.layer2 = Conv2d_GN_ReLU(out_channels, out_channels, num_groups,
            ksize=ksize, stride=stride)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'num_groups': 1}]

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
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_group_norm_relu_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr2, out_ptr3, 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
    x0 = xindex
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    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 = tmp0 - tmp10
    tmp18 = 64.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp29, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused_native_group_norm_relu_threshold_backward_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, 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
    x0 = xindex
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    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 = tmp0 - tmp10
    tmp18 = 64.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp29, xmask)
    tl.store(out_ptr3 + (r1 + 64 * x0), tmp31, xmask)
    tl.store(out_ptr4 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, 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, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, 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 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_relu_0[grid(4)](buf0, primals_3,
            primals_4, buf1, buf5, buf4, 4, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_4
        buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_per_fused_native_group_norm_relu_threshold_backward_1[grid(4)](
            buf6, primals_6, primals_7, buf7, buf11, buf12, buf10, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        del primals_7
    return (buf11, primals_1, primals_2, primals_3, primals_5, primals_6,
        buf0, reinterpret_tensor(buf1, (4, 1), (1, 1), 0),
        reinterpret_tensor(buf4, (4, 1), (1, 1), 0), buf5, buf6,
        reinterpret_tensor(buf7, (4, 1), (1, 1), 0), reinterpret_tensor(
        buf10, (4, 1), (1, 1), 0), buf12)


class Conv2d_GN_ReLU(nn.Module):
    """ Implements a module that performs 
            conv2d + groupnorm + ReLU + 

        Assumes kernel size is odd
    """

    def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
        ):
        super(Conv2d_GN_ReLU, self).__init__()
        padding = 0 if ksize < 2 else ksize // 2
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=ksize,
            stride=stride, padding=padding, bias=False)
        self.gn1 = nn.GroupNorm(num_groups, out_channels)
        self.relu1 = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.conv1(x)
        out = self.gn1(out)
        out = self.relu1(out)
        return out


class Conv2d_GN_ReLUx2New(nn.Module):
    """ Implements a module that performs 
            conv2d + groupnorm + ReLU + 
            conv2d + groupnorm + ReLU
            (and a possible downsampling operation)

        Assumes kernel size is odd
    """

    def __init__(self, in_channels, out_channels, num_groups, ksize=3, stride=1
        ):
        super(Conv2d_GN_ReLUx2New, self).__init__()
        self.layer1 = Conv2d_GN_ReLU(in_channels, out_channels, num_groups,
            ksize=ksize, stride=stride)
        self.layer2 = Conv2d_GN_ReLU(out_channels, out_channels, num_groups,
            ksize=ksize, stride=stride)

    def forward(self, input_0):
        primals_1 = self.layer1.conv1.weight
        primals_3 = self.layer1.gn1.weight
        primals_4 = self.layer1.gn1.bias
        primals_5 = self.layer2.conv1.weight
        primals_6 = self.layer2.gn1.weight
        primals_7 = self.layer2.gn1.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
