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.triton_helpers import libdevice
import torch.nn as nn
from typing import Tuple
from typing import Union
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_0(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    rnumel = 49
    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, :]
    rmask = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 49 * x3), rmask & 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(rmask & xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 49, 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(rmask & xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 49.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tl.store(in_out_ptr0 + (r2 + 49 * x3), tmp2, rmask & xmask)
    tl.store(out_ptr2 + (r2 + 49 * x3), tmp25, rmask & xmask)
    tl.store(out_ptr3 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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, 4, 4), (64, 16, 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=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 7, 7), (196, 49, 7, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf6 = empty_strided_cuda((1, 16, 7, 7), (784, 49, 7, 1), torch.float32
            )
        buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_convolution_0[grid(16)](buf1,
            primals_2, buf2, buf6, buf5, 16, 49, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_2
    return reinterpret_tensor(buf6, (4, 4, 7, 7), (196, 49, 7, 1), 0
        ), primals_1, primals_3, buf1, reinterpret_tensor(buf5, (16,), (1,), 0
        ), reinterpret_tensor(buf2, (1, 16, 1, 1), (16, 1, 1, 1), 0)


class ConvTransposeInstanceNorm2dNew(nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'Union[int, Tuple[int]]', stride: 'Union[int, Tuple[int]]'=1,
        padding: 'Union[int, Tuple[int]]'=0, output_padding:
        'Union[int, Tuple[int]]'=0, dilation: 'Union[int, Tuple[int]]'=1,
        groups: 'int'=1, bias: 'bool'=True, padding_mode: 'str'='zeros'):
        super().__init__()
        self.dconv = nn.ConvTranspose2d(in_channels, out_channels,
            kernel_size, stride, padding, output_padding, groups, bias,
            dilation, padding_mode)
        self.norm = nn.InstanceNorm2d(out_channels)

    def forward(self, input_0):
        primals_1 = self.dconv.weight
        primals_2 = self.dconv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.035893