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, math as tl_math
import torch.nn as nn
from torch.nn import Parameter
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_reflection_pad2d_0(out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + x0) + -4 * tl_math
        .abs(-3 + x1) + 16 * x2), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_mv_2(in_ptr0, in_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.load(in_ptr0 + (4 + r0), None)
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp9 = tl.load(in_ptr0 + (8 + r0), None)
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp14 = tl.load(in_ptr0 + (12 + r0), None)
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.sum(tmp20, 1)[:, None]
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-12
    tmp25 = tmp23 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp26, None)


@triton.jit
def triton_per_fused_add_div_dot_linalg_vector_norm_mv_3(in_ptr0, in_ptr1,
    out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.sum(tmp20, 1)[:, None]
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-12
    tmp25 = tmp23 + tmp24
    tmp26 = tmp18 / tmp25
    tmp27 = tmp26 * tmp18
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tl.store(out_ptr3 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp26, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)


@triton.jit
def triton_poi_fused_div_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_5(in_out_ptr0, in_ptr0,
    out_ptr0, 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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), 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], 16, 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 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.2
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_mv_7(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.load(in_ptr0 + (36 + r0), rmask, other=0.0)
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp9 = tl.load(in_ptr0 + (72 + r0), rmask, other=0.0)
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp14 = tl.load(in_ptr0 + (108 + r0), rmask, other=0.0)
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.where(rmask, tmp20, 0)
    tmp23 = tl.sum(tmp22, 1)[:, None]
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-12
    tmp26 = tmp24 + tmp25
    tmp27 = tmp18 / tmp26
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp18, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, None)
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp27, rmask)


@triton.jit
def triton_per_fused_div_mv_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 36
    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
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 36 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp4 = tmp1 / tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_9(in_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-12
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp8, None)


@triton.jit
def triton_per_fused_dot_10(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused_div_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_relu_threshold_backward_12(
    in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp29 = tl.load(in_ptr1 + (r1 + 16 * x0), 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], 16, 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 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp26 = 0.2
    tmp27 = tmp23 * tmp26
    tmp28 = tl.where(tmp25, tmp23, tmp27)
    tmp30 = tmp28 + tmp29
    tmp31 = tl.full([1, 1], 0, tl.int32)
    tmp32 = triton_helpers.maximum(tmp31, tmp30)
    tmp33 = tmp32 <= tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 16 * x0), tmp32, xmask)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp33, 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (36,), (1,))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(4)](buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_reflection_pad2d_1[grid(256)](primals_1, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_mv_2[grid(1)](primals_4,
            primals_2, buf4, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf30 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_div_dot_linalg_vector_norm_mv_3[grid(1)](primals_4
            , buf4, buf7, buf30, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_div_4[grid(16)](primals_4, buf7, buf8, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf9 = extern_kernels.convolution(buf1, buf8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf13 = reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf11
        triton_per_fused__native_batch_norm_legit_5[grid(16)](buf13, buf9,
            buf10, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
            )
        triton_poi_fused_reflection_pad2d_6[grid(576)](buf9, buf10, buf13,
            buf14, 576, XBLOCK=128, num_warps=4, num_stages=1)
        buf15 = empty_strided_cuda((36,), (1,), torch.float32)
        buf16 = buf7
        del buf7
        buf17 = buf16
        del buf16
        buf44 = empty_strided_cuda((36,), (1,), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_mv_7[grid(1)](buf17,
            primals_7, primals_5, buf15, buf44, 1, 36, XBLOCK=1, num_warps=
            2, num_stages=1)
        buf18 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_div_mv_8[grid(4)](primals_7, buf15, buf17, buf18, 
            4, 36, XBLOCK=1, num_warps=2, num_stages=1)
        buf20 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_9[grid(1)](buf18, buf20,
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        buf21 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_dot_10[grid(1)](buf20, buf18, buf21, 1, 4, XBLOCK=
            1, num_warps=2, num_stages=1)
        del buf18
        buf22 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_div_11[grid(144)](primals_7, buf21, buf22, 144,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf23 = extern_kernels.convolution(buf14, buf22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 4, 4, 4), (64, 16, 4, 1))
        buf24 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf25 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf27 = reinterpret_tensor(buf25, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf25
        buf28 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf29 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_per_fused__native_batch_norm_legit_add_relu_threshold_backward_12[
            grid(16)](buf27, buf23, primals_1, buf24, buf28, buf29, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del primals_1
        buf31 = torch.ops.aten.set_.source_Tensor(primals_2, buf30)
        assert_size_stride(buf31, (4,), (1,))
        del primals_2
        buf35 = torch.ops.aten.set_.source_Tensor(primals_3, buf4)
        assert_size_stride(buf35, (4,), (1,))
        del primals_3
        buf39 = torch.ops.aten.set_.source_Tensor(primals_5, buf20)
        assert_size_stride(buf39, (4,), (1,))
        del buf15
        buf45 = torch.ops.aten.set_.source_Tensor(primals_6, buf44)
        assert_size_stride(buf45, (36,), (1,))
        del primals_6
    return (buf28, buf8, buf22, primals_4, primals_5, primals_7, buf0, buf1,
        buf4, buf8, buf9, buf10, buf13, buf14, buf17, buf20, buf21, buf22,
        buf23, buf24, buf27, buf29)


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class LayerNorm(nn.Module):

    def __init__(self, num_features, eps=1e-08, affine=True):
        super(LayerNorm, self).__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = Parameter(torch.Tensor(num_features).uniform_())
            self.beta = Parameter(torch.zeros(num_features))

    def forward(self, x):
        shape = [-1] + [1] * (x.dim() - 1)
        if x.size(0) == 1:
            mean = x.view(-1).mean().view(*shape)
            std = x.view(-1).std().view(*shape)
        else:
            mean = x.view(x.size(0), -1).mean(1).view(*shape)
            std = x.view(x.size(0), -1).std(1).view(*shape)
        x = (x - mean) / (std + self.eps)
        if self.affine:
            shape = [1, -1] + [1] * (x.dim() - 2)
            x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class Conv2dLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='zero', activation='lrelu', norm=
        'none', sn=True):
        super(Conv2dLayer, self).__init__()
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad2d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad2d(padding)
        elif pad_type == 'zero':
            self.pad = nn.ZeroPad2d(padding)
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(out_channels)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(out_channels)
        elif norm == 'ln':
            self.norm = LayerNorm(out_channels)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'prelu':
            self.activation = nn.PReLU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv2d = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation, bias=False))
        else:
            self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride, padding=0, dilation=dilation, bias=False)

    def forward(self, x):
        x = self.pad(x)
        x = self.conv2d(x)
        if self.norm:
            x = self.norm(x)
        if self.activation:
            x = self.activation(x)
        return x


class BasicBlock_INNew(nn.Module):

    def __init__(self, inplanes, planes, stride=1, first=False):
        super(BasicBlock_INNew, self).__init__()
        self.expansion = 4
        self.stride = stride
        self.first = first
        self.conv1 = Conv2dLayer(inplanes, planes, 1, stride, 0, pad_type=
            'reflect', activation='lrelu', norm='in')
        self.conv2 = Conv2dLayer(planes, planes, 3, 1, 1, pad_type=
            'reflect', activation='lrelu', norm='in')
        if stride != 1 or first is True:
            self.downsample = Conv2dLayer(inplanes, planes * self.expansion,
                1, stride, 0, pad_type='reflect', activation='lrelu', norm='bn'
                )
        self.relu = nn.ReLU(inplace=True)

    def forward(self, input_0):
        primals_2 = self.conv1.conv2d.module.weight_u
        primals_3 = self.conv1.conv2d.module.weight_v
        primals_4 = self.conv1.conv2d.module.weight_bar
        primals_5 = self.conv2.conv2d.module.weight_u
        primals_6 = self.conv2.conv2d.module.weight_v
        primals_7 = self.conv2.conv2d.module.weight_bar
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.107862