# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/xl/cxlk2zmc3hpwjqxewas3mgw6gadfa7aumzsmflghwni54n2ev2cj.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   x_1 => repeat
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_3, [4]), kwargs = {})
triton_poi_fused_repeat_0 = async_compile.triton('triton_poi_fused_repeat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_0(in_ptr0, 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 % 4), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4z/c4zvwvsryxghc5ysn6bpnjiv32y2hcfq23grsiocdduitkk25dil.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   x_1 => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_1 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_1(in_ptr0, out_ptr0, out_ptr1, 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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2l/c2ltoxti4vablzqcsbomvbkbrq355u3db4abx22nd2iin26g2lsf.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_2 => relu
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = (xindex // 4)
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4g/c4gcr34isn5uv5kxz7a4uttjmiiim6ryfo2imaotdptez6c56f53.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_3 => getitem_2, getitem_3
# Graph fragment:
#   %getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, 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.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + ((-3) + (4*x0)), tmp6 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + ((-2) + (4*x0)), tmp11 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp13 = triton_helpers.maximum(tmp12, tmp7)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp18 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp20 = triton_helpers.maximum(tmp19, tmp13)
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp21 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp23 = triton_helpers.maximum(tmp22, tmp20)
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + (4*x0), tmp24 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp26 = triton_helpers.maximum(tmp25, tmp23)
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + (4*x0)), tmp27 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + (4*x0)), tmp30 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp29)
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + (4*x0)), tmp33 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + (4*x0)), tmp36 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = tmp12 > tmp7
    tmp40 = tl.full([1], 1, tl.int8)
    tmp41 = tl.full([1], 0, tl.int8)
    tmp42 = tl.where(tmp39, tmp40, tmp41)
    tmp43 = tmp19 > tmp13
    tmp44 = tl.full([1], 2, tl.int8)
    tmp45 = tl.where(tmp43, tmp44, tmp42)
    tmp46 = tmp22 > tmp20
    tmp47 = tl.full([1], 3, tl.int8)
    tmp48 = tl.where(tmp46, tmp47, tmp45)
    tmp49 = tmp25 > tmp23
    tmp50 = tl.full([1], 4, tl.int8)
    tmp51 = tl.where(tmp49, tmp50, tmp48)
    tmp52 = tmp28 > tmp26
    tmp53 = tl.full([1], 5, tl.int8)
    tmp54 = tl.where(tmp52, tmp53, tmp51)
    tmp55 = tmp31 > tmp29
    tmp56 = tl.full([1], 6, tl.int8)
    tmp57 = tl.where(tmp55, tmp56, tmp54)
    tmp58 = tmp34 > tmp32
    tmp59 = tl.full([1], 7, tl.int8)
    tmp60 = tl.where(tmp58, tmp59, tmp57)
    tmp61 = tmp37 > tmp35
    tmp62 = tl.full([1], 8, tl.int8)
    tmp63 = tl.where(tmp61, tmp62, tmp60)
    tl.store(out_ptr0 + (x0), tmp38, xmask)
    tl.store(out_ptr1 + (x0), tmp63, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 7, 7), (196, 49, 7, 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, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 2), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.repeat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_repeat_0.run(primals_3, buf1, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_1.run(buf0, buf2, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf0, buf2, buf3, buf1, primals_4, buf4, 64, grid=grid(64), stream=stream0)
        del buf2
        del primals_4
        buf5 = reinterpret_tensor(buf3, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf3  # reuse
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf4, buf5, buf6, 16, grid=grid(16), stream=stream0)
    return (buf5, primals_1, primals_2, buf0, buf1, buf4, buf6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 7, 7), (196, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.utils.data


def ibnb_conv7x7_block(in_channels, out_channels, stride=1, padding=3, bias
    =False, activate=True):
    """
    7x7 version of the IBN(b)-ResNet specific convolution block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int, default 1
        Strides of the convolution.
    padding : int or tuple/list of 2 int, default 3
        Padding value for convolution layer.
    bias : bool, default False
        Whether the layer uses a bias vector.
    activate : bool, default True
        Whether activate the convolution block.
    """
    return IBNbConvBlock(in_channels=in_channels, out_channels=out_channels,
        kernel_size=7, stride=stride, padding=padding, bias=bias, activate=
        activate)


class IBNbConvBlock(nn.Module):
    """
    IBN(b)-ResNet specific convolution block with Instance normalization and ReLU activation.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    padding : int or tuple/list of 2 int
        Padding value for convolution layer.
    dilation : int or tuple/list of 2 int, default 1
        Dilation value for convolution layer.
    groups : int, default 1
        Number of groups.
    bias : bool, default False
        Whether the layer uses a bias vector.
    activate : bool, default True
        Whether activate the convolution block.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, dilation=1, groups=1, bias=False, activate=True):
        super(IBNbConvBlock, self).__init__()
        self.activate = activate
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding, dilation=dilation, groups=groups, bias=bias)
        self.inst_norm = nn.InstanceNorm2d(num_features=out_channels,
            affine=True)
        if self.activate:
            self.activ = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.inst_norm(x)
        if self.activate:
            x = self.activ(x)
        return x


class IBNbResInitBlock(nn.Module):
    """
    IBN(b)-ResNet specific initial block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    """

    def __init__(self, in_channels, out_channels):
        super(IBNbResInitBlock, self).__init__()
        self.conv = ibnb_conv7x7_block(in_channels=in_channels,
            out_channels=out_channels, stride=2)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = self.conv(x)
        x = self.pool(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
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_repeat_0(in_ptr0, 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 % 4, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_1(in_ptr0, out_ptr0, out_ptr1,
    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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    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.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + (-3 + 4 * x0), tmp6 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + (-2 + 4 * x0), tmp11 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp13 = triton_helpers.maximum(tmp12, tmp7)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp18 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp20 = triton_helpers.maximum(tmp19, tmp13)
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp21 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp23 = triton_helpers.maximum(tmp22, tmp20)
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + 4 * x0, tmp24 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp26 = triton_helpers.maximum(tmp25, tmp23)
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + 4 * x0), tmp27 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + 4 * x0), tmp30 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp29)
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + 4 * x0), tmp33 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + 4 * x0), tmp36 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = tmp12 > tmp7
    tmp40 = tl.full([1], 1, tl.int8)
    tmp41 = tl.full([1], 0, tl.int8)
    tmp42 = tl.where(tmp39, tmp40, tmp41)
    tmp43 = tmp19 > tmp13
    tmp44 = tl.full([1], 2, tl.int8)
    tmp45 = tl.where(tmp43, tmp44, tmp42)
    tmp46 = tmp22 > tmp20
    tmp47 = tl.full([1], 3, tl.int8)
    tmp48 = tl.where(tmp46, tmp47, tmp45)
    tmp49 = tmp25 > tmp23
    tmp50 = tl.full([1], 4, tl.int8)
    tmp51 = tl.where(tmp49, tmp50, tmp48)
    tmp52 = tmp28 > tmp26
    tmp53 = tl.full([1], 5, tl.int8)
    tmp54 = tl.where(tmp52, tmp53, tmp51)
    tmp55 = tmp31 > tmp29
    tmp56 = tl.full([1], 6, tl.int8)
    tmp57 = tl.where(tmp55, tmp56, tmp54)
    tmp58 = tmp34 > tmp32
    tmp59 = tl.full([1], 7, tl.int8)
    tmp60 = tl.where(tmp58, tmp59, tmp57)
    tmp61 = tmp37 > tmp35
    tmp62 = tl.full([1], 8, tl.int8)
    tmp63 = tl.where(tmp61, tmp62, tmp60)
    tl.store(out_ptr0 + x0, tmp38, xmask)
    tl.store(out_ptr1 + x0, tmp63, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 7, 7), (196, 49, 7, 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = empty_strided_cuda((16,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(16)](primals_3, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_poi_fused__native_batch_norm_legit_1[grid(16)](buf0, buf2,
            buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_relu_2[grid(64)](buf0, buf2, buf3, buf1, primals_4,
            buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
        del primals_4
        buf5 = reinterpret_tensor(buf3, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf3
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(16)](buf4, buf5,
            buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf5, primals_1, primals_2, buf0, buf1, buf4, buf6


def ibnb_conv7x7_block(in_channels, out_channels, stride=1, padding=3, bias
    =False, activate=True):
    """
    7x7 version of the IBN(b)-ResNet specific convolution block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int, default 1
        Strides of the convolution.
    padding : int or tuple/list of 2 int, default 3
        Padding value for convolution layer.
    bias : bool, default False
        Whether the layer uses a bias vector.
    activate : bool, default True
        Whether activate the convolution block.
    """
    return IBNbConvBlock(in_channels=in_channels, out_channels=out_channels,
        kernel_size=7, stride=stride, padding=padding, bias=bias, activate=
        activate)


class IBNbConvBlock(nn.Module):
    """
    IBN(b)-ResNet specific convolution block with Instance normalization and ReLU activation.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    padding : int or tuple/list of 2 int
        Padding value for convolution layer.
    dilation : int or tuple/list of 2 int, default 1
        Dilation value for convolution layer.
    groups : int, default 1
        Number of groups.
    bias : bool, default False
        Whether the layer uses a bias vector.
    activate : bool, default True
        Whether activate the convolution block.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, dilation=1, groups=1, bias=False, activate=True):
        super(IBNbConvBlock, self).__init__()
        self.activate = activate
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding, dilation=dilation, groups=groups, bias=bias)
        self.inst_norm = nn.InstanceNorm2d(num_features=out_channels,
            affine=True)
        if self.activate:
            self.activ = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.inst_norm(x)
        if self.activate:
            x = self.activ(x)
        return x


class IBNbResInitBlockNew(nn.Module):
    """
    IBN(b)-ResNet specific initial block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    """

    def __init__(self, in_channels, out_channels):
        super(IBNbResInitBlockNew, self).__init__()
        self.conv = ibnb_conv7x7_block(in_channels=in_channels,
            out_channels=out_channels, stride=2)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, input_0):
        primals_1 = self.conv.conv.weight
        primals_3 = self.conv.inst_norm.weight
        primals_4 = self.conv.inst_norm.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
