# 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_1/inductor_cache/lw/clwmhv3ard4vibrwx3znjaja7nsm4gxiuq5s4gjf3wv47kclvepv.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x => add
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %primals_2), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_0(in_ptr0, in_ptr1, 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
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fx/cfxyl7fil5puh5xllnvnypodhp2566clml7xolta3dccawk6glj2.py
# Topologically Sorted Source Nodes: [x_1, add_1, out, x_2], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   add_1 => add_1
#   out => relu
#   x_1 => convolution
#   x_2 => add_2
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %primals_3, %primals_4, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %primals_5), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %primals_6), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_relu_threshold_backward_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_add_convolution_relu_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_convolution_relu_threshold_backward_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (0))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 + tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp10 = tmp7 + tmp9
    tmp11 = 0.0
    tmp12 = tmp7 <= tmp11
    tl.store(out_ptr0 + (x3), tmp10, xmask)
    tl.store(out_ptr1 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ki/ckicbc5amfeyao6o6reux5pjkp6hdqwsvkwip4tonvuecjc3pmll.py
# Topologically Sorted Source Nodes: [x_3, mul, out_1, out_2, out_3], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   mul => mul
#   out_1 => add_3
#   out_2 => add_4
#   out_3 => relu_1
#   x_3 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %primals_7, %primals_8, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, %primals_9), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_10), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_convolution_mul_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_add_convolution_mul_relu_threshold_backward_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_convolution_mul_relu_threshold_backward_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp9 = tl.load(in_ptr3 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 * tmp4
    tmp8 = tmp5 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = 0.0
    tmp14 = tmp12 <= tmp13
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
    tl.store(out_ptr1 + (x3), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (1, ), (1, ))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (1, ), (1, ))
    assert_size_stride(primals_10, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1, add_1, out, x_2], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf1, primals_4, primals_5, primals_6, buf2, buf7, 256, grid=grid(256), stream=stream0)
        del primals_4
        del primals_5
        del primals_6
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = buf1; del buf1  # reuse
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3, mul, out_1, out_2, out_3], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_convolution_mul_relu_threshold_backward_2.run(buf4, primals_8, primals_9, primals_10, primals_1, buf5, buf6, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_10
        del primals_8
    return (buf5, primals_3, primals_7, primals_9, buf0, buf2, buf4, buf6, buf7, )


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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
    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 as th
import torch.utils.data
import torch.nn as nn


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


def _get_norm_layer(norm_layer, channels):
    valid = ['instance', 'batch']
    assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
    if norm_layer == 'instance':
        layer = nn.InstanceNorm2d(channels, affine=True)
    elif norm_layer == 'batch':
        layer = nn.BatchNorm2d(channels, affine=True)
    nn.init.constant_(layer.bias, 0.0)
    nn.init.constant_(layer.weight, 1.0)
    return layer


class ConvModule(nn.Module):
    """Basic convolution module with conv + norm(optional) + activation(optional).

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      ksize(int): size of the convolution kernel (square).
      stride(int): downsampling factor
      pad(bool): if True, pad the convolutions to maintain a constant size.
      padding_mode(str): 'zero' or 'reflection'.
      activation(str): nonlinear activation function between convolutions.
      norm_layer(str): normalization to apply between the convolution modules.
    """

    def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
        padding_mode='zero', activation=None, norm_layer=None):
        super(ConvModule, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
            n_in)
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
            n_out)
        assert isinstance(ksize, int
            ) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
            ksize)
        assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
        padding = (ksize - 1) // 2 if pad else 0
        use_bias_in_conv = norm_layer is None
        conv_pad = padding
        if padding_mode == 'reflection' and padding > 0:
            self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
            conv_pad = 0
        else:
            self.add_module('no-op', nn.Identity())
        self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
            padding=conv_pad, bias=use_bias_in_conv))
        if norm_layer is not None:
            self.add_module('norm', _get_norm_layer(norm_layer, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        _init_fc_or_conv(self.conv, activation)

    def forward(self, x):
        for c in self.children():
            x = c(x)
        return x


class FixupBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, n_features, ksize=3, pad=True, padding_mode='zero',
        activation='relu'):
        super(FixupBasicBlock, self).__init__()
        self.bias1a = nn.Parameter(th.zeros(1))
        self.conv1 = ConvModule(n_features, n_features, ksize=ksize, stride
            =1, pad=pad, activation=None, norm_layer=None, padding_mode=
            padding_mode)
        self.bias1b = nn.Parameter(th.zeros(1))
        self.activation = _get_activation(activation)
        self.bias2a = nn.Parameter(th.zeros(1))
        self.conv2 = ConvModule(n_features, n_features, ksize=ksize, stride
            =1, pad=pad, activation=None, norm_layer=None, padding_mode=
            padding_mode)
        self.scale = nn.Parameter(th.ones(1))
        self.bias2b = nn.Parameter(th.zeros(1))
        self.activation2 = _get_activation(activation)
        self.ksize = 3
        self.pad = pad

    def forward(self, x):
        identity = x
        out = self.conv1(x + self.bias1a)
        out = self.activation(out + self.bias1b)
        out = self.conv2(out + self.bias2a)
        out = out * self.scale + self.bias2b
        crop = (self.ksize - 1) // 2 * 2
        if crop > 0 and not self.pad:
            identity = identity[:, :, crop:-crop, crop:-crop]
        out += identity
        out = self.activation2(out)
        return out


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


def get_init_inputs():
    return [[], {'n_features': 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
import torch as th
import torch.utils.data
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


@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, 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
    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_poi_fused_add_convolution_relu_threshold_backward_1(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 + tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp10 = tmp7 + tmp9
    tmp11 = 0.0
    tmp12 = tmp7 <= tmp11
    tl.store(out_ptr0 + x3, tmp10, xmask)
    tl.store(out_ptr1 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp9 = tl.load(in_ptr3 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 * tmp4
    tmp8 = tmp5 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = 0.0
    tmp14 = tmp12 <= tmp13
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)
    tl.store(out_ptr1 + x3, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
            buf1, primals_4, primals_5, primals_6, buf2, buf7, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        del primals_5
        del primals_6
        buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        buf5 = buf1
        del buf1
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_mul_relu_threshold_backward_2[grid
            (256)](buf4, primals_8, primals_9, primals_10, primals_1, buf5,
            buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_10
        del primals_8
    return buf5, primals_3, primals_7, primals_9, buf0, buf2, buf4, buf6, buf7


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


def _get_norm_layer(norm_layer, channels):
    valid = ['instance', 'batch']
    assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
    if norm_layer == 'instance':
        layer = nn.InstanceNorm2d(channels, affine=True)
    elif norm_layer == 'batch':
        layer = nn.BatchNorm2d(channels, affine=True)
    nn.init.constant_(layer.bias, 0.0)
    nn.init.constant_(layer.weight, 1.0)
    return layer


class ConvModule(nn.Module):
    """Basic convolution module with conv + norm(optional) + activation(optional).

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      ksize(int): size of the convolution kernel (square).
      stride(int): downsampling factor
      pad(bool): if True, pad the convolutions to maintain a constant size.
      padding_mode(str): 'zero' or 'reflection'.
      activation(str): nonlinear activation function between convolutions.
      norm_layer(str): normalization to apply between the convolution modules.
    """

    def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
        padding_mode='zero', activation=None, norm_layer=None):
        super(ConvModule, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
            n_in)
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
            n_out)
        assert isinstance(ksize, int
            ) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
            ksize)
        assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
        padding = (ksize - 1) // 2 if pad else 0
        use_bias_in_conv = norm_layer is None
        conv_pad = padding
        if padding_mode == 'reflection' and padding > 0:
            self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
            conv_pad = 0
        else:
            self.add_module('no-op', nn.Identity())
        self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
            padding=conv_pad, bias=use_bias_in_conv))
        if norm_layer is not None:
            self.add_module('norm', _get_norm_layer(norm_layer, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        _init_fc_or_conv(self.conv, activation)

    def forward(self, x):
        for c in self.children():
            x = c(x)
        return x


class FixupBasicBlockNew(nn.Module):
    expansion = 1

    def __init__(self, n_features, ksize=3, pad=True, padding_mode='zero',
        activation='relu'):
        super(FixupBasicBlockNew, self).__init__()
        self.bias1a = nn.Parameter(th.zeros(1))
        self.conv1 = ConvModule(n_features, n_features, ksize=ksize, stride
            =1, pad=pad, activation=None, norm_layer=None, padding_mode=
            padding_mode)
        self.bias1b = nn.Parameter(th.zeros(1))
        self.activation = _get_activation(activation)
        self.bias2a = nn.Parameter(th.zeros(1))
        self.conv2 = ConvModule(n_features, n_features, ksize=ksize, stride
            =1, pad=pad, activation=None, norm_layer=None, padding_mode=
            padding_mode)
        self.scale = nn.Parameter(th.ones(1))
        self.bias2b = nn.Parameter(th.zeros(1))
        self.activation2 = _get_activation(activation)
        self.ksize = 3
        self.pad = pad

    def forward(self, input_0):
        primals_2 = self.bias1a
        primals_5 = self.bias1b
        primals_6 = self.bias2a
        primals_9 = self.scale
        primals_10 = self.bias2b
        primals_3 = self.conv1.conv.weight
        primals_4 = self.conv1.conv.bias
        primals_7 = self.conv2.conv.weight
        primals_8 = self.conv2.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
