# 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/yu/cyukjy6zyghpoobj2kiqmvhtfm26momxqx4wj5dwpqojz2bjrmsy.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   input_2 => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gr/cgrwhgfgy5oslpy5fg5njmlqbzejcwcuv32bitkhyt2gn2zdk5ii.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   input_2 => add_1, clamp_max
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_1, 1), kwargs = {})
#   %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_1, 8), kwargs = {})
triton_poi_fused_add_clamp_1 = async_compile.triton('triton_poi_fused_add_clamp_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_add_clamp_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/e2/ce2qg44mtnheqb2yu5clx667f3whkqdwqocu7v3etgzg4v7s4msf.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   input_2 => add, clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul, sub, sub_2
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (18,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.5), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 0.5), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_2(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vq/cvqky6a76zp3wn7x5f3gzrqxqf46cd7ymeuxgjs42q7itcubfmcc.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_4, add_5, add_6, mul_2, mul_3, mul_4, sub_3, sub_4, sub_6
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [4, 4], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %clamp_max_2), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_3), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_4), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_3 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: '*fp32', 9: '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, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_mul_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5184
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 18) % 18
    x0 = xindex % 18
    x6 = (xindex // 324)
    x2 = (xindex // 324) % 4
    x3 = (xindex // 1296)
    x4 = xindex % 1296
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x1), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (9*tmp4) + (81*x6)), xmask, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + (9*tmp4) + (81*x6)), xmask, eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + (9*tmp25) + (81*x6)), xmask, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + (9*tmp25) + (81*x6)), xmask, eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tmp36 = tmp21 + tmp35
    tl.store(out_ptr2 + (x7), tmp36, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = empty_strided_cuda((18, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_0.run(buf1, 18, grid=grid(18), stream=stream0)
        buf2 = empty_strided_cuda((18, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_1.run(buf2, 18, grid=grid(18), stream=stream0)
        buf3 = empty_strided_cuda((18, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_0.run(buf3, 18, grid=grid(18), stream=stream0)
        buf4 = empty_strided_cuda((18, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_1.run(buf4, 18, grid=grid(18), stream=stream0)
        buf5 = empty_strided_cuda((18, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2.run(buf5, 18, grid=grid(18), stream=stream0)
        buf7 = empty_strided_cuda((18, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2.run(buf7, 18, grid=grid(18), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 18, 18), (1296, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_3.run(buf1, buf3, buf0, primals_2, buf4, buf5, buf2, buf7, buf9, 5184, grid=grid(5184), stream=stream0)
        del buf0
        del primals_2
    return (buf9, primals_1, primals_3, buf1, buf2, buf3, buf4, buf5, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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.utils.data
import torch
import torch.nn as nn


class UpsamplingBlock(nn.Module):

    def __init__(self, input_nc, output_nc, kernel, stride, pad):
        """
        Single block of upsampling operation

        Input:
        - int input_nc    : Input number of channels
        - int output_nc   : Output number of channels
        - int kernel      : Kernel size
        - int stride	  : Stride length
        - int pad         : Padd_moduleing
        """
        super(UpsamplingBlock, self).__init__()
        conv = nn.Conv2d
        biup = nn.Upsample
        block = nn.Sequential()
        block.add_module('conv_1', conv(input_nc, output_nc, kernel, stride,
            pad))
        block.add_module('upsample_2', biup(scale_factor=2, mode='bilinear'))
        self.biup_block = block

    def forward(self, x):
        return self.biup_block(x)


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


def get_init_inputs():
    return [[], {'input_nc': 4, 'output_nc': 4, 'kernel': 4, 'stride': 1,
        'pad': 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.utils.data
import torch
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__to_copy_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 18
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_3(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 5184
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 18 % 18
    x0 = xindex % 18
    x6 = xindex // 324
    x2 = xindex // 324 % 4
    xindex // 1296
    xindex % 1296
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 9 * tmp4 + 81 * x6), xmask,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 9 * tmp4 + 81 * x6), xmask,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 9 * tmp25 + 81 * x6), xmask,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 9 * tmp25 + 81 * x6), xmask,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tmp36 = tmp21 + tmp35
    tl.store(out_ptr2 + x7, tmp36, 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=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = empty_strided_cuda((18, 1), (1, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(18)](buf1, 18, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((18, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_1[grid(18)](buf2, 18, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((18,), (1,), torch.int64)
        triton_poi_fused__to_copy_0[grid(18)](buf3, 18, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((18,), (1,), torch.int64)
        triton_poi_fused_add_clamp_1[grid(18)](buf4, 18, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((18,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2[grid(18)](buf5,
            18, XBLOCK=32, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((18, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_2[grid(18)](buf7,
            18, XBLOCK=32, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 18, 18), (1296, 324, 18, 1), torch
            .float32)
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_3[grid(5184)](
            buf1, buf3, buf0, primals_2, buf4, buf5, buf2, buf7, buf9, 5184,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf9, primals_1, primals_3, buf1, buf2, buf3, buf4, buf5, buf7


class UpsamplingBlockNew(nn.Module):

    def __init__(self, input_nc, output_nc, kernel, stride, pad):
        """
        Single block of upsampling operation

        Input:
        - int input_nc    : Input number of channels
        - int output_nc   : Output number of channels
        - int kernel      : Kernel size
        - int stride	  : Stride length
        - int pad         : Padd_moduleing
        """
        super(UpsamplingBlockNew, self).__init__()
        conv = nn.Conv2d
        biup = nn.Upsample
        block = nn.Sequential()
        block.add_module('conv_1', conv(input_nc, output_nc, kernel, stride,
            pad))
        block.add_module('upsample_2', biup(scale_factor=2, mode='bilinear'))
        self.biup_block = block

    def forward(self, input_0):
        primals_1 = self.biup_block.conv_1.weight
        primals_2 = self.biup_block.conv_1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
