# AOT ID: ['0_inference']
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/et/cetn7rmsx6tivm45ec3fj2gtj5hjbn26gb6o6nprv5mippdvfh5b.py
# Topologically Sorted Source Nodes: [mul, mul_1, add, mul_2, add_1], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 0.299), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, 0.587), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_2, 0.114), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_2), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_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=[64], 
    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_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp1 = 0.299
    tmp2 = tmp0 * tmp1
    tmp4 = 0.587
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = 0.114
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4f/c4fjzuz675f2kz7y7n6ofr7suximzytj4whwtg3arydfso2bvlu4.py
# Topologically Sorted Source Nodes: [pow_1, pow_2, add_2, x_1, min_1, max_1, min_2], Original ATen: [aten.pow, aten.add, aten.min, aten.max]
# Source node to ATen node mapping:
#   add_2 => add_2
#   max_1 => max_1
#   min_1 => min_1
#   min_2 => min_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   x_1 => pow_3
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%convolution, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%convolution_1, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %pow_3 : [num_users=4] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_2, 0.5), kwargs = {})
#   %min_1 : [num_users=1] = call_function[target=torch.ops.aten.min.default](args = (%pow_3,), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%pow_3,), kwargs = {})
#   %min_2 : [num_users=1] = call_function[target=torch.ops.aten.min.default](args = (%pow_3,), kwargs = {})
triton_per_fused_add_max_min_pow_1 = async_compile.triton('triton_per_fused_add_max_min_pow_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_max_min_pow_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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}
)
@triton.jit
def triton_per_fused_add_max_min_pow_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp2 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = triton_helpers.min2(tmp6, 1)[:, None]
    tmp10 = triton_helpers.max2(tmp6, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, None)
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp10, None)
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2g/c2gcliugfqrmhgxgfxkeop7kku2cer54jg4aqp257fhigudythgg.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_4 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%arg0_1, %constant_pad_nd], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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: '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_cat_2', '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_cat_2(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
    x2 = xindex
    x0 = xindex % 64
    x1 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tl.store(out_ptr0 + (x0 + (80*x1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jd/cjdzp6d74icsvf5plstv3riz52kw4dpgs5f4soc4dvecrag7yv56.py
# Topologically Sorted Source Nodes: [pow_1, pow_2, add_2, x_1, sub, sub_1, x_2, x_3], Original ATen: [aten.pow, aten.add, aten.sub, aten.div, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   add_2 => add_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   x_1 => pow_3
#   x_2 => div
#   x_3 => constant_pad_nd
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%convolution, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%convolution_1, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %pow_3 : [num_users=4] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_2, 0.5), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_3, %min_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%max_1, %min_2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sub_1), kwargs = {})
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%div, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_add_constant_pad_nd_div_pow_sub_3 = async_compile.triton('triton_poi_fused_add_constant_pad_nd_div_pow_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=[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_add_constant_pad_nd_div_pow_sub_3', '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_add_constant_pad_nd_div_pow_sub_3(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
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex % 16
    tmp17 = tl.load(in_ptr2 + (0))
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK])
    tmp20 = tl.load(in_ptr3 + (0))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp22 = tl.load(in_ptr4 + (0))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-3) + x0 + (2*x1) + (4*x2)), tmp10 & xmask, other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tl.load(in_ptr1 + ((-3) + x0 + (2*x1) + (4*x2)), tmp10 & xmask, other=0.0)
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp19 = tmp16 - tmp18
    tmp24 = tmp21 - tmp23
    tmp25 = tmp19 / tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp10, tmp25, tmp26)
    tl.store(out_ptr0 + (x4 + (80*x2)), tmp27, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(arg2_1, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, add, mul_2, add_1], Original ATen: [aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [gradx], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 0, 4, 1), 0), arg1_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 2, 2), (4, 4, 2, 1))
        del arg1_1
        # Topologically Sorted Source Nodes: [grady], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 0, 4, 1), 0), arg2_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 2, 2), (4, 4, 2, 1))
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, pow_2, add_2, x_1, min_1, max_1, min_2], Original ATen: [aten.pow, aten.add, aten.min, aten.max]
        triton_per_fused_add_max_min_pow_1.run(buf1, buf2, buf3, buf4, buf5, 1, 16, grid=grid(1), stream=stream0)
        buf8 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        buf6 = reinterpret_tensor(buf8, (4, 4, 4, 4), (80, 16, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(arg0_1, buf6, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf7 = reinterpret_tensor(buf8, (4, 1, 4, 4), (80, 16, 4, 1), 64)  # alias
        # Topologically Sorted Source Nodes: [pow_1, pow_2, add_2, x_1, sub, sub_1, x_2, x_3], Original ATen: [aten.pow, aten.add, aten.sub, aten.div, aten.constant_pad_nd]
        triton_poi_fused_add_constant_pad_nd_div_pow_sub_3.run(buf1, buf2, buf3, buf4, buf5, buf7, 64, grid=grid(64), stream=stream0)
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
    return (buf8, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    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 numpy as np
from torch import nn
import torch.nn.functional as F


class sobel_net(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv_opx = nn.Conv2d(1, 1, 3, bias=False)
        self.conv_opy = nn.Conv2d(1, 1, 3, bias=False)
        sobel_kernelx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
            dtype='float32').reshape((1, 1, 3, 3))
        sobel_kernely = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]],
            dtype='float32').reshape((1, 1, 3, 3))
        self.conv_opx.weight.data = torch.from_numpy(sobel_kernelx)
        self.conv_opy.weight.data = torch.from_numpy(sobel_kernely)
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, im):
        x = (0.299 * im[:, 0, :, :] + 0.587 * im[:, 1, :, :] + 0.114 * im[:,
            2, :, :]).unsqueeze(1)
        gradx = self.conv_opx(x)
        grady = self.conv_opy(x)
        x = (gradx ** 2 + grady ** 2) ** 0.5
        x = (x - x.min()) / (x.max() - x.min())
        x = F.pad(x, (1, 1, 1, 1))
        x = torch.cat([im, x], dim=1)
        return x


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


def get_init_inputs():
    return [[], {}]

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 numpy as np
from torch import nn
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_add_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp1 = 0.299
    tmp2 = tmp0 * tmp1
    tmp4 = 0.587
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = 0.114
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_add_max_min_pow_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    tmp2 = tl.load(in_ptr1 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = triton_helpers.min2(tmp6, 1)[:, None]
    tmp10 = triton_helpers.max2(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp10, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused_cat_2(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
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 80 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_add_constant_pad_nd_div_pow_sub_3(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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    tmp17 = tl.load(in_ptr2 + 0)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK])
    tmp20 = tl.load(in_ptr3 + 0)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp22 = tl.load(in_ptr4 + 0)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-3 + x0 + 2 * x1 + 4 * x2), tmp10 & xmask,
        other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tl.load(in_ptr1 + (-3 + x0 + 2 * x1 + 4 * x2), tmp10 & xmask,
        other=0.0)
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp19 = tmp16 - tmp18
    tmp24 = tmp21 - tmp23
    tmp25 = tmp19 / tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp10, tmp25, tmp26)
    tl.store(out_ptr0 + (x4 + 80 * x2), tmp27, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(arg2_1, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 1, 4,
            4), (16, 0, 4, 1), 0), arg1_1, stride=(1, 1), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 2, 2), (4, 4, 2, 1))
        del arg1_1
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 1, 4,
            4), (16, 0, 4, 1), 0), arg2_1, stride=(1, 1), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 2, 2), (4, 4, 2, 1))
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_max_min_pow_1[grid(1)](buf1, buf2, buf3, buf4,
            buf5, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        buf6 = reinterpret_tensor(buf8, (4, 4, 4, 4), (80, 16, 4, 1), 0)
        triton_poi_fused_cat_2[grid(256)](arg0_1, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf7 = reinterpret_tensor(buf8, (4, 1, 4, 4), (80, 16, 4, 1), 64)
        triton_poi_fused_add_constant_pad_nd_div_pow_sub_3[grid(64)](buf1,
            buf2, buf3, buf4, buf5, buf7, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
    return buf8,


class sobel_netNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv_opx = nn.Conv2d(1, 1, 3, bias=False)
        self.conv_opy = nn.Conv2d(1, 1, 3, bias=False)
        sobel_kernelx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
            dtype='float32').reshape((1, 1, 3, 3))
        sobel_kernely = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]],
            dtype='float32').reshape((1, 1, 3, 3))
        self.conv_opx.weight.data = torch.from_numpy(sobel_kernelx)
        self.conv_opy.weight.data = torch.from_numpy(sobel_kernely)
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, input_0):
        arg1_1 = self.conv_opx.weight
        arg2_1 = self.conv_opy.weight
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
