# 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/g4/cg4ecqzb4dmjtwbsjdl47dmonnkxdihi4rkdo72tfcvuyrt35n7o.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sqrt, %sqrt_1, %sqrt_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 36) % 3
    x0 = xindex % 36
    x2 = (xindex // 108)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (36*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp5 * tmp5
    tmp7 = tl.load(in_ptr1 + (x0 + (36*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = 1e-06
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 2, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x0 + (36*x2)), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 * tmp19
    tmp21 = tl.load(in_ptr3 + (x0 + (36*x2)), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = tmp23 + tmp10
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp18, tmp25, tmp26)
    tmp28 = tmp0 >= tmp16
    tmp29 = tl.full([1], 3, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tl.load(in_ptr4 + (x0 + (36*x2)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 * tmp31
    tmp33 = tl.load(in_ptr5 + (x0 + (36*x2)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp34 = tmp33 * tmp33
    tmp35 = tmp32 + tmp34
    tmp36 = tmp35 + tmp10
    tmp37 = libdevice.sqrt(tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp28, tmp37, tmp38)
    tmp40 = tl.where(tmp18, tmp27, tmp39)
    tmp41 = tl.where(tmp4, tmp14, tmp40)
    tl.store(out_ptr0 + (x3), tmp41, 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)
        # Topologically Sorted Source Nodes: [x0_v], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 0), arg1_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 6, 6), (36, 36, 6, 1))
        # Topologically Sorted Source Nodes: [x0_h], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 0), arg2_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 6, 6), (36, 36, 6, 1))
        # Topologically Sorted Source Nodes: [x1_v], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), arg1_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 6, 6), (36, 36, 6, 1))
        # Topologically Sorted Source Nodes: [x1_h], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), arg2_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 6, 6), (36, 36, 6, 1))
        # Topologically Sorted Source Nodes: [x2_v], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), arg1_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 1, 6, 6), (36, 36, 6, 1))
        del arg1_1
        # Topologically Sorted Source Nodes: [x2_h], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), arg2_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 6, 6), (36, 36, 6, 1))
        del arg0_1
        del arg2_1
        buf6 = empty_strided_cuda((4, 3, 6, 6), (108, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, buf1, buf2, buf3, buf4, buf5, buf6, 432, grid=grid(432), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
    return (buf6, )


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
from torch import nn
import torch.nn.functional as F
import torch.utils.data


class Gradient(nn.Module):

    def __init__(self):
        super(Gradient, self).__init__()
        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)
        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)

    def forward(self, x):
        x0 = x[:, 0]
        x1 = x[:, 1]
        x2 = x[:, 2]
        x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
        x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
        x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
        x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
        x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
        x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
        x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-06)
        x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-06)
        x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-06)
        x = torch.cat([x0, x1, x2], 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.triton_helpers import libdevice
from torch import 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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 36 % 3
    x0 = xindex % 36
    x2 = xindex // 108
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 36 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp5 * tmp5
    tmp7 = tl.load(in_ptr1 + (x0 + 36 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = 1e-06
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 2, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x0 + 36 * x2), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 * tmp19
    tmp21 = tl.load(in_ptr3 + (x0 + 36 * x2), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = tmp23 + tmp10
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp18, tmp25, tmp26)
    tmp28 = tmp0 >= tmp16
    tl.full([1], 3, tl.int64)
    tmp31 = tl.load(in_ptr4 + (x0 + 36 * x2), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 * tmp31
    tmp33 = tl.load(in_ptr5 + (x0 + 36 * x2), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp34 = tmp33 * tmp33
    tmp35 = tmp32 + tmp34
    tmp36 = tmp35 + tmp10
    tmp37 = libdevice.sqrt(tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp28, tmp37, tmp38)
    tmp40 = tl.where(tmp18, tmp27, tmp39)
    tmp41 = tl.where(tmp4, tmp14, tmp40)
    tl.store(out_ptr0 + x3, tmp41, 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 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 0), arg1_1, stride=(1, 1), padding=(2, 2),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 6, 6), (36, 36, 6, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 0), arg2_1, stride=(1, 1), padding=(2, 2),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 6, 6), (36, 36, 6, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 16), arg1_1, stride=(1, 1), padding=(2, 2
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 6, 6), (36, 36, 6, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 16), arg2_1, stride=(1, 1), padding=(2, 2
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 6, 6), (36, 36, 6, 1))
        buf4 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 32), arg1_1, stride=(1, 1), padding=(2, 2
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf4, (4, 1, 6, 6), (36, 36, 6, 1))
        del arg1_1
        buf5 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 32), arg2_1, stride=(1, 1), padding=(2, 2
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 6, 6), (36, 36, 6, 1))
        del arg0_1
        del arg2_1
        buf6 = empty_strided_cuda((4, 3, 6, 6), (108, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(432)](buf0, buf1, buf2, buf3, buf4,
            buf5, buf6, 432, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
    return buf6,


class GradientNew(nn.Module):

    def __init__(self):
        super(GradientNew, self).__init__()
        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)
        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)

    def forward(self, input_0):
        arg1_1 = self.weight_h
        arg2_1 = self.weight_v
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
