# 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_1/inductor_cache/nd/cnd7i6ikfohhr7cv6t5hgbordc6otybc225qvzaa5aboneegp7sq.py
# Topologically Sorted Source Nodes: [conv2d, conv2d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
#   conv2d_1 => convolution_1
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg0_1, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 4), kwargs = {})
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg2_1, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_convolution_0(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/sb/csblbppfaoexwwzbj6kzlvxsskzy4fbftk3huh4lvz7v7dj7jcoy.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_cat_1', '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x0) + (64*x2) + x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x0) + (64*x2) + ((-4) + x1)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, 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, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, conv2d_1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(arg1_1, buf0, buf2, 16, 16, grid=grid(16, 16), stream=stream0)
        del arg1_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 1, 16, 4))
        del arg0_1
        del buf0
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 1, 16, 4))
        del arg2_1
        del buf2
        buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf1, buf3, buf4, 512, grid=grid(512), stream=stream0)
        del buf1
        del buf3
    return (buf4, )


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, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 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 torch as th
import torch.utils.data


class ImageGradients(th.nn.Module):
    """
  Args:
    c_in(int): number of channels expected in the images.
    use_sobel(bool): if True, uses a (smoother) Sobel filter instead of simple
      finite differences.
  """

    def __init__(self, c_in, use_sobel=True):
        super(ImageGradients, self).__init__()
        if use_sobel:
            self.dx = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
                False, groups=c_in)
            self.dy = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
                False, groups=c_in)
            self.dx.weight.data.zero_()
            self.dx.weight.data[:, :, 0, 0] = -1
            self.dx.weight.data[:, :, 0, 2] = 1
            self.dx.weight.data[:, :, 1, 0] = -2
            self.dx.weight.data[:, :, 1, 2] = 2
            self.dx.weight.data[:, :, 2, 0] = -1
            self.dx.weight.data[:, :, 2, 2] = 1
            self.dy.weight.data.zero_()
            self.dy.weight.data[:, :, 0, 0] = -1
            self.dy.weight.data[:, :, 2, 0] = 1
            self.dy.weight.data[:, :, 0, 1] = -2
            self.dy.weight.data[:, :, 2, 1] = 2
            self.dy.weight.data[:, :, 0, 2] = -1
            self.dy.weight.data[:, :, 2, 2] = 1
        else:
            self.dx = th.nn.Conv2d(c_in, c_in, [1, 3], padding=(0, 1), bias
                =False, groups=c_in)
            self.dy = th.nn.Conv2d(c_in, c_in, [3, 1], padding=(1, 0), bias
                =False, groups=c_in)
            self.dx.weight.data.zero_()
            self.dx.weight.data[:, :, 0, 0] = -1
            self.dx.weight.data[:, :, 0, 1] = 1
            self.dy.weight.data.zero_()
            self.dy.weight.data[:, :, 0, 0] = -1
            self.dy.weight.data[:, :, 1, 0] = 1
        self.dx.weight.requires_grad = False
        self.dy.weight.requires_grad = False

    def forward(self, im):
        return th.cat([self.dx(im), self.dy(im)], 1)


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


def get_init_inputs():
    return [[], {'c_in': 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
import torch as th
import torch.utils.data
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_convolution_0(in_ptr0, out_ptr0, out_ptr1, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + 64 * x2 + x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x0 + 64 * x2 + (-4 + x1)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 16)](arg1_1, buf0, buf2, 16,
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 1, 16, 4))
        del arg0_1
        del buf0
        buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 1, 16, 4))
        del arg2_1
        del buf2
        buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](buf1, buf3, buf4, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        del buf3
    return buf4,


class ImageGradientsNew(th.nn.Module):
    """
  Args:
    c_in(int): number of channels expected in the images.
    use_sobel(bool): if True, uses a (smoother) Sobel filter instead of simple
      finite differences.
  """

    def __init__(self, c_in, use_sobel=True):
        super(ImageGradientsNew, self).__init__()
        if use_sobel:
            self.dx = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
                False, groups=c_in)
            self.dy = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
                False, groups=c_in)
            self.dx.weight.data.zero_()
            self.dx.weight.data[:, :, 0, 0] = -1
            self.dx.weight.data[:, :, 0, 2] = 1
            self.dx.weight.data[:, :, 1, 0] = -2
            self.dx.weight.data[:, :, 1, 2] = 2
            self.dx.weight.data[:, :, 2, 0] = -1
            self.dx.weight.data[:, :, 2, 2] = 1
            self.dy.weight.data.zero_()
            self.dy.weight.data[:, :, 0, 0] = -1
            self.dy.weight.data[:, :, 2, 0] = 1
            self.dy.weight.data[:, :, 0, 1] = -2
            self.dy.weight.data[:, :, 2, 1] = 2
            self.dy.weight.data[:, :, 0, 2] = -1
            self.dy.weight.data[:, :, 2, 2] = 1
        else:
            self.dx = th.nn.Conv2d(c_in, c_in, [1, 3], padding=(0, 1), bias
                =False, groups=c_in)
            self.dy = th.nn.Conv2d(c_in, c_in, [3, 1], padding=(1, 0), bias
                =False, groups=c_in)
            self.dx.weight.data.zero_()
            self.dx.weight.data[:, :, 0, 0] = -1
            self.dx.weight.data[:, :, 0, 1] = 1
            self.dy.weight.data.zero_()
            self.dy.weight.data[:, :, 0, 0] = -1
            self.dy.weight.data[:, :, 1, 0] = 1
        self.dx.weight.requires_grad = False
        self.dy.weight.requires_grad = False

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