# 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/cx/ccxv2gddx3aj3sz6nkndhqvo3cxhrcahhkhcpxra2clpmdiwddhw.py
# Topologically Sorted Source Nodes: [diff_x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   diff_x_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %slice_20, %mul_1], 3), 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=[16384], 
    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_0', 'mutated_arg_names': [], '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = x0
    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 + (1 + (64*x1)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (64*x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 63, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr1 + (1 + (64*x1) + ((-1) + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp0 >= tmp13
    tmp18 = tl.full([1], 64, tl.int64)
    tmp19 = tmp0 < tmp18
    tmp20 = tl.load(in_ptr0 + (63 + (64*x1)), tmp17, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + (62 + (64*x1)), tmp17, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp8
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp17, tmp23, tmp24)
    tmp26 = tl.where(tmp15, tmp16, tmp25)
    tmp27 = tl.where(tmp4, tmp11, tmp26)
    tl.store(out_ptr0 + (x2), tmp27, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/je/cjeo7qorxupmmefggd6adcalocz2e73k5vsn5b22yzaqjmvjorqf.py
# Topologically Sorted Source Nodes: [diff_y_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   diff_y_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul_2, %slice_39, %mul_3], 2), 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=[16384], 
    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': 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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 64
    x0 = xindex % 64
    x2 = (xindex // 4096)
    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 + (64 + x0 + (4096*x2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (x0 + (4096*x2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 63, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr1 + (64 + x0 + (64*((-1) + x1)) + (4096*x2)), tmp15, other=0.0)
    tmp17 = tmp0 >= tmp13
    tmp18 = tl.full([1], 64, tl.int64)
    tmp19 = tmp0 < tmp18
    tmp20 = tl.load(in_ptr0 + (4032 + x0 + (4096*x2)), tmp17, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + (3968 + x0 + (4096*x2)), tmp17, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp8
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp17, tmp23, tmp24)
    tmp26 = tl.where(tmp15, tmp16, tmp25)
    tmp27 = tl.where(tmp4, tmp11, tmp26)
    tl.store(out_ptr0 + (x3), tmp27, None)
''', 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, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 1, 1, 3), (3, 3, 3, 1))
    assert_size_stride(primals_3, (1, 1, 3, 1), (3, 3, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [diff_x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 64, 64), (4096, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [diff_y], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 1), padding=(1, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf2 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [diff_x_1], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, buf0, buf2, 16384, grid=grid(16384), stream=stream0)
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [diff_y_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf1, buf3, 16384, grid=grid(16384), stream=stream0)
        del buf1
    return (buf2, buf3, primals_1, primals_2, primals_3, )


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, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 1, 1, 3), (3, 3, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 1, 3, 1), (3, 3, 1, 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.nn as nn


class Centered_Grad(nn.Module):

    def __init__(self):
        super(Centered_Grad, self).__init__()
        self.x_ker_init = torch.tensor([[[[-0.5, 0, 0.5]]]], dtype=torch.
            float, requires_grad=True)
        self.y_ker_init = torch.tensor([[[[-0.5], [0], [0.5]]]], dtype=
            torch.float, requires_grad=True)
        self.center_conv_x = nn.Conv2d(1, 1, (1, 3), padding=(0, 1), bias=False
            )
        self.center_conv_x.weight.data = self.x_ker_init
        self.center_conv_y = nn.Conv2d(1, 1, (3, 1), padding=(1, 0), bias=False
            )
        self.center_conv_y.weight.data = self.y_ker_init

    def forward(self, x):
        assert len(x.shape) == 4
        diff_x = self.center_conv_x(x)
        diff_y = self.center_conv_y(x)
        first_col = 0.5 * (x[:, :, :, 1:2] - x[:, :, :, 0:1])
        last_col = 0.5 * (x[:, :, :, -1:] - x[:, :, :, -2:-1])
        diff_x_valid = diff_x[:, :, :, 1:-1]
        diff_x = torch.cat((first_col, diff_x_valid, last_col), 3)
        first_row = 0.5 * (x[:, :, 1:2, :] - x[:, :, 0:1, :])
        last_row = 0.5 * (x[:, :, -1:, :] - x[:, :, -2:-1, :])
        diff_y_valid = diff_y[:, :, 1:-1, :]
        diff_y = torch.cat((first_row, diff_y_valid, last_row), 2)
        return diff_x, diff_y


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


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
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (1 + 64 * x1), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + 64 * x1, tmp4, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 63, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr1 + (1 + 64 * x1 + (-1 + x0)), tmp15,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp0 >= tmp13
    tl.full([1], 64, tl.int64)
    tmp20 = tl.load(in_ptr0 + (63 + 64 * x1), tmp17, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + (62 + 64 * x1), tmp17, eviction_policy=
        'evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp8
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp17, tmp23, tmp24)
    tmp26 = tl.where(tmp15, tmp16, tmp25)
    tmp27 = tl.where(tmp4, tmp11, tmp26)
    tl.store(out_ptr0 + x2, tmp27, None)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 64
    x0 = xindex % 64
    x2 = xindex // 4096
    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 + (64 + x0 + 4096 * x2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (x0 + 4096 * x2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 63, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr1 + (64 + x0 + 64 * (-1 + x1) + 4096 * x2), tmp15,
        other=0.0)
    tmp17 = tmp0 >= tmp13
    tl.full([1], 64, tl.int64)
    tmp20 = tl.load(in_ptr0 + (4032 + x0 + 4096 * x2), tmp17,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr0 + (3968 + x0 + 4096 * x2), tmp17,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp8
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp17, tmp23, tmp24)
    tmp26 = tl.where(tmp15, tmp16, tmp25)
    tmp27 = tl.where(tmp4, tmp11, tmp26)
    tl.store(out_ptr0 + x3, tmp27, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 1, 1, 3), (3, 3, 3, 1))
    assert_size_stride(primals_3, (1, 1, 3, 1), (3, 3, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 
            1), padding=(1, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf2 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(16384)](primals_1, buf0, buf2, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = buf0
        del buf0
        triton_poi_fused_cat_1[grid(16384)](primals_1, buf1, buf3, 16384,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return buf2, buf3, primals_1, primals_2, primals_3


class Centered_GradNew(nn.Module):

    def __init__(self):
        super(Centered_GradNew, self).__init__()
        self.x_ker_init = torch.tensor([[[[-0.5, 0, 0.5]]]], dtype=torch.
            float, requires_grad=True)
        self.y_ker_init = torch.tensor([[[[-0.5], [0], [0.5]]]], dtype=
            torch.float, requires_grad=True)
        self.center_conv_x = nn.Conv2d(1, 1, (1, 3), padding=(0, 1), bias=False
            )
        self.center_conv_x.weight.data = self.x_ker_init
        self.center_conv_y = nn.Conv2d(1, 1, (3, 1), padding=(1, 0), bias=False
            )
        self.center_conv_y.weight.data = self.y_ker_init

    def forward(self, input_0):
        primals_2 = self.center_conv_x.weight
        primals_3 = self.center_conv_y.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
