# 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/ws/cws4sdopkhyearti52wlp2z4abgojsuab65psc2axw54r5kutrve.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   pad => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%arg0_1, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_3]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_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, 64], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', '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_reflection_pad2d_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12
    xnumel = 36
    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 % 6
    x3 = (xindex // 6)
    y4 = yindex
    x5 = xindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x2))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x3))))) + (16*y4)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x5) + (108*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/g3/cg3wkzl5cbgzh6yjo4jtvwnt27wvkgcha7liszb7ob7r3lh76xhv.py
# Topologically Sorted Source Nodes: [pad, conv2d], Original ATen: [aten.reflection_pad2d, aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
#   pad => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%arg0_1, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_3]), kwargs = {})
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %arg1_1, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_1 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_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=[4, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_1', '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_convolution_reflection_pad2d_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 3
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (x1 + (16*y0)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x1)), tmp0, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 3, 4, 4), (48, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 6, 6), (108, 1, 18, 3), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(arg0_1, buf0, 12, 36, grid=grid(12, 36), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((1, 3, 4, 4), (48, 1, 12, 3), torch.float32)
        # Topologically Sorted Source Nodes: [pad, conv2d], Original ATen: [aten.reflection_pad2d, aten.convolution]
        triton_poi_fused_convolution_reflection_pad2d_1.run(arg1_1, buf1, 3, 16, grid=grid(3, 16), stream=stream0)
        del arg1_1
        # Topologically Sorted Source Nodes: [pad, conv2d], Original ATen: [aten.reflection_pad2d, aten.convolution]
        buf2 = extern_kernels.convolution(buf0, buf1, 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, 3, 3), (9, 1, 3, 1))
        del buf0
        del buf1
    return (buf2, )


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, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((1, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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
import torch.nn as nn
import torch.nn.functional as F


class FixedBlurLayer(nn.Module):

    def __init__(self, kernel):
        super(FixedBlurLayer, self).__init__()
        self.kernel = kernel
        to_pad_x = int((self.kernel.shape[0] - 1) / 2)
        to_pad_y = int((self.kernel.shape[1] - 1) / 2)
        self.pad = nn.ReflectionPad2d((to_pad_x, to_pad_x, to_pad_y, to_pad_y))
        self.mask_np = np.zeros(shape=(1, 3, self.kernel.shape[0], self.
            kernel.shape[1]))
        self.mask_np[0, 0, :, :] = self.kernel
        self.mask_np[0, 1, :, :] = self.kernel
        self.mask_np[0, 2, :, :] = self.kernel
        self.mask = nn.Parameter(data=torch.FloatTensor(self.mask_np),
            requires_grad=False)

    def forward(self, x):
        return F.conv2d(self.pad(x), self.mask)


def get_inputs():
    return [torch.rand([4, 3, 4, 4])]


def get_init_inputs():
    return [[], {'kernel': torch.rand([4, 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.triton_helpers import math as tl_math
import numpy as np
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_reflection_pad2d_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    xnumel = 36
    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 % 6
    x3 = xindex // 6
    y4 = yindex
    x5 = xindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x2)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x3)) + 16 * y4), 
        xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x5 + 108 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_1(in_ptr0, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 3
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (x1 + 16 * y0), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x1), tmp0, xmask & ymask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 3, 4, 4), (48, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 6, 6), (108, 1, 18, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(12, 36)](arg0_1, buf0, 12,
            36, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((1, 3, 4, 4), (48, 1, 12, 3), torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_1[grid(3, 16)](arg1_1,
            buf1, 3, 16, XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
        del arg1_1
        buf2 = extern_kernels.convolution(buf0, buf1, 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, 3, 3), (9, 1, 3, 1))
        del buf0
        del buf1
    return buf2,


class FixedBlurLayerNew(nn.Module):

    def __init__(self, kernel):
        super(FixedBlurLayerNew, self).__init__()
        self.kernel = kernel
        to_pad_x = int((self.kernel.shape[0] - 1) / 2)
        to_pad_y = int((self.kernel.shape[1] - 1) / 2)
        self.pad = nn.ReflectionPad2d((to_pad_x, to_pad_x, to_pad_y, to_pad_y))
        self.mask_np = np.zeros(shape=(1, 3, self.kernel.shape[0], self.
            kernel.shape[1]))
        self.mask_np[0, 0, :, :] = self.kernel
        self.mask_np[0, 1, :, :] = self.kernel
        self.mask_np[0, 2, :, :] = self.kernel
        self.mask = nn.Parameter(data=torch.FloatTensor(self.mask_np),
            requires_grad=False)

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