# 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/fo/cfohcgdet6wflivfp6hbudmzhk6yw2j2czym6kwy2yiif6mh3avd.py
# Topologically Sorted Source Nodes: [ins], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   ins => cat
# Graph fragment:
#   %cat : [num_users=7] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_12, %slice_20, %slice_28, %arg0_1, %slice_32, %slice_24, %slice_16], 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=[1024], 
    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_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = (xindex // 10)
    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 + (3 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (2 + (4*x1)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (1 + (4*x1)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 7, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + ((4*x1) + ((-3) + x0)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (2 + (4*x1)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp0 >= tmp22
    tmp27 = tl.full([1], 9, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tl.load(in_ptr0 + (1 + (4*x1)), tmp29 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp0 >= tmp27
    tmp32 = tl.full([1], 10, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tl.load(in_ptr0 + (4*x1), tmp31 & xmask, eviction_policy='evict_last', other=0.0)
    tmp35 = tl.where(tmp29, tmp30, tmp34)
    tmp36 = tl.where(tmp24, tmp25, tmp35)
    tmp37 = tl.where(tmp19, tmp20, tmp36)
    tmp38 = tl.where(tmp14, tmp15, tmp37)
    tmp39 = tl.where(tmp9, tmp10, tmp38)
    tmp40 = tl.where(tmp4, tmp5, tmp39)
    tl.store(out_ptr0 + (x2), tmp40, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lh/clh5qhodzshgdlwsocwfpewyjsmigbgl4r4ofkh3zy7nqeug2s6a.py
# Topologically Sorted Source Nodes: [ins_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   ins_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_43, %slice_51, %slice_59, %cat, %slice_63, %slice_55, %slice_47], 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=[2048], 
    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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 10) % 10
    x0 = xindex % 10
    x2 = (xindex // 100)
    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 + (30 + x0 + (40*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (20 + x0 + (40*x2)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (10 + x0 + (40*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 7, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (x0 + (10*((-3) + x1)) + (40*x2)), tmp19 & xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (20 + x0 + (40*x2)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp0 >= tmp22
    tmp27 = tl.full([1], 9, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tl.load(in_ptr0 + (10 + x0 + (40*x2)), tmp29 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp0 >= tmp27
    tmp32 = tl.full([1], 10, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tl.load(in_ptr0 + (x0 + (40*x2)), tmp31 & xmask, eviction_policy='evict_last', other=0.0)
    tmp35 = tl.where(tmp29, tmp30, tmp34)
    tmp36 = tl.where(tmp24, tmp25, tmp35)
    tmp37 = tl.where(tmp19, tmp20, tmp36)
    tmp38 = tl.where(tmp14, tmp15, tmp37)
    tmp39 = tl.where(tmp9, tmp10, tmp38)
    tmp40 = tl.where(tmp4, tmp5, tmp39)
    tl.store(out_ptr0 + (x3), tmp40, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ins], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 640, grid=grid(640), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ins_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf0, buf1, 1600, grid=grid(1600), stream=stream0)
        del buf0
    return (buf1, )


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)
    fn = lambda: call([arg0_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


class ReflectPad2d(torch.nn.Module):
    """ reflectionpad2d that can be transfered across onnx etc
        size : int (the size of padding)
    """

    def __init__(self, size):
        super().__init__()
        self.size = size

    def forward(self, ins):
        size = self.size
        l_list, r_list = [], []
        u_list, d_list = [], []
        for i in range(size):
            left = ins[:, :, :, size - i:size - i + 1]
            l_list.append(left)
            right = ins[:, :, :, i - size - 1:i - size]
            r_list.append(right)
        l_list.append(ins)
        ins = torch.cat(l_list + r_list[::-1], dim=3)
        for i in range(size):
            up = ins[:, :, size - i:size - i + 1, :]
            u_list.append(up)
            down = ins[:, :, i - size - 1:i - size, :]
            d_list.append(down)
        u_list.append(ins)
        ins = torch.cat(u_list + d_list[::-1], dim=2)
        return ins


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


def get_init_inputs():
    return [[], {'size': 4}]

import torch
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
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, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10
    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 + (3 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (1 + 4 * x1), tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 7, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (4 * x1 + (-3 + x0)), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (2 + 4 * x1), tmp24 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp0 >= tmp22
    tmp27 = tl.full([1], 9, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tl.load(in_ptr0 + (1 + 4 * x1), tmp29 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp31 = tmp0 >= tmp27
    tl.full([1], 10, tl.int64)
    tmp34 = tl.load(in_ptr0 + 4 * x1, tmp31 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp35 = tl.where(tmp29, tmp30, tmp34)
    tmp36 = tl.where(tmp24, tmp25, tmp35)
    tmp37 = tl.where(tmp19, tmp20, tmp36)
    tmp38 = tl.where(tmp14, tmp15, tmp37)
    tmp39 = tl.where(tmp9, tmp10, tmp38)
    tmp40 = tl.where(tmp4, tmp5, tmp39)
    tl.store(out_ptr0 + x2, tmp40, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 10 % 10
    x0 = xindex % 10
    x2 = xindex // 100
    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 + (30 + x0 + 40 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (20 + x0 + 40 * x2), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (10 + x0 + 40 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 7, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (x0 + 10 * (-3 + x1) + 40 * x2), tmp19 &
        xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (20 + x0 + 40 * x2), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp26 = tmp0 >= tmp22
    tmp27 = tl.full([1], 9, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tl.load(in_ptr0 + (10 + x0 + 40 * x2), tmp29 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp31 = tmp0 >= tmp27
    tl.full([1], 10, tl.int64)
    tmp34 = tl.load(in_ptr0 + (x0 + 40 * x2), tmp31 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp35 = tl.where(tmp29, tmp30, tmp34)
    tmp36 = tl.where(tmp24, tmp25, tmp35)
    tmp37 = tl.where(tmp19, tmp20, tmp36)
    tmp38 = tl.where(tmp14, tmp15, tmp37)
    tmp39 = tl.where(tmp9, tmp10, tmp38)
    tmp40 = tl.where(tmp4, tmp5, tmp39)
    tl.store(out_ptr0 + x3, tmp40, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(640)](arg0_1, buf0, 640, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
            float32)
        triton_poi_fused_cat_1[grid(1600)](buf0, buf1, 1600, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf0
    return buf1,


class ReflectPad2dNew(torch.nn.Module):
    """ reflectionpad2d that can be transfered across onnx etc
        size : int (the size of padding)
    """

    def __init__(self, size):
        super().__init__()
        self.size = size

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