# 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_7/inductor_cache/yu/cyu6oyysdpuqkq5k34tbyb2prxsfirwvz7teg6euxroe2cpoacjb.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.copy]
# Source node to ATen node mapping:
#   x => copy
# Graph fragment:
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_3, %slice_4), kwargs = {})
#   %slice_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor, %copy, 2, 1, 5), kwargs = {})
#   %slice_scatter_default_1 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%empty, %slice_scatter_default, 3, 0, 4), kwargs = {})
#   %slice_scatter_default_2 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_1, %slice_11, 2, 0, 1), kwargs = {})
#   %slice_scatter_default_3 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_2, %slice_16, 2, 5, 6), kwargs = {})
triton_poi_fused_copy_0 = async_compile.triton('triton_poi_fused_copy_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_copy_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 6
    x2 = (xindex // 24)
    x3 = xindex % 24
    x4 = xindex
    tmp38 = tl.load(in_ptr1 + (x4), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = (-4) + x1
    tmp4 = tl.full([1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tmp0 >= tmp4
    tmp8 = tmp0 < tmp1
    tmp9 = tmp7 & tmp8
    tmp10 = tmp9 & tmp6
    tmp11 = tl.load(in_ptr0 + ((-4) + x3 + (16*x2)), tmp10 & xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x4), tmp6 & xmask, other=0.0)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tmp3 >= tmp4
    tmp17 = tmp3 < tmp1
    tmp18 = tmp16 & tmp17
    tmp19 = tmp18 & tmp2
    tmp20 = tl.load(in_ptr0 + ((-20) + x3 + (16*x2)), tmp19 & xmask, other=0.0)
    tmp21 = tl.load(in_ptr1 + ((-16) + x4), tmp2 & xmask, other=0.0)
    tmp22 = tl.where(tmp18, tmp20, tmp21)
    tmp23 = tl.where(tmp5, tmp15, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = 4 + x1
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x3 + (16*x2)), tmp31 & xmask, other=0.0)
    tmp33 = tl.load(in_ptr1 + (16 + x4), tmp26 & xmask, other=0.0)
    tmp34 = tl.where(tmp30, tmp32, tmp33)
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp26, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + ((-4) + x3 + (16*x2)), tmp9 & xmask, other=0.0)
    tmp39 = tl.where(tmp9, tmp37, tmp38)
    tmp40 = tl.where(tmp26, tmp36, tmp39)
    tmp41 = tl.where(tmp2, tmp25, tmp40)
    tl.store(out_ptr0 + (x4), tmp41, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uz/cuzrirpm2rdhvffydfmkfje2kmzy3ztj3vjhhhy5koqgexvvo35y.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
#   x_1 => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%slice_scatter_default_3, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad2d_1 = async_compile.triton('triton_poi_fused_replication_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=[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_replication_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_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((5) * ((5) <= (x1)) + (x1) * ((x1) < (5)))) + (24*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, 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))
    buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused_copy_0.run(arg0_1, buf0, buf1, 384, grid=grid(384), stream=stream0)
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.replication_pad2d]
        triton_poi_fused_replication_pad2d_1.run(buf1, buf2, 576, grid=grid(576), stream=stream0)
        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, 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
from itertools import product as product
import torch.nn as nn


class MixPad2d(nn.Module):
    """Mixed padding modes for H and W dimensions

    Args:
        padding (tuple): the size of the padding for x and y, ie (pad_x, pad_y)
        modes (tuple): the padding modes for x and y, the values of each can be
            ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
            Default: ``['replicate', 'circular']``

    """
    __constants__ = ['modes', 'padding']

    def __init__(self, padding=[1, 1], modes=['replicate', 'circular']):
        super(MixPad2d, self).__init__()
        assert len(padding) == 2
        self.padding = padding
        self.modes = modes

    def forward(self, x):
        x = nn.functional.pad(x, (0, 0, self.padding[1], self.padding[1]),
            self.modes[1])
        x = nn.functional.pad(x, (self.padding[0], self.padding[0], 0, 0),
            self.modes[0])
        return x

    def extra_repr(self):
        repr_ = (
            'Mixed Padding: \t x axis: mode: {}, padding: {},\n\t y axis mode: {}, padding: {}'
            .format(self.modes[0], self.padding[0], self.modes[1], self.
            padding[1]))
        return repr_


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


def get_init_inputs():
    return [[], {}]

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
from itertools import product as product
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_copy_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x2 = xindex // 24
    x3 = xindex % 24
    x4 = xindex
    tmp38 = tl.load(in_ptr1 + x4, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = -4 + x1
    tmp4 = tl.full([1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tmp0 >= tmp4
    tmp8 = tmp0 < tmp1
    tmp9 = tmp7 & tmp8
    tmp10 = tmp9 & tmp6
    tmp11 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp10 & xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + x4, tmp6 & xmask, other=0.0)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tmp3 >= tmp4
    tmp17 = tmp3 < tmp1
    tmp18 = tmp16 & tmp17
    tmp19 = tmp18 & tmp2
    tmp20 = tl.load(in_ptr0 + (-20 + x3 + 16 * x2), tmp19 & xmask, other=0.0)
    tmp21 = tl.load(in_ptr1 + (-16 + x4), tmp2 & xmask, other=0.0)
    tmp22 = tl.where(tmp18, tmp20, tmp21)
    tmp23 = tl.where(tmp5, tmp15, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = 4 + x1
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x3 + 16 * x2), tmp31 & xmask, other=0.0)
    tmp33 = tl.load(in_ptr1 + (16 + x4), tmp26 & xmask, other=0.0)
    tmp34 = tl.where(tmp30, tmp32, tmp33)
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp26, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + (-4 + x3 + 16 * x2), tmp9 & xmask, other=0.0)
    tmp39 = tl.where(tmp9, tmp37, tmp38)
    tmp40 = tl.where(tmp26, tmp36, tmp39)
    tmp41 = tl.where(tmp2, tmp25, tmp40)
    tl.store(out_ptr0 + x4, tmp41, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (5 * (5 <= x1) + x1 * (x1 < 5)) + 24 * x2 +
        (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (
        0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + 
        (-1 + x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_copy_0[grid(384)](arg0_1, buf0, buf1, 384, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_replication_pad2d_1[grid(576)](buf1, buf2, 576,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return buf2,


class MixPad2dNew(nn.Module):
    """Mixed padding modes for H and W dimensions

    Args:
        padding (tuple): the size of the padding for x and y, ie (pad_x, pad_y)
        modes (tuple): the padding modes for x and y, the values of each can be
            ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
            Default: ``['replicate', 'circular']``

    """
    __constants__ = ['modes', 'padding']

    def __init__(self, padding=[1, 1], modes=['replicate', 'circular']):
        super(MixPad2dNew, self).__init__()
        assert len(padding) == 2
        self.padding = padding
        self.modes = modes

    def extra_repr(self):
        repr_ = (
            'Mixed Padding: \t x axis: mode: {}, padding: {},\n\t y axis mode: {}, padding: {}'
            .format(self.modes[0], self.padding[0], self.modes[1], self.
            padding[1]))
        return repr_

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