# 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/vj/cvjnharxtp7htz2vy2wxxsu3ttn5s2srhtb6wqyqzyxuya3jefs6.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   pad => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%arg0_1, [4, 4, 4, 4], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[4096], 
    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_constant_pad_nd_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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 12) % 12
    x0 = xindex % 12
    x2 = (xindex // 144)
    x4 = xindex
    tmp0 = (-4) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-4) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-20) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp11, 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, 12, 12), (576, 144, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(arg0_1, buf0, 2304, grid=grid(2304), stream=stream0)
        del arg0_1
    return (buf0, )


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


def mixed_pad(input, pad, mode='constant', value=0, reversed_axes=False):
    """Mixed mode padding.

       :type input: tensor[B,C,D1,D2,...,DD]
       :type pad: int or tuple of ints with 2*D length
       :type mode: str or tuple
       :type value: float or tuple

       Dimension numbering: reverse order means [B,C,Dn,...,D2,D1],
       while regular order means [B,C,D1,D2,...Dn].

       If the mode is a string, everything falls back to classic padding.
       If the mode is a tuple, for each dimension the given padding is used.
       The input tensor must have D+2 dimensions (batch, channel, D1,D2,...)
       The padding must be integer or (left, right) padding for each dimension.
       If the padding value is not a single float, it must be a sequence
       with length of D always. E.g. the value '1' isn't used but required:
          mixed_pad(t, pad=(1,1,2,2,1,1),
                       mode=('circular','constant', 'constant'),
                       value = (1,2,3))

    """
    D = input.ndim - 2
    if not isinstance(pad, tuple):
        pad = (pad, pad) * D
    if not isinstance(mode, tuple):
        return torch.nn.functional.pad(input, pad, mode=mode, value=value)
    if not reversed_axes:
        pad = pad[::-1]
        mode = mode[::-1]
    assert len(mode) == D
    if not isinstance(value, tuple):
        value = (value,) * D
    zero = (0, 0) * D
    result = input
    for i in range(D):
        pre = zero[0:2 * i]
        mid = pad[2 * i:2 * i + 2]
        post = zero[2 * i + 2:]
        sequence = pre + mid + post
        if mode[i] == 'constant':
            result = torch.nn.functional.pad(result, sequence, mode=
                'constant', value=value[i])
        else:
            result = torch.nn.functional.pad(result, sequence, mode=mode[i])
    return result


class MixedPad(torch.nn.Module):

    def __init__(self, pad, mode='constant', value=0, reversed_axes=False):
        super().__init__()
        self.pad = pad
        self.mode = mode
        self.value = value
        self.reversed_axes = reversed_axes

    def forward(self, x):
        return mixed_pad(x, self.pad, self.mode, self.value, self.reversed_axes
            )


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


def get_init_inputs():
    return [[], {'pad': 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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 12 % 12
    x0 = xindex % 12
    x2 = xindex // 144
    x4 = xindex
    tmp0 = -4 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -4 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-20 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, 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, 12, 12), (576, 144, 12, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(2304)](arg0_1, buf0, 2304,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def mixed_pad(input, pad, mode='constant', value=0, reversed_axes=False):
    """Mixed mode padding.

       :type input: tensor[B,C,D1,D2,...,DD]
       :type pad: int or tuple of ints with 2*D length
       :type mode: str or tuple
       :type value: float or tuple

       Dimension numbering: reverse order means [B,C,Dn,...,D2,D1],
       while regular order means [B,C,D1,D2,...Dn].

       If the mode is a string, everything falls back to classic padding.
       If the mode is a tuple, for each dimension the given padding is used.
       The input tensor must have D+2 dimensions (batch, channel, D1,D2,...)
       The padding must be integer or (left, right) padding for each dimension.
       If the padding value is not a single float, it must be a sequence
       with length of D always. E.g. the value '1' isn't used but required:
          mixed_pad(t, pad=(1,1,2,2,1,1),
                       mode=('circular','constant', 'constant'),
                       value = (1,2,3))

    """
    D = input.ndim - 2
    if not isinstance(pad, tuple):
        pad = (pad, pad) * D
    if not isinstance(mode, tuple):
        return torch.nn.functional.pad(input, pad, mode=mode, value=value)
    if not reversed_axes:
        pad = pad[::-1]
        mode = mode[::-1]
    assert len(mode) == D
    if not isinstance(value, tuple):
        value = (value,) * D
    zero = (0, 0) * D
    result = input
    for i in range(D):
        pre = zero[0:2 * i]
        mid = pad[2 * i:2 * i + 2]
        post = zero[2 * i + 2:]
        sequence = pre + mid + post
        if mode[i] == 'constant':
            result = torch.nn.functional.pad(result, sequence, mode=
                'constant', value=value[i])
        else:
            result = torch.nn.functional.pad(result, sequence, mode=mode[i])
    return result


class MixedPadNew(torch.nn.Module):

    def __init__(self, pad, mode='constant', value=0, reversed_axes=False):
        super().__init__()
        self.pad = pad
        self.mode = mode
        self.value = value
        self.reversed_axes = reversed_axes

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