# 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/ac/cactp4raijeuupgkf72atpb5dqx2m7nq5k7fpzjavsrr2lquv6jf.py
# Topologically Sorted Source Nodes: [specgram_1], Original ATen: [aten.replication_pad1d]
# Source node to ATen node mapping:
#   specgram_1 => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%view, [None, None, %clamp_max]), kwargs = {})
triton_poi_fused_replication_pad1d_0 = async_compile.triton('triton_poi_fused_replication_pad1d_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: '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_pad1d_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_replication_pad1d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*(x1 % 4)) + (16*(x1 // 16)) + (64*((x1 // 4) % 4)) + ((3) * ((3) <= (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0))))) + (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) * ((((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ns/cnsqxcdmqckv2b4hbcx2tgslbei3i2eta37z6mobhzwxtt56do2q.py
# Topologically Sorted Source Nodes: [arange, kernel], Original ATen: [aten.arange, aten.repeat]
# Source node to ATen node mapping:
#   arange => add, convert_element_type, iota_1, mul
#   kernel => repeat
# Graph fragment:
#   %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (5,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_1, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, -2), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%convert_element_type, [64, 1, 1]), kwargs = {})
triton_poi_fused_arange_repeat_1 = async_compile.triton('triton_poi_fused_arange_repeat_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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_arange_repeat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_arange_repeat_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x2 = xindex
    tmp0 = (-2) + x0
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x2), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7o/c7o6lz3z3wc2hctevxaw5a7vifalwi3bccdwtwzi62tq4djyl2kg.py
# Topologically Sorted Source Nodes: [specgram_3], Original ATen: [aten.replication_pad1d]
# Source node to ATen node mapping:
#   specgram_3 => _unsafe_index_1
# Graph fragment:
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%view_2, [None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad1d_2 = async_compile.triton('triton_poi_fused_replication_pad1d_2', '''
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: '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_pad1d_2', '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_pad1d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*x1) + ((3) * ((3) <= (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0))))) + (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) * ((((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tmp1 = 0.1
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/i7/ci7zzwpyjyzmhwbdzexdktbiadv5a4qpgn2zumseiqxejm7orwxo.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%arg0_1, %permute_1, %permute_3], -1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', '''
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: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x4 = (xindex // 12)
    x1 = (xindex // 12) % 4
    x2 = (xindex // 48) % 4
    x3 = (xindex // 192)
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x4) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + (16*x3) + (64*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = 0.1
    tmp12 = tmp10 * tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 12, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr2 + ((4*x1) + (16*x3) + (64*x2) + ((-8) + x0)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 * tmp11
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp15, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp14, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + (x5), tmp23, 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((1, 64, 8), (512, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [specgram_1], Original ATen: [aten.replication_pad1d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_replication_pad1d_0.run(arg0_1, buf0, 512, grid=grid(512), stream=stream0)
        buf1 = empty_strided_cuda((64, 1, 5), (5, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [arange, kernel], Original ATen: [aten.arange, aten.repeat]
        triton_poi_fused_arange_repeat_1.run(buf1, 320, grid=grid(320), stream=stream0)
        # Topologically Sorted Source Nodes: [specgram_1, arange, kernel, conv1d], Original ATen: [aten.replication_pad1d, aten.arange, aten.repeat, aten.convolution]
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=64, bias=None)
        assert_size_stride(buf2, (1, 64, 4), (256, 4, 1))
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [specgram_3], Original ATen: [aten.replication_pad1d]
        triton_poi_fused_replication_pad1d_2.run(buf2, buf3, 512, grid=grid(512), stream=stream0)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [arange_1, kernel_1], Original ATen: [aten.arange, aten.repeat]
        triton_poi_fused_arange_repeat_1.run(buf4, 320, grid=grid(320), stream=stream0)
        # Topologically Sorted Source Nodes: [specgram_3, arange_1, kernel_1, conv1d_1], Original ATen: [aten.replication_pad1d, aten.arange, aten.repeat, aten.convolution]
        buf5 = extern_kernels.convolution(buf3, buf4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=64, bias=None)
        assert_size_stride(buf5, (1, 64, 4), (256, 4, 1))
        del buf3
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(arg0_1, buf2, buf5, buf6, 768, grid=grid(768), stream=stream0)
        del arg0_1
        del buf2
        del buf5
    return (buf6, )


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
import torch.nn as nn
from torchaudio import transforms


class Delta(nn.Module):

    def __init__(self, order=2, **kwargs):
        super(Delta, self).__init__()
        self.order = order
        self.compute_delta = transforms.ComputeDeltas(**kwargs)

    def forward(self, x):
        feats = [x]
        for o in range(self.order):
            feat = feats[-1].transpose(0, 1).unsqueeze(0)
            delta = self.compute_delta(feat)
            feats.append(delta.squeeze(0).transpose(0, 1))
        x = torch.cat(feats, dim=-1)
        return x


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


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
from torchaudio import transforms
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_replication_pad1d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (x1 % 4) + 16 * (x1 // 16) + 64 * (x1 // 
        4 % 4) + (3 * (3 <= 0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) +
        (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) * (0 * (0 >= -2 +
        x0) + (-2 + x0) * (-2 + x0 > 0) < 3))), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_arange_repeat_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x2 = xindex
    tmp0 = -2 + x0
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp1, xmask)


@triton.jit
def triton_poi_fused_replication_pad1d_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + (3 * (3 <= 0 * (0 >= -2 + x0) + (-2 +
        x0) * (-2 + x0 > 0)) + (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 >
        0)) * (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0) < 3))), xmask,
        eviction_policy='evict_last')
    tmp1 = 0.1
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x4 = xindex // 12
    x1 = xindex // 12 % 4
    x2 = xindex // 48 % 4
    x3 = xindex // 192
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x4 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + 16 * x3 + 64 * x2 + (-4 + x0)), 
        tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = 0.1
    tmp12 = tmp10 * tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp18 = tl.load(in_ptr2 + (4 * x1 + 16 * x3 + 64 * x2 + (-8 + x0)), 
        tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 * tmp11
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp15, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp14, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + x5, tmp23, 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((1, 64, 8), (512, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad1d_0[grid(512)](arg0_1, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 1, 5), (5, 5, 1), torch.float32)
        triton_poi_fused_arange_repeat_1[grid(320)](buf1, 320, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(1,), padding=
            (0,), dilation=(1,), transposed=False, output_padding=(0,),
            groups=64, bias=None)
        assert_size_stride(buf2, (1, 64, 4), (256, 4, 1))
        buf3 = buf0
        del buf0
        triton_poi_fused_replication_pad1d_2[grid(512)](buf2, buf3, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = buf1
        del buf1
        triton_poi_fused_arange_repeat_1[grid(320)](buf4, 320, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf3, buf4, stride=(1,), padding=
            (0,), dilation=(1,), transposed=False, output_padding=(0,),
            groups=64, bias=None)
        assert_size_stride(buf5, (1, 64, 4), (256, 4, 1))
        del buf3
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(768)](arg0_1, buf2, buf5, buf6, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf2
        del buf5
    return buf6,


class DeltaNew(nn.Module):

    def __init__(self, order=2, **kwargs):
        super(DeltaNew, self).__init__()
        self.order = order
        self.compute_delta = transforms.ComputeDeltas(**kwargs)

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