# AOT ID: ['0_forward']
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_1/inductor_cache/yh/cyhi44ojre4kwa5u32igv2uvvqurj3osagpq3uhqwe4n27jwosbr.py
# Topologically Sorted Source Nodes: [padded_fm_t0], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   padded_fm_t0 => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=3] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%getitem, [0, 0, 0, 0], 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (128*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xx/cxxfvjz6ub4yvxjqyaq362yd4rdgkxqh2n7kb2kmfkmn5uk3srln.py
# Topologically Sorted Source Nodes: [k_out_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   k_out_2 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%add, %add_1], 1), 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 4, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr0 + (32 + x0 + (16*((-2) + x1)) + (64*x2)), tmp10 & xmask, other=0.0)
    tmp14 = tl.load(in_ptr2 + ((-2) + x1), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/m7/cm7kmz27xw2d6dxn76ng23te6qi4myssyw6tmikynavr6iezyoji.py
# Topologically Sorted Source Nodes: [mul, inter_out], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   inter_out => sum_1
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_2), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
triton_poi_fused_mul_sum_2 = async_compile.triton('triton_poi_fused_mul_sum_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zy/czylnpu55pkqbsyty6d6yczn3ra67tlxw4xeo4s53pzbhdkdgw55.py
# Topologically Sorted Source Nodes: [v_out_1, einsum], Original ATen: [aten.unfold, aten.mul]
# Source node to ATen node mapping:
#   einsum => mul_1
#   v_out_1 => unfold_3
# Graph fragment:
#   %unfold_3 : [num_users=2] = call_function[target=torch.ops.aten.unfold.default](args = (%unfold_2, 3, 1, 1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %permute_1), kwargs = {})
triton_poi_fused_mul_unfold_3 = async_compile.triton('triton_poi_fused_mul_unfold_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_unfold_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_unfold_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 16
    x3 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x1 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp4 * tmp0
    tl.store(in_out_ptr0 + (x0), tmp0, xmask)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1))
    assert_size_stride(primals_6, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [padded_fm_t0], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [q_out], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 4, 4, 4), (128, 16, 4, 1), 64), primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [k_out], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [v_out], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_out_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf2, primals_5, primals_6, buf4, 256, grid=grid(256), stream=stream0)
        del primals_5
        del primals_6
        buf6 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, inter_out], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_2.run(buf1, buf4, buf6, 64, grid=grid(64), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 4, 1), 0); del buf3  # reuse
        buf7 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4, 1), (64, 64, 16, 4, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [v_out_1, einsum], Original ATen: [aten.unfold, aten.mul]
        triton_poi_fused_mul_unfold_3.run(buf5, buf6, buf7, 256, grid=grid(256), stream=stream0)
        del buf6
    return (reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_2, primals_3, primals_4, reinterpret_tensor(primals_1, (4, 4, 4, 4), (128, 16, 4, 1), 64), buf0, buf1, buf4, buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 8, 4, 4), (128, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, 1, 1, 1, 1), (1, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2, 1, 1, 1, 1), (1, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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
import torch.nn.functional as F
import torch.nn.init as init


class Temporal_Attention(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=0, groups=1, bias=False, refinement=False):
        super(Temporal_Attention, self).__init__()
        self.outc = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.groups = groups
        self.refinement = refinement
        None
        if self.refinement:
            None
        assert self.outc % self.groups == 0, 'out_channels should be divided by groups.'
        self.w_q = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.w_k = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.w_v = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.rel_h = nn.Parameter(torch.randn(self.outc // 2, 1, 1, self.
            kernel_size, 1), requires_grad=True)
        self.rel_w = nn.Parameter(torch.randn(self.outc // 2, 1, 1, 1, self
            .kernel_size), requires_grad=True)
        init.normal_(self.rel_h, 0, 1)
        init.normal_(self.rel_w, 0, 1)
        init.kaiming_normal_(self.w_q.weight, mode='fan_out', nonlinearity=
            'relu')
        init.kaiming_normal_(self.w_k.weight, mode='fan_out', nonlinearity=
            'relu')
        init.kaiming_normal_(self.w_v.weight, mode='fan_out', nonlinearity=
            'relu')

    def forward(self, feature_map):
        fm_t0, fm_t1 = torch.split(feature_map, feature_map.size()[1] // 2, 1)
        assert fm_t0.size() == fm_t1.size(
            ), 'The size of feature maps of image t0 and t1 should be same.'
        batch, _, h, w = fm_t0.size()
        padded_fm_t0 = F.pad(fm_t0, [self.padding, self.padding, self.
            padding, self.padding])
        q_out = self.w_q(fm_t1)
        k_out = self.w_k(padded_fm_t0)
        v_out = self.w_v(padded_fm_t0)
        if self.refinement:
            padding = self.kernel_size
            padded_fm_col = F.pad(fm_t0, [0, 0, padding, padding])
            padded_fm_row = F.pad(fm_t0, [padding, padding, 0, 0])
            k_out_col = self.w_k(padded_fm_col)
            k_out_row = self.w_k(padded_fm_row)
            v_out_col = self.w_v(padded_fm_col)
            v_out_row = self.w_v(padded_fm_row)
            k_out_col = k_out_col.unfold(2, self.kernel_size * 2 + 1, self.
                stride)
            k_out_row = k_out_row.unfold(3, self.kernel_size * 2 + 1, self.
                stride)
            v_out_col = v_out_col.unfold(2, self.kernel_size * 2 + 1, self.
                stride)
            v_out_row = v_out_row.unfold(3, self.kernel_size * 2 + 1, self.
                stride)
        q_out_base = q_out.view(batch, self.groups, self.outc // self.
            groups, h, w, 1).repeat(1, 1, 1, 1, 1, self.kernel_size * self.
            kernel_size)
        q_out_ref = q_out.view(batch, self.groups, self.outc // self.groups,
            h, w, 1).repeat(1, 1, 1, 1, 1, self.kernel_size * 2 + 1)
        k_out = k_out.unfold(2, self.kernel_size, self.stride).unfold(3,
            self.kernel_size, self.stride)
        k_out_h, k_out_w = k_out.split(self.outc // 2, dim=1)
        k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1)
        k_out = k_out.contiguous().view(batch, self.groups, self.outc //
            self.groups, h, w, -1)
        v_out = v_out.unfold(2, self.kernel_size, self.stride).unfold(3,
            self.kernel_size, self.stride)
        v_out = v_out.contiguous().view(batch, self.groups, self.outc //
            self.groups, h, w, -1)
        inter_out = (q_out_base * k_out).sum(dim=2)
        out = F.softmax(inter_out, dim=-1)
        out = torch.einsum('bnhwk,bnchwk -> bnchw', out, v_out).contiguous(
            ).view(batch, -1, h, w)
        if self.refinement:
            k_out_row = k_out_row.contiguous().view(batch, self.groups, 
                self.outc // self.groups, h, w, -1)
            k_out_col = k_out_col.contiguous().view(batch, self.groups, 
                self.outc // self.groups, h, w, -1)
            v_out_row = v_out_row.contiguous().view(batch, self.groups, 
                self.outc // self.groups, h, w, -1)
            v_out_col = v_out_col.contiguous().view(batch, self.groups, 
                self.outc // self.groups, h, w, -1)
            out_row = F.softmax((q_out_ref * k_out_row).sum(dim=2), dim=-1)
            out_col = F.softmax((q_out_ref * k_out_col).sum(dim=2), dim=-1)
            out += torch.einsum('bnhwk,bnchwk -> bnchw', out_row, v_out_row
                ).contiguous().view(batch, -1, h, w)
            out += torch.einsum('bnhwk,bnchwk -> bnchw', out_col, v_out_col
                ).contiguous().view(batch, -1, h, w)
        return out


def get_inputs():
    return [torch.rand([4, 8, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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 torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp13 = tl.load(in_ptr0 + (32 + x0 + 16 * (-2 + x1) + 64 * x2), tmp10 &
        xmask, other=0.0)
    tmp14 = tl.load(in_ptr2 + (-2 + x1), tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tl.store(out_ptr0 + x3, tmp18, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_mul_unfold_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 16
    x3 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (x1 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tmp5 = tmp4 * tmp0
    tl.store(in_out_ptr0 + x0, tmp0, xmask)
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1))
    assert_size_stride(primals_6, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(256)](primals_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            4, 4, 4), (128, 16, 4, 1), 64), primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(256)](buf2, primals_5, primals_6, buf4,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        del primals_6
        buf6 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64),
            torch.float32)
        triton_poi_fused_mul_sum_2[grid(64)](buf1, buf4, buf6, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 
            4, 1), 0)
        del buf3
        buf7 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4, 1), (64, 64, 16, 4,
            1, 1), 0)
        del buf2
        triton_poi_fused_mul_unfold_3[grid(256)](buf5, buf6, buf7, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
    return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_2, primals_3, primals_4, reinterpret_tensor(primals_1, (
        4, 4, 4, 4), (128, 16, 4, 1), 64), buf0, buf1, buf4, buf5


class Temporal_AttentionNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=0, groups=1, bias=False, refinement=False):
        super(Temporal_AttentionNew, self).__init__()
        self.outc = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.groups = groups
        self.refinement = refinement
        None
        if self.refinement:
            None
        assert self.outc % self.groups == 0, 'out_channels should be divided by groups.'
        self.w_q = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.w_k = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.w_v = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias
            =bias)
        self.rel_h = nn.Parameter(torch.randn(self.outc // 2, 1, 1, self.
            kernel_size, 1), requires_grad=True)
        self.rel_w = nn.Parameter(torch.randn(self.outc // 2, 1, 1, 1, self
            .kernel_size), requires_grad=True)
        init.normal_(self.rel_h, 0, 1)
        init.normal_(self.rel_w, 0, 1)
        init.kaiming_normal_(self.w_q.weight, mode='fan_out', nonlinearity=
            'relu')
        init.kaiming_normal_(self.w_k.weight, mode='fan_out', nonlinearity=
            'relu')
        init.kaiming_normal_(self.w_v.weight, mode='fan_out', nonlinearity=
            'relu')

    def forward(self, input_0):
        primals_5 = self.rel_h
        primals_6 = self.rel_w
        primals_2 = self.w_q.weight
        primals_3 = self.w_k.weight
        primals_4 = self.w_v.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
