# 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_7/inductor_cache/6x/c6xdtz2w5en5fsdbgutlchlfsh4q7a2byarfiaglzh45nn222wce.py
# Topologically Sorted Source Nodes: [unfold], Original ATen: [aten.im2col]
# Source node to ATen node mapping:
#   unfold => add
# Graph fragment:
#   %add : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
triton_poi_fused_im2col_0 = async_compile.triton('triton_poi_fused_im2col_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_im2col_0', '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_im2col_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = x0 + x1
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7h/c7howbh27c6wmleecf2uzap4cbx7ucljylpyhqy6ghbnqufvr5po.py
# Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   avg_pool2d => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%permute_4, [1, 1], [1, 1], [0, 0], True), kwargs = {})
triton_poi_fused_avg_pool2d_1 = async_compile.triton('triton_poi_fused_avg_pool2d_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: '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_avg_pool2d_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_avg_pool2d_1(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kf/ckffsfrpbgm36wdcy3msjdhmjfplumvvqv7gqiishdz4ksyk4nl7.py
# Topologically Sorted Source Nodes: [attn_2, attn_3], Original ATen: [aten.mul, aten._softmax]
# Source node to ATen node mapping:
#   attn_2 => mul
#   attn_3 => amax, clone_1, div, exp, sub, sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_7, 1.0), kwargs = {})
#   %clone_1 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%mul,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_mul_2 = async_compile.triton('triton_per_fused__softmax_mul_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.persistent_reduction(
    size_hints=[4096, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_mul_2(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 2304
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r2 = rindex
    x7 = xindex
    x0 = xindex % 36
    x3 = xindex % 9
    x4 = (xindex // 9) % 4
    x5 = (xindex // 36) % 16
    x6 = (xindex // 576)
    tmp0 = tl.load(in_ptr0 + (r2 + (9*x7)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + (9*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp5, float("-inf"))
    tmp8 = triton_helpers.max2(tmp7, 1)[:, None]
    tmp9 = tmp4 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r2 + (9*x3) + (81*x5) + (1312*x4) + (5248*x6)), tmp15, rmask & xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5r/c5rssi353wkd2scirr2i3cdjitakcddf5xxkaiysfvnfwz4wbx4l.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 9
    x1 = (xindex // 9) % 16
    x2 = (xindex // 144) % 4
    x3 = (xindex // 576)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*(x0 // 3)) + (x1 // 4)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + ((4*(x0 % 3)) + (x1 % 4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 6, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 6)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 6")
    tmp7 = tmp6 + tmp1
    tmp8 = tmp6 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp6)
    tl.device_assert(((0 <= tmp9) & (tmp9 < 6)) | ~(xmask), "index out of bounds: 0 <= tmp9 < 6")
    tmp11 = (-1) + tmp4
    tmp12 = tl.full([1], 0, tl.int64)
    tmp13 = tmp11 >= tmp12
    tmp14 = tl.full([1], 4, tl.int64)
    tmp15 = tmp11 < tmp14
    tmp16 = (-1) + tmp9
    tmp17 = tmp16 >= tmp12
    tmp18 = tmp16 < tmp14
    tmp19 = tmp13 & tmp15
    tmp20 = tmp19 & tmp17
    tmp21 = tmp20 & tmp18
    tmp22 = tl.load(in_ptr1 + ((-20) + x2 + (4*tmp9) + (16*tmp4) + (64*x3)), tmp21 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x5), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ya/cyaau7d2v6nud7wiegjd72lx2uhv7wc6gc6x5o3epgg6odllcau7.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
# Source node to ATen node mapping:
#   matmul => bmm
# Graph fragment:
#   %bmm : [num_users=1] = call_function[target=torch.ops.aten.bmm.default](args = (%view_7, %view_8), kwargs = {})
triton_poi_fused_bmm_4 = async_compile.triton('triton_poi_fused_bmm_4', '''
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=[32768], 
    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_bmm_4', '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_bmm_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20736
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 81
    x1 = (xindex // 81)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (81*(x1 % 16)) + (1312*(x1 // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tv/ctvlqogy5r6ohjndwmx3qbdwvnnrjj2qm7iknoh6f6ckrtehwqir.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.col2im]
# Source node to ATen node mapping:
#   x_1 => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 6, 6], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_col2im_5 = async_compile.triton('triton_poi_fused_col2im_5', '''
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: '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_col2im_5', '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_col2im_5(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
    tmp0 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ih/cihl6moqjmq37jz7lm5yhuwfyf6mbpckcqjigxxaznjgffp3y5ca.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.col2im]
# Source node to ATen node mapping:
#   x_1 => index_put
# Graph fragment:
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%full_default, [None, None, %unsqueeze_5, %add], %permute_9, True), kwargs = {})
triton_poi_fused_col2im_6 = async_compile.triton('triton_poi_fused_col2im_6', '''
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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_col2im_6', 'mutated_arg_names': ['out_ptr0'], '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_col2im_6(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 576
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y5 = (yindex // 3) % 12
    x4 = xindex
    y0 = yindex % 3
    y1 = (yindex // 3) % 4
    y2 = (yindex // 12) % 3
    y3 = (yindex // 36)
    tmp0 = tl.load(in_ptr0 + (y5), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (x4 + (4*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (y0 + (3*y2) + (9*x4) + (36*y1) + (144*y3) + (144*((y0 + (3*y2)) // 9))), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK, YBLOCK], 6, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 6)) | ~(ymask), "index out of bounds: 0 <= tmp4 < 6")
    tmp7 = tmp6 + tmp1
    tmp8 = tmp6 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp6)
    tl.device_assert(((0 <= tmp9) & (tmp9 < 6)) | ~(xmask & ymask), "index out of bounds: 0 <= tmp9 < 6")
    tl.atomic_add(out_ptr0 + (tl.broadcast_to(tmp9 + (6*tmp4) + (36*y3), [XBLOCK, YBLOCK])), tmp11, xmask & ymask, sem='relaxed')
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6j/c6j6zr7wslvz3frvfzwveytngq4nfxv75gmqi2vju57tya4iykk7.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   x_2 => clone_5
# Graph fragment:
#   %clone_5 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_10,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_7 = async_compile.triton('triton_poi_fused_clone_7', '''
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, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_7', '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_clone_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y1 = (yindex // 4) % 4
    y0 = yindex % 4
    x3 = xindex
    y2 = (yindex // 16)
    y5 = yindex
    tmp0 = 1 + y1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 6, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 1 + y0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (7 + y0 + (6*y1) + (36*x3) + (144*y2)), tmp10 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x3 + (4*y5)), tmp11, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wv/cwvxucyxlsbx6r4eu4pwwxtgq2adykv2e5ulhy576dumppymjdrc.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_2 => add_4
# Graph fragment:
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %primals_6), kwargs = {})
triton_poi_fused_add_8 = async_compile.triton('triton_poi_fused_add_8', '''
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=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_add_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (324, 4), (4, 1))
    assert_size_stride(primals_4, (324, ), (1, ))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((3, 4), (4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [unfold], Original ATen: [aten.im2col]
        stream0 = get_raw_stream(0)
        triton_poi_fused_im2col_0.run(buf1, 12, grid=grid(12), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_1.run(primals_1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((64, 324), (324, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 324), (1, 4), 0), out=buf3)
        del primals_3
        buf6 = empty_strided_cuda((4, 4, 16, 9, 9), (5248, 1312, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_2, attn_3], Original ATen: [aten.mul, aten._softmax]
        triton_per_fused__softmax_mul_2.run(buf3, primals_4, buf6, 2304, 9, grid=grid(2304), stream=stream0)
        del primals_4
        buf7 = empty_strided_cuda((4, 4, 16, 9, 1), (576, 144, 9, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf1, buf0, buf7, 2304, grid=grid(2304), stream=stream0)
        buf8 = reinterpret_tensor(buf3, (256, 9, 9), (81, 9, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        triton_poi_fused_bmm_4.run(buf6, buf8, 20736, grid=grid(20736), stream=stream0)
        buf9 = empty_strided_cuda((256, 9, 1), (9, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf8, reinterpret_tensor(buf7, (256, 9, 1), (9, 1, 0), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.col2im]
        triton_poi_fused_col2im_5.run(buf10, 576, grid=grid(576), stream=stream0)
        buf11 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.col2im]
        triton_poi_fused_col2im_5.run(buf11, 576, grid=grid(576), stream=stream0)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.col2im]
        triton_poi_fused_col2im_6.run(buf1, buf9, buf11, 576, 4, grid=grid(576, 4), stream=stream0)
        del buf9
        buf13 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_7.run(buf11, buf13, 64, 4, grid=grid(64, 4), stream=stream0)
        del buf11
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf14  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
        triton_poi_fused_add_8.run(buf15, primals_6, 256, grid=grid(256), stream=stream0)
        del primals_6
    return (buf15, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), buf6, buf10, reinterpret_tensor(buf13, (64, 4), (4, 1), 0), primals_5, reinterpret_tensor(buf7, (256, 1, 9), (9, 1, 1), 0), )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((324, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((324, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel


class OutlookAttention(nn.Module):
    """
    Implementation of outlook attention
    --dim: hidden dim
    --num_heads: number of heads
    --kernel_size: kernel size in each window for outlook attention
    return: token features after outlook attention
    """

    def __init__(self, dim, num_heads, kernel_size=3, padding=1, stride=1,
        qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        head_dim = dim // num_heads
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.padding = padding
        self.stride = stride
        self.scale = qk_scale or head_dim ** -0.5
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn = nn.Linear(dim, kernel_size ** 4 * num_heads)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.unfold = nn.Unfold(kernel_size=kernel_size, padding=padding,
            stride=stride)
        self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride,
            ceil_mode=True)

    def forward(self, x):
        B, H, W, C = x.shape
        v = self.v(x).permute(0, 3, 1, 2)
        h, w = math.ceil(H / self.stride), math.ceil(W / self.stride)
        v = self.unfold(v).reshape(B, self.num_heads, C // self.num_heads, 
            self.kernel_size * self.kernel_size, h * w).permute(0, 1, 4, 3, 2)
        attn = self.pool(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
        attn = self.attn(attn).reshape(B, h * w, self.num_heads, self.
            kernel_size * self.kernel_size, self.kernel_size * self.kernel_size
            ).permute(0, 2, 1, 3, 4)
        attn = attn * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).permute(0, 1, 4, 3, 2).reshape(B, C * self.
            kernel_size * self.kernel_size, h * w)
        x = F.fold(x, output_size=(H, W), kernel_size=self.kernel_size,
            padding=self.padding, stride=self.stride)
        x = self.proj(x.permute(0, 2, 3, 1))
        x = self.proj_drop(x)
        return x


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


def get_init_inputs():
    return [[], {'dim': 4, 'num_heads': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.parallel
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_im2col_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + x1
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_mul_2(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 2304
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x7 = xindex
    x0 = xindex % 36
    x3 = xindex % 9
    x4 = xindex // 9 % 4
    x5 = xindex // 36 % 16
    x6 = xindex // 576
    tmp0 = tl.load(in_ptr0 + (r2 + 9 * x7), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + 9 * x0), rmask & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp5, float('-inf'))
    tmp8 = triton_helpers.max2(tmp7, 1)[:, None]
    tmp9 = tmp4 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r2 + 9 * x3 + 81 * x5 + 1312 * x4 + 5248 * x6),
        tmp15, rmask & xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 9
    x1 = xindex // 9 % 16
    x2 = xindex // 144 % 4
    x3 = xindex // 576
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (x0 // 3) + x1 // 4), xmask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 * (x0 % 3) + x1 % 4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 6, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 6) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 6')
    tmp7 = tmp6 + tmp1
    tmp8 = tmp6 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp6)
    tl.device_assert((0 <= tmp9) & (tmp9 < 6) | ~xmask,
        'index out of bounds: 0 <= tmp9 < 6')
    tmp11 = -1 + tmp4
    tmp12 = tl.full([1], 0, tl.int64)
    tmp13 = tmp11 >= tmp12
    tmp14 = tl.full([1], 4, tl.int64)
    tmp15 = tmp11 < tmp14
    tmp16 = -1 + tmp9
    tmp17 = tmp16 >= tmp12
    tmp18 = tmp16 < tmp14
    tmp19 = tmp13 & tmp15
    tmp20 = tmp19 & tmp17
    tmp21 = tmp20 & tmp18
    tmp22 = tl.load(in_ptr1 + (-20 + x2 + 4 * tmp9 + 16 * tmp4 + 64 * x3), 
        tmp21 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x5, tmp22, xmask)


@triton.jit
def triton_poi_fused_bmm_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 20736
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 81
    x1 = xindex // 81
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 81 * (x1 % 16) + 1312 * (x1 // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_col2im_5(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
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_col2im_6(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 576
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y5 = yindex // 3 % 12
    x4 = xindex
    y0 = yindex % 3
    y1 = yindex // 3 % 4
    y2 = yindex // 12 % 3
    y3 = yindex // 36
    tmp0 = tl.load(in_ptr0 + y5, ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (x4 + 4 * y0), xmask & ymask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (y0 + 3 * y2 + 9 * x4 + 36 * y1 + 144 * y3 + 
        144 * ((y0 + 3 * y2) // 9)), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.full([XBLOCK, YBLOCK], 6, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 6) | ~ymask,
        'index out of bounds: 0 <= tmp4 < 6')
    tmp7 = tmp6 + tmp1
    tmp8 = tmp6 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp6)
    tl.device_assert((0 <= tmp9) & (tmp9 < 6) | ~(xmask & ymask),
        'index out of bounds: 0 <= tmp9 < 6')
    tl.atomic_add(out_ptr0 + tl.broadcast_to(tmp9 + 6 * tmp4 + 36 * y3, [
        XBLOCK, YBLOCK]), tmp11, xmask & ymask, sem='relaxed')


@triton.jit
def triton_poi_fused_clone_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y1 = yindex // 4 % 4
    y0 = yindex % 4
    x3 = xindex
    y2 = yindex // 16
    y5 = yindex
    tmp0 = 1 + y1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 6, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 1 + y0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (7 + y0 + 6 * y1 + 36 * x3 + 144 * y2), tmp10 &
        xmask & ymask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x3 + 4 * y5), tmp11, xmask & ymask)


@triton.jit
def triton_poi_fused_add_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (324, 4), (4, 1))
    assert_size_stride(primals_4, (324,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((3, 4), (4, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_im2col_0[grid(12)](buf1, 12, XBLOCK=16, num_warps=
            1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(256)](primals_1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 324), (324, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 324), (1, 4), 0), out=buf3)
        del primals_3
        buf6 = empty_strided_cuda((4, 4, 16, 9, 9), (5248, 1312, 81, 9, 1),
            torch.float32)
        triton_per_fused__softmax_mul_2[grid(2304)](buf3, primals_4, buf6, 
            2304, 9, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_4
        buf7 = empty_strided_cuda((4, 4, 16, 9, 1), (576, 144, 9, 1, 1),
            torch.float32)
        triton_poi_fused_clone_3[grid(2304)](buf1, buf0, buf7, 2304, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf3, (256, 9, 9), (81, 9, 1), 0)
        del buf3
        triton_poi_fused_bmm_4[grid(20736)](buf6, buf8, 20736, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((256, 9, 1), (9, 1, 1), torch.float32)
        extern_kernels.bmm(buf8, reinterpret_tensor(buf7, (256, 9, 1), (9, 
            1, 0), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
            )
        triton_poi_fused_col2im_5[grid(576)](buf10, 576, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
            )
        triton_poi_fused_col2im_5[grid(576)](buf11, 576, XBLOCK=256,
            num_warps=4, num_stages=1)
        triton_poi_fused_col2im_6[grid(576, 4)](buf1, buf9, buf11, 576, 4,
            XBLOCK=1, YBLOCK=256, num_warps=4, num_stages=1)
        del buf9
        buf13 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_7[grid(64, 4)](buf11, buf13, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf11
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf14
        triton_poi_fused_add_8[grid(256)](buf15, primals_6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_6
    return buf15, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
        ), buf6, buf10, reinterpret_tensor(buf13, (64, 4), (4, 1), 0
        ), primals_5, reinterpret_tensor(buf7, (256, 1, 9), (9, 1, 1), 0)


class OutlookAttentionNew(nn.Module):
    """
    Implementation of outlook attention
    --dim: hidden dim
    --num_heads: number of heads
    --kernel_size: kernel size in each window for outlook attention
    return: token features after outlook attention
    """

    def __init__(self, dim, num_heads, kernel_size=3, padding=1, stride=1,
        qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        head_dim = dim // num_heads
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.padding = padding
        self.stride = stride
        self.scale = qk_scale or head_dim ** -0.5
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn = nn.Linear(dim, kernel_size ** 4 * num_heads)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.unfold = nn.Unfold(kernel_size=kernel_size, padding=padding,
            stride=stride)
        self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride,
            ceil_mode=True)

    def forward(self, input_0):
        primals_2 = self.v.weight
        primals_3 = self.attn.weight
        primals_4 = self.attn.bias
        primals_5 = self.proj.weight
        primals_6 = self.proj.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
