# 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_4/inductor_cache/pw/cpw5jgywzg5ntkknxkt5orxsrrr5zq7a6eoteboi3ba7zrcxj2p7.py
# Topologically Sorted Source Nodes: [g], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   g => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=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_convolution_0', '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_convolution_0(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
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ow/cowknkiz5k2g6ztnk6tbibjnrz3filkhxucrp3a2phbhxhq6vwgy.py
# Topologically Sorted Source Nodes: [aff_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   aff_1 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mm, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mm, %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_1 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/c3/cc33uengbbrpbc2oxl2uomdjuzv6zahbzvvxfrdoulgb5c2nlpg3.py
# Topologically Sorted Source Nodes: [o, mul, mul_1, y], Original ATen: [aten.cat, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   o => cat
#   y => add
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_3, %view_4, %view_5, %view_6],), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat, 4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, -3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_cat_mul_2 = async_compile.triton('triton_poi_fused_add_cat_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.pointwise(
    size_hints=[64, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_cat_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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 // 16)
    x2 = xindex
    y0 = yindex % 16
    tmp25 = tl.load(in_ptr4 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp0 = y1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x2 + (4*y0)), tmp4 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x2 + (4*y0)), tmp9 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1, 1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x2 + (4*y0)), tmp14 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1, 1], 4, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x2 + (4*y0)), tmp16 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tmp23 = 4.0
    tmp24 = tmp22 * tmp23
    tmp26 = -3.0
    tmp27 = tmp25 * tmp26
    tmp28 = tmp24 + tmp27
    tl.store(out_ptr0 + (y0 + (16*x2) + (64*y1)), tmp28, xmask & ymask)
''', 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, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [f], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [g], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_3, primals_4, 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: [h], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_3, primals_6, 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 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [g], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf3, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [f], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf4, primals_2, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [aff], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 0), reinterpret_tensor(buf4, (4, 16), (16, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [aff_1], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf5, buf8, 16, 16, grid=grid(16), stream=stream0)
        buf9 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf9, primals_7, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf10 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cur_o], Original ATen: [aten.mm]
        extern_kernels.mm(buf8, reinterpret_tensor(buf9, (16, 4), (1, 16), 0), out=buf10)
        buf11 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [aff_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 64), reinterpret_tensor(buf4, (4, 16), (16, 1), 64), out=buf11)
        buf14 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [aff_3], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf11, buf14, 16, 16, grid=grid(16), stream=stream0)
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cur_o_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf14, reinterpret_tensor(buf9, (16, 4), (1, 16), 64), out=buf15)
        buf16 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [aff_4], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 128), reinterpret_tensor(buf4, (4, 16), (16, 1), 128), out=buf16)
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [aff_5], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf16, buf19, 16, 16, grid=grid(16), stream=stream0)
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cur_o_6], Original ATen: [aten.mm]
        extern_kernels.mm(buf19, reinterpret_tensor(buf9, (16, 4), (1, 16), 128), out=buf20)
        buf21 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [aff_6], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 192), reinterpret_tensor(buf4, (4, 16), (16, 1), 192), out=buf21)
        buf24 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [aff_7], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf21, buf24, 16, 16, grid=grid(16), stream=stream0)
        buf25 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cur_o_9], Original ATen: [aten.mm]
        extern_kernels.mm(buf24, reinterpret_tensor(buf9, (16, 4), (1, 16), 192), out=buf25)
        buf26 = reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf21  # reuse
        # Topologically Sorted Source Nodes: [o, mul, mul_1, y], Original ATen: [aten.cat, aten.mul, aten.add]
        triton_poi_fused_add_cat_mul_2.run(buf10, buf15, buf20, buf25, primals_3, buf26, 64, 4, grid=grid(64, 4), stream=stream0)
        del buf10
        del buf15
        del buf20
        del buf25
    return (buf26, primals_1, primals_3, primals_4, primals_6, reinterpret_tensor(buf4, (4, 16), (16, 1), 0), buf8, reinterpret_tensor(buf4, (4, 16), (16, 1), 64), buf14, reinterpret_tensor(buf4, (4, 16), (16, 1), 128), buf19, reinterpret_tensor(buf4, (4, 16), (16, 1), 192), buf24, reinterpret_tensor(buf9, (4, 16), (16, 1), 192), reinterpret_tensor(buf3, (4, 16), (16, 1), 192), reinterpret_tensor(buf9, (4, 16), (16, 1), 128), reinterpret_tensor(buf3, (4, 16), (16, 1), 128), reinterpret_tensor(buf9, (4, 16), (16, 1), 64), reinterpret_tensor(buf3, (4, 16), (16, 1), 64), reinterpret_tensor(buf9, (4, 16), (16, 1), 0), reinterpret_tensor(buf3, (4, 16), (16, 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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = 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, primals_7])
    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
from torch.nn import init


def weights_init(init_type='gaussian'):

    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0
            ) and hasattr(m, 'weight'):
            if init_type == 'gaussian':
                init.normal(m.weight.data, 0.0, 0.02)
            elif init_type == 'xavier':
                init.xavier_normal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, 'Unsupported initialization: {}'.format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant(m.bias.data, 0.0)
    return init_fun


class SelfAttention(nn.Module):

    def __init__(self, feat_dim, proj_dim, gamma):
        super(SelfAttention, self).__init__()
        self.W_g = torch.nn.Conv2d(feat_dim, proj_dim, kernel_size=(1, 1))
        self.W_f = torch.nn.Conv2d(feat_dim, proj_dim, kernel_size=(1, 1))
        self.W_h = torch.nn.Conv2d(feat_dim, feat_dim, kernel_size=(1, 1))
        self.softmax = torch.nn.Softmax(dim=1)
        self.gamma = gamma
        self.W_g.apply(weights_init('gaussian'))
        self.W_f.apply(weights_init('gaussian'))
        self.W_h.apply(weights_init('gaussian'))
        self.W_g
        self.W_f
        self.W_h

    def forward(self, x):
        f = self.W_f.forward(x)
        g = self.W_g.forward(x)
        h = self.W_h.forward(x)
        N, feat_D, hgt, wid = x.size(0), x.size(1), x.size(2), x.size(3)
        proj_D = f.size(1)
        f = f.view(N, proj_D, -1).transpose(1, 2)
        g = g.view(N, proj_D, -1).transpose(1, 2)
        h = h.view(N, feat_D, -1).transpose(1, 2)
        o = []
        for idx in range(N):
            aff = torch.mm(g[idx], f[idx].transpose(0, 1))
            aff = self.softmax(aff)
            cur_o = torch.mm(aff, h[idx])
            cur_o = cur_o.transpose(0, 1).contiguous()
            cur_o = cur_o.view(1, feat_D, hgt, wid)
            o.append(cur_o)
        o = torch.cat(o, 0)
        y = self.gamma * o + (1 - self.gamma) * x
        return y


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


def get_init_inputs():
    return [[], {'feat_dim': 4, 'proj_dim': 4, 'gamma': 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 math
import torch.nn as nn
from torch.nn import 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_convolution_0(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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_add_cat_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, 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 // 16
    x2 = xindex
    y0 = yindex % 16
    tmp25 = tl.load(in_ptr4 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp0 = y1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x2 + 4 * y0), tmp4 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x2 + 4 * y0), tmp9 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1, 1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x2 + 4 * y0), tmp14 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1, 1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x2 + 4 * y0), tmp16 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tmp23 = 4.0
    tmp24 = tmp22 * tmp23
    tmp26 = -3.0
    tmp27 = tmp25 * tmp26
    tmp28 = tmp24 + tmp27
    tl.store(out_ptr0 + (y0 + 16 * x2 + 64 * y1), tmp28, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_3, primals_4, 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(primals_3, primals_6, 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 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf3, primals_5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(256)](buf4, primals_2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 0),
            reinterpret_tensor(buf4, (4, 16), (16, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(16)](buf5, buf8, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf9 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(256)](buf9, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(buf9, (16, 4), (1, 16), 
            0), out=buf10)
        buf11 = buf5
        del buf5
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 64),
            reinterpret_tensor(buf4, (4, 16), (16, 1), 64), out=buf11)
        buf14 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(16)](buf11, buf14, 16, 16, XBLOCK=
            8, num_warps=2, num_stages=1)
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf14, reinterpret_tensor(buf9, (16, 4), (1, 16),
            64), out=buf15)
        buf16 = buf11
        del buf11
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 128),
            reinterpret_tensor(buf4, (4, 16), (16, 1), 128), out=buf16)
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(16)](buf16, buf19, 16, 16, XBLOCK=
            8, num_warps=2, num_stages=1)
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf19, reinterpret_tensor(buf9, (16, 4), (1, 16),
            128), out=buf20)
        buf21 = buf16
        del buf16
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (1, 16), 192),
            reinterpret_tensor(buf4, (4, 16), (16, 1), 192), out=buf21)
        buf24 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(16)](buf21, buf24, 16, 16, XBLOCK=
            8, num_warps=2, num_stages=1)
        buf25 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf24, reinterpret_tensor(buf9, (16, 4), (1, 16),
            192), out=buf25)
        buf26 = reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf21
        triton_poi_fused_add_cat_mul_2[grid(64, 4)](buf10, buf15, buf20,
            buf25, primals_3, buf26, 64, 4, XBLOCK=4, YBLOCK=64, num_warps=
            4, num_stages=1)
        del buf10
        del buf15
        del buf20
        del buf25
    return (buf26, primals_1, primals_3, primals_4, primals_6,
        reinterpret_tensor(buf4, (4, 16), (16, 1), 0), buf8,
        reinterpret_tensor(buf4, (4, 16), (16, 1), 64), buf14,
        reinterpret_tensor(buf4, (4, 16), (16, 1), 128), buf19,
        reinterpret_tensor(buf4, (4, 16), (16, 1), 192), buf24,
        reinterpret_tensor(buf9, (4, 16), (16, 1), 192), reinterpret_tensor
        (buf3, (4, 16), (16, 1), 192), reinterpret_tensor(buf9, (4, 16), (
        16, 1), 128), reinterpret_tensor(buf3, (4, 16), (16, 1), 128),
        reinterpret_tensor(buf9, (4, 16), (16, 1), 64), reinterpret_tensor(
        buf3, (4, 16), (16, 1), 64), reinterpret_tensor(buf9, (4, 16), (16,
        1), 0), reinterpret_tensor(buf3, (4, 16), (16, 1), 0))


def weights_init(init_type='gaussian'):

    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0
            ) and hasattr(m, 'weight'):
            if init_type == 'gaussian':
                init.normal(m.weight.data, 0.0, 0.02)
            elif init_type == 'xavier':
                init.xavier_normal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, 'Unsupported initialization: {}'.format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant(m.bias.data, 0.0)
    return init_fun


class SelfAttentionNew(nn.Module):

    def __init__(self, feat_dim, proj_dim, gamma):
        super(SelfAttentionNew, self).__init__()
        self.W_g = torch.nn.Conv2d(feat_dim, proj_dim, kernel_size=(1, 1))
        self.W_f = torch.nn.Conv2d(feat_dim, proj_dim, kernel_size=(1, 1))
        self.W_h = torch.nn.Conv2d(feat_dim, feat_dim, kernel_size=(1, 1))
        self.softmax = torch.nn.Softmax(dim=1)
        self.gamma = gamma
        self.W_g.apply(weights_init('gaussian'))
        self.W_f.apply(weights_init('gaussian'))
        self.W_h.apply(weights_init('gaussian'))
        self.W_g
        self.W_f
        self.W_h

    def forward(self, input_0):
        primals_1 = self.W_g.weight
        primals_2 = self.W_g.bias
        primals_4 = self.W_f.weight
        primals_5 = self.W_f.bias
        primals_6 = self.W_h.weight
        primals_7 = self.W_h.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
