# 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/l2/cl2ne4geqkszldz224l7hhcrcj4fumq5pt3lxp454c46zva5qjqs.py
# Topologically Sorted Source Nodes: [context_mask_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   context_mask_2 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_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, [2], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_0 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[4, 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_0', '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_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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_7/inductor_cache/mj/cmjkp7ajowfgqys7puj4r62dapqlmqnxd75735zspl5ujhk6wbu5.py
# Topologically Sorted Source Nodes: [avg_x], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   avg_x => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%convolution_3, [-1, -2], True), kwargs = {})
triton_per_fused_mean_1 = async_compile.triton('triton_per_fused_mean_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=[8, 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_mean_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    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, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/i7/ci7umzuzokfj7fo6e5444gnobrg3hrcxwbinehhhgwglrqvzgzui.py
# Topologically Sorted Source Nodes: [context_4], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   context_4 => amax_1, exp_1, sub_1, sum_2
# Graph fragment:
#   %amax_1 : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%bmm_1, [2], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm_1, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [2], True), kwargs = {})
triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_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=[4, 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', '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_2(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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]
    tl.store(out_ptr0 + (x0), tmp4, xmask)
    tl.store(out_ptr1 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/my/cmypyznookobb2k7gii75erk7jiic2ciobkgdpi4gamceyy2d2jq.py
# Topologically Sorted Source Nodes: [mask_ch, out, mask_sp, out_1, out_2], Original ATen: [aten.sigmoid, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mask_ch => sigmoid
#   mask_sp => sigmoid_1
#   out => mul
#   out_1 => mul_1
#   out_2 => add
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_10,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul), kwargs = {})
triton_poi_fused_add_mul_sigmoid_3 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_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: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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_mul_sigmoid_3', '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_mul_sigmoid_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    x4 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 / tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp0 * tmp7
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 + tmp11
    tl.store(out_ptr0 + (x3), tmp12, 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, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_5, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (2, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [input_x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, 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, 2, 4, 4), (32, 16, 4, 1))
        # Topologically Sorted Source Nodes: [context_mask], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 1, 16), (16, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [context_mask_2], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_0.run(buf1, buf4, 4, 16, grid=grid(4), stream=stream0)
        buf5 = empty_strided_cuda((4, 2, 1), (2, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [context], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 2, 16), (32, 16, 1), 0), reinterpret_tensor(buf4, (4, 16, 1), (16, 1, 16), 0), out=buf5)
        # Topologically Sorted Source Nodes: [context_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (4, 2, 1, 1), (2, 1, 1, 1), 0), primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        # Topologically Sorted Source Nodes: [g_x], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 2, 4, 4), (32, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        buf10 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [avg_x], Original ATen: [aten.mean]
        triton_per_fused_mean_1.run(buf10, buf7, 8, 16, grid=grid(8), stream=stream0)
        del buf7
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(primals_2, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 2, 4, 4), (32, 16, 4, 1))
        buf11 = reinterpret_tensor(buf1, (4, 1, 16), (16, 16, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [context_3], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf10, (4, 1, 2), (2, 0, 1), 0), reinterpret_tensor(buf9, (4, 2, 16), (32, 16, 1), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [context_4], Original ATen: [aten._softmax]
        triton_per_fused__softmax_2.run(buf11, buf12, buf13, 4, 16, grid=grid(4), stream=stream0)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mask_ch, out, mask_sp, out_1, out_2], Original ATen: [aten.sigmoid, aten.mul, aten.add]
        triton_poi_fused_add_mul_sigmoid_3.run(primals_2, buf11, buf12, buf13, buf6, buf14, 256, grid=grid(256), stream=stream0)
    return (buf14, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, buf4, reinterpret_tensor(buf5, (4, 2, 1, 1), (2, 1, 1, 1), 0), buf6, buf11, buf12, buf13, reinterpret_tensor(buf10, (4, 2, 1), (2, 1, 1), 0), reinterpret_tensor(buf9, (4, 16, 2), (32, 1, 16), 0), reinterpret_tensor(buf0, (4, 16, 2), (32, 1, 16), 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((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 2, 1, 1), (2, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2, 4, 1, 1), (4, 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._utils
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


def kaiming_init(module, a=0, mode='fan_out', nonlinearity='relu', bias=0,
    distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.kaiming_uniform_(module.weight, a=a, mode=mode,
            nonlinearity=nonlinearity)
    else:
        nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity
            =nonlinearity)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


class PSA_p(nn.Module):

    def __init__(self, inplanes, planes, kernel_size=1, stride=1):
        super(PSA_p, self).__init__()
        self.inplanes = inplanes
        self.inter_planes = planes // 2
        self.planes = planes
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = (kernel_size - 1) // 2
        self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1,
            stride=stride, padding=0, bias=False)
        self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.conv_up = nn.Conv2d(self.inter_planes, self.planes,
            kernel_size=1, stride=1, padding=0, bias=False)
        self.softmax_right = nn.Softmax(dim=2)
        self.sigmoid = nn.Sigmoid()
        self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.softmax_left = nn.Softmax(dim=2)
        self.reset_parameters()

    def reset_parameters(self):
        kaiming_init(self.conv_q_right, mode='fan_in')
        kaiming_init(self.conv_v_right, mode='fan_in')
        kaiming_init(self.conv_q_left, mode='fan_in')
        kaiming_init(self.conv_v_left, mode='fan_in')
        self.conv_q_right.inited = True
        self.conv_v_right.inited = True
        self.conv_q_left.inited = True
        self.conv_v_left.inited = True

    def spatial_pool(self, x):
        input_x = self.conv_v_right(x)
        batch, channel, height, width = input_x.size()
        input_x = input_x.view(batch, channel, height * width)
        context_mask = self.conv_q_right(x)
        context_mask = context_mask.view(batch, 1, height * width)
        context_mask = self.softmax_right(context_mask)
        context = torch.matmul(input_x, context_mask.transpose(1, 2))
        context = context.unsqueeze(-1)
        context = self.conv_up(context)
        mask_ch = self.sigmoid(context)
        out = x * mask_ch
        return out

    def channel_pool(self, x):
        g_x = self.conv_q_left(x)
        batch, channel, height, width = g_x.size()
        avg_x = self.avg_pool(g_x)
        batch, channel, avg_x_h, avg_x_w = avg_x.size()
        avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1)
        theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height *
            width)
        context = torch.matmul(avg_x, theta_x)
        context = self.softmax_left(context)
        context = context.view(batch, 1, height, width)
        mask_sp = self.sigmoid(context)
        out = x * mask_sp
        return out

    def forward(self, x):
        context_channel = self.spatial_pool(x)
        context_spatial = self.channel_pool(x)
        out = context_spatial + context_channel
        return out


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


def get_init_inputs():
    return [[], {'inplanes': 4, 'planes': 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._utils
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_per_fused__softmax_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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_per_fused_mean_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 8
    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, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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]
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 / tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp0 * tmp7
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 + tmp11
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_5, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (2, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, 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, 2, 4, 4), (32, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 1, 16), (16, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(4)](buf1, buf4, 4, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4, 2, 1), (2, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 2, 16), (32, 16, 1),
            0), reinterpret_tensor(buf4, (4, 16, 1), (16, 1, 16), 0), out=buf5)
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (4, 2, 1,
            1), (2, 1, 1, 1), 0), primals_4, stride=(1, 1), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 2, 4, 4), (32, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        buf10 = buf8
        del buf8
        triton_per_fused_mean_1[grid(8)](buf10, buf7, 8, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf7
        buf9 = extern_kernels.convolution(primals_2, primals_6, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 2, 4, 4), (32, 16, 4, 1))
        buf11 = reinterpret_tensor(buf1, (4, 1, 16), (16, 16, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf10, (4, 1, 2), (2, 0, 1), 
            0), reinterpret_tensor(buf9, (4, 2, 16), (32, 16, 1), 0), out=buf11
            )
        buf12 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused__softmax_2[grid(4)](buf11, buf12, buf13, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_3[grid(256)](primals_2, buf11,
            buf12, buf13, buf6, buf14, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
    return (buf14, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, buf4, reinterpret_tensor(buf5, (4, 2, 1, 1), (2, 1, 1, 1
        ), 0), buf6, buf11, buf12, buf13, reinterpret_tensor(buf10, (4, 2, 
        1), (2, 1, 1), 0), reinterpret_tensor(buf9, (4, 16, 2), (32, 1, 16),
        0), reinterpret_tensor(buf0, (4, 16, 2), (32, 1, 16), 0))


def kaiming_init(module, a=0, mode='fan_out', nonlinearity='relu', bias=0,
    distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.kaiming_uniform_(module.weight, a=a, mode=mode,
            nonlinearity=nonlinearity)
    else:
        nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity
            =nonlinearity)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


class PSA_pNew(nn.Module):

    def __init__(self, inplanes, planes, kernel_size=1, stride=1):
        super(PSA_pNew, self).__init__()
        self.inplanes = inplanes
        self.inter_planes = planes // 2
        self.planes = planes
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = (kernel_size - 1) // 2
        self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1,
            stride=stride, padding=0, bias=False)
        self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.conv_up = nn.Conv2d(self.inter_planes, self.planes,
            kernel_size=1, stride=1, padding=0, bias=False)
        self.softmax_right = nn.Softmax(dim=2)
        self.sigmoid = nn.Sigmoid()
        self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes,
            kernel_size=1, stride=stride, padding=0, bias=False)
        self.softmax_left = nn.Softmax(dim=2)
        self.reset_parameters()

    def reset_parameters(self):
        kaiming_init(self.conv_q_right, mode='fan_in')
        kaiming_init(self.conv_v_right, mode='fan_in')
        kaiming_init(self.conv_q_left, mode='fan_in')
        kaiming_init(self.conv_v_left, mode='fan_in')
        self.conv_q_right.inited = True
        self.conv_v_right.inited = True
        self.conv_q_left.inited = True
        self.conv_v_left.inited = True

    def spatial_pool(self, x):
        input_x = self.conv_v_right(x)
        batch, channel, height, width = input_x.size()
        input_x = input_x.view(batch, channel, height * width)
        context_mask = self.conv_q_right(x)
        context_mask = context_mask.view(batch, 1, height * width)
        context_mask = self.softmax_right(context_mask)
        context = torch.matmul(input_x, context_mask.transpose(1, 2))
        context = context.unsqueeze(-1)
        context = self.conv_up(context)
        mask_ch = self.sigmoid(context)
        out = x * mask_ch
        return out

    def channel_pool(self, x):
        g_x = self.conv_q_left(x)
        batch, channel, height, width = g_x.size()
        avg_x = self.avg_pool(g_x)
        batch, channel, avg_x_h, avg_x_w = avg_x.size()
        avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1)
        theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height *
            width)
        context = torch.matmul(avg_x, theta_x)
        context = self.softmax_left(context)
        context = context.view(batch, 1, height, width)
        mask_sp = self.sigmoid(context)
        out = x * mask_sp
        return out

    def forward(self, input_0):
        primals_3 = self.conv_q_right.weight
        primals_1 = self.conv_v_right.weight
        primals_4 = self.conv_up.weight
        primals_5 = self.conv_q_left.weight
        primals_6 = self.conv_v_left.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
