# 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_8/inductor_cache/ca/ccalm3hakjc3y6pyouzotjhedyolmiuxj66frcn7j2bllcgh7gjc.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[1024, 4096], 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (1048576*y1)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/pi/cpiqq6ydqends5jgnpbakdwghpnovztoymnjrsnfd3ycpt6b7cp7.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[65536, 16], 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_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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4q/c4qmtshyh2quqp4mfbem56kakgs5tn4mkrnwi3suy4oa6joff42a.py
# Topologically Sorted Source Nodes: [conv2d, ac, conv2d_1, ap, mul, mul_1], Original ATen: [aten.convolution, aten.sigmoid, aten.tanh, aten.mul]
# Source node to ATen node mapping:
#   ac => sigmoid
#   ap => tanh
#   conv2d => convolution
#   conv2d_1 => convolution_1
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sigmoid), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %tanh), kwargs = {})
triton_poi_fused_convolution_mul_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_convolution_mul_sigmoid_tanh_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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_mul_sigmoid_tanh_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_convolution_mul_sigmoid_tanh_2(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4194304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x2), None)
    tmp4 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x2), None)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tl.sigmoid(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = libdevice.tanh(tmp2)
    tmp10 = tmp6 * tmp9
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(in_out_ptr1 + (x2), tmp5, None)
    tl.store(out_ptr0 + (x2), tmp8, None)
    tl.store(out_ptr1 + (x2), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/pn/cpngdclw7lzjwmfou3d3kip6qoxuvwgrgxlt5gs2kd5udjsfz5ny.py
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_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=[16, 4096], tile_hint=TileHint.DEFAULT,
    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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4096
    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 = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16384*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, 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, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_2, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_3, (256, ), (1, ))
    assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (256, ), (1, ))
    assert_size_stride(primals_6, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 1024, 4096, grid=grid(1024, 4096), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_2, buf1, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_4, buf2, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf0, buf1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf0, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf6 = buf5; del buf5  # reuse
        buf4 = buf3; del buf3  # reuse
        buf7 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, ac, conv2d_1, ap, mul, mul_1], Original ATen: [aten.convolution, aten.sigmoid, aten.tanh, aten.mul]
        triton_poi_fused_convolution_mul_sigmoid_tanh_2.run(buf6, buf4, primals_5, primals_3, buf0, buf7, buf10, 4194304, grid=grid(4194304), stream=stream0)
        del primals_3
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf9 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf8, primals_7, buf9, 16, 4096, grid=grid(16, 4096), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf12 = reinterpret_tensor(buf8, (4, 4, 64, 64), (16384, 4096, 64, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf11, primals_9, buf12, 16, 4096, grid=grid(16, 4096), stream=stream0)
        del buf11
        del primals_9
    return (buf9, buf12, buf0, buf1, buf2, primals_6, primals_8, buf4, buf6, buf7, buf10, )


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, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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, primals_8, primals_9])
    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.utils.data
import torch.nn
import torch.optim


class UpsampleCBR(torch.nn.Sequential):

    def __init__(self, input_channels, output_channels, count=1, num_flat=0):
        layers = []
        for i in range(count):
            if i == 0:
                inch = input_channels
            else:
                inch = output_channels
            layers += [torch.nn.ConvTranspose2d(inch, output_channels,
                kernel_size=4, stride=2, padding=1), torch.nn.BatchNorm2d(
                output_channels), torch.nn.ReLU()]
            for i in range(num_flat):
                layers += [torch.nn.Conv2d(output_channels, output_channels,
                    kernel_size=3, stride=1, padding=1), torch.nn.
                    BatchNorm2d(output_channels), torch.nn.ReLU()]
        super(UpsampleCBR, self).__init__(*layers)


class CmapPafHeadAttention(torch.nn.Module):

    def __init__(self, input_channels, cmap_channels, paf_channels,
        upsample_channels=256, num_upsample=0, num_flat=0):
        super(CmapPafHeadAttention, self).__init__()
        self.cmap_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.paf_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.cmap_att = torch.nn.Conv2d(upsample_channels,
            upsample_channels, kernel_size=3, stride=1, padding=1)
        self.paf_att = torch.nn.Conv2d(upsample_channels, upsample_channels,
            kernel_size=3, stride=1, padding=1)
        self.cmap_conv = torch.nn.Conv2d(upsample_channels, cmap_channels,
            kernel_size=1, stride=1, padding=0)
        self.paf_conv = torch.nn.Conv2d(upsample_channels, paf_channels,
            kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        xc = self.cmap_up(x)
        ac = torch.sigmoid(self.cmap_att(xc))
        xp = self.paf_up(x)
        ap = torch.tanh(self.paf_att(xp))
        return self.cmap_conv(xc * ac), self.paf_conv(xp * ap)


def get_inputs():
    return [torch.rand([4, 256, 64, 64])]


def get_init_inputs():
    return [[], {'input_channels': 4, 'cmap_channels': 4, 'paf_channels': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn
import torch.optim
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_tanh_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, None)
    tmp4 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + x2, None)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tl.sigmoid(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = libdevice.tanh(tmp2)
    tmp10 = tmp6 * tmp9
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(in_out_ptr1 + x2, tmp5, None)
    tl.store(out_ptr0 + x2, tmp8, None)
    tl.store(out_ptr1 + x2, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_2, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1024, 4096)](primals_1, buf0, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_1[grid(65536, 9)](primals_2, buf1, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_1[grid(65536, 9)](primals_4, buf2, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf5 = extern_kernels.convolution(buf0, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf6 = buf5
        del buf5
        buf4 = buf3
        del buf3
        buf7 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 
            256), torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_tanh_2[grid(4194304)](buf6,
            buf4, primals_5, primals_3, buf0, buf7, buf10, 4194304, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_3
        del primals_5
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf9 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_3[grid(16, 4096)](buf8, primals_7,
            buf9, 16, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_7
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf12 = reinterpret_tensor(buf8, (4, 4, 64, 64), (16384, 4096, 64, 
            1), 0)
        del buf8
        triton_poi_fused_convolution_3[grid(16, 4096)](buf11, primals_9,
            buf12, 16, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf11
        del primals_9
    return (buf9, buf12, buf0, buf1, buf2, primals_6, primals_8, buf4, buf6,
        buf7, buf10)


class UpsampleCBR(torch.nn.Sequential):

    def __init__(self, input_channels, output_channels, count=1, num_flat=0):
        layers = []
        for i in range(count):
            if i == 0:
                inch = input_channels
            else:
                inch = output_channels
            layers += [torch.nn.ConvTranspose2d(inch, output_channels,
                kernel_size=4, stride=2, padding=1), torch.nn.BatchNorm2d(
                output_channels), torch.nn.ReLU()]
            for i in range(num_flat):
                layers += [torch.nn.Conv2d(output_channels, output_channels,
                    kernel_size=3, stride=1, padding=1), torch.nn.
                    BatchNorm2d(output_channels), torch.nn.ReLU()]
        super(UpsampleCBR, self).__init__(*layers)


class CmapPafHeadAttentionNew(torch.nn.Module):

    def __init__(self, input_channels, cmap_channels, paf_channels,
        upsample_channels=256, num_upsample=0, num_flat=0):
        super(CmapPafHeadAttentionNew, self).__init__()
        self.cmap_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.paf_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.cmap_att = torch.nn.Conv2d(upsample_channels,
            upsample_channels, kernel_size=3, stride=1, padding=1)
        self.paf_att = torch.nn.Conv2d(upsample_channels, upsample_channels,
            kernel_size=3, stride=1, padding=1)
        self.cmap_conv = torch.nn.Conv2d(upsample_channels, cmap_channels,
            kernel_size=1, stride=1, padding=0)
        self.paf_conv = torch.nn.Conv2d(upsample_channels, paf_channels,
            kernel_size=1, stride=1, padding=0)

    def forward(self, input_0):
        primals_2 = self.cmap_att.weight
        primals_3 = self.cmap_att.bias
        primals_4 = self.paf_att.weight
        primals_5 = self.paf_att.bias
        primals_6 = self.cmap_conv.weight
        primals_7 = self.cmap_conv.bias
        primals_8 = self.paf_conv.weight
        primals_9 = self.paf_conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]
