# 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/eh/ceh2tavoj4zbzeixfhcp4k45qw7zbbme7m2weqdqilclzphzdslt.py
# Topologically Sorted Source Nodes: [cls_kernel, pk], Original ATen: [aten.convolution, aten.view]
# Source node to ATen node mapping:
#   cls_kernel => convolution
#   pk => view
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%convolution, [-1, 256, 62, 62]), kwargs = {})
triton_poi_fused_convolution_view_0 = async_compile.triton('triton_poi_fused_convolution_view_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=[8388608], 
    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_view_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_view_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7872512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x1 = (xindex // 3844) % 512
    tmp0 = tl.load(in_out_ptr0 + (x4), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x4), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/y6/cy6qx57jpiu4vlxl2g25y7rel2psvzfz7ebvhvgjv3bc3t32qrmr.py
# Topologically Sorted Source Nodes: [cls_feature], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   cls_feature => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_8, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[4194304], 
    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_1', '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3936256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 3844) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/xq/cxq3v2kja3zv4alli7z5uribrvjd3atbdmdoxvcj4tw3dddxqyxq.py
# Topologically Sorted Source Nodes: [loc_kernel, pk_1], Original ATen: [aten.convolution, aten.view]
# Source node to ATen node mapping:
#   loc_kernel => convolution_1
#   pk_1 => view_3
# 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 = {})
#   %view_3 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%convolution_1, [-1, 256, 62, 62]), kwargs = {})
triton_poi_fused_convolution_view_2 = async_compile.triton('triton_poi_fused_convolution_view_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=[16777216], 
    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_view_2', '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_view_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 15745024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x1 = (xindex // 3844) % 1024
    tmp0 = tl.load(in_out_ptr0 + (x4), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x4), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/5v/c5vp5kwiougsjrt24sbuebc3tdgtaqyy6acw5g4woberjney646p.py
# Topologically Sorted Source Nodes: [loc], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   loc => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_5, %primals_11, %primals_12, [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], 
    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_3', '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_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_2, (512, ), (1, ))
    assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_4, (1024, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (1024, ), (1, ))
    assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_9, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_10, (256, ), (1, ))
    assert_size_stride(primals_11, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [cls_kernel], 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, 512, 62, 62), (1968128, 3844, 62, 1))
        # Topologically Sorted Source Nodes: [loc_kernel], 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, 1024, 62, 62), (3936256, 3844, 62, 1))
        # Topologically Sorted Source Nodes: [cls_feature], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_8, 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, 256, 62, 62), (984064, 3844, 62, 1))
        # Topologically Sorted Source Nodes: [loc_feature], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_8, primals_9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 62, 62), (984064, 3844, 62, 1))
        buf4 = buf0; del buf0  # reuse
        buf5 = reinterpret_tensor(buf4, (8, 256, 62, 62), (984064, 3844, 62, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [cls_kernel, pk], Original ATen: [aten.convolution, aten.view]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_view_0.run(buf5, primals_2, 7872512, grid=grid(7872512), stream=stream0)
        del primals_2
        buf6 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [cls_feature], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf6, primals_7, 3936256, grid=grid(3936256), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [po], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 1024, 62, 62), (0, 3844, 62, 1), 0), buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (1, 8, 1, 1), (8, 1, 1, 1))
        buf8 = buf1; del buf1  # reuse
        buf9 = reinterpret_tensor(buf8, (16, 256, 62, 62), (984064, 3844, 62, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [loc_kernel, pk_1], Original ATen: [aten.convolution, aten.view]
        triton_poi_fused_convolution_view_2.run(buf9, primals_5, 15745024, grid=grid(15745024), stream=stream0)
        del primals_5
        buf10 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [loc_feature], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf10, primals_10, 3936256, grid=grid(3936256), stream=stream0)
        del primals_10
        # Topologically Sorted Source Nodes: [po_2], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(reinterpret_tensor(buf10, (1, 1024, 62, 62), (0, 3844, 62, 1), 0), buf9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf11, (1, 16, 1, 1), (16, 1, 1, 1))
        # Topologically Sorted Source Nodes: [loc], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 4, 1, 1), (4, 1, 1, 1), 0), primals_11, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 1, 1), (4, 1, 1, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [loc], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf13, primals_12, 16, grid=grid(16), stream=stream0)
        del primals_12
    return (reinterpret_tensor(buf7, (4, 2, 1, 1), (2, 1, 1, 1), 0), buf13, primals_1, primals_3, primals_4, primals_6, primals_8, primals_9, primals_11, buf5, reinterpret_tensor(buf6, (1, 1024, 62, 62), (3936256, 3844, 62, 1), 0), buf9, reinterpret_tensor(buf10, (1, 1024, 62, 62), (3936256, 3844, 62, 1), 0), reinterpret_tensor(buf11, (4, 4, 1, 1), (4, 1, 1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1024, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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, primals_10, primals_11, primals_12])
    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.functional as F
import torch.nn as nn


def xcorr_fast(x, kernel):
    """group conv2d to calculate cross correlation, fast version
    """
    batch = kernel.size()[0]
    pk = kernel.view(-1, x.size()[1], kernel.size()[2], kernel.size()[3])
    px = x.view(1, -1, x.size()[2], x.size()[3])
    po = F.conv2d(px, pk, groups=batch)
    po = po.view(batch, -1, po.size()[2], po.size()[3])
    return po


class BAN(nn.Module):

    def __init__(self):
        super(BAN, self).__init__()

    def forward(self, z_f, x_f):
        raise NotImplementedError


class UPChannelBAN(BAN):

    def __init__(self, feature_in=256, cls_out_channels=2):
        super(UPChannelBAN, self).__init__()
        cls_output = cls_out_channels
        loc_output = 4
        self.template_cls_conv = nn.Conv2d(feature_in, feature_in *
            cls_output, kernel_size=3)
        self.template_loc_conv = nn.Conv2d(feature_in, feature_in *
            loc_output, kernel_size=3)
        self.search_cls_conv = nn.Conv2d(feature_in, feature_in, kernel_size=3)
        self.search_loc_conv = nn.Conv2d(feature_in, feature_in, kernel_size=3)
        self.loc_adjust = nn.Conv2d(loc_output, loc_output, kernel_size=1)

    def forward(self, z_f, x_f):
        cls_kernel = self.template_cls_conv(z_f)
        loc_kernel = self.template_loc_conv(z_f)
        cls_feature = self.search_cls_conv(x_f)
        loc_feature = self.search_loc_conv(x_f)
        cls = xcorr_fast(cls_feature, cls_kernel)
        loc = self.loc_adjust(xcorr_fast(loc_feature, loc_kernel))
        return cls, loc


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


def get_init_inputs():
    return [[], {}]

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
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_view_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x1 = xindex // 3844 % 512
    tmp0 = tl.load(in_out_ptr0 + x4, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x4, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 3844 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_view_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x1 = xindex // 3844 % 1024
    tmp0 = tl.load(in_out_ptr0 + x4, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x4, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_4, (1024, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_9, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_10, (256,), (1,))
    assert_size_stride(primals_11, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_12, (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, 512, 62, 62), (1968128, 3844, 62, 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, 1024, 62, 62), (3936256, 3844, 62, 1))
        buf2 = extern_kernels.convolution(primals_8, 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, 256, 62, 62), (984064, 3844, 62, 1))
        buf3 = extern_kernels.convolution(primals_8, primals_9, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 62, 62), (984064, 3844, 62, 1))
        buf4 = buf0
        del buf0
        buf5 = reinterpret_tensor(buf4, (8, 256, 62, 62), (984064, 3844, 62,
            1), 0)
        del buf4
        get_raw_stream(0)
        triton_poi_fused_convolution_view_0[grid(7872512)](buf5, primals_2,
            7872512, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf6 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(3936256)](buf6, primals_7, 
            3936256, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 1024,
            62, 62), (0, 3844, 62, 1), 0), buf5, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=4, bias=None)
        assert_size_stride(buf7, (1, 8, 1, 1), (8, 1, 1, 1))
        buf8 = buf1
        del buf1
        buf9 = reinterpret_tensor(buf8, (16, 256, 62, 62), (984064, 3844, 
            62, 1), 0)
        del buf8
        triton_poi_fused_convolution_view_2[grid(15745024)](buf9, primals_5,
            15745024, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf10 = buf3
        del buf3
        triton_poi_fused_convolution_1[grid(3936256)](buf10, primals_10, 
            3936256, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_10
        buf11 = extern_kernels.convolution(reinterpret_tensor(buf10, (1, 
            1024, 62, 62), (0, 3844, 62, 1), 0), buf9, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf11, (1, 16, 1, 1), (16, 1, 1, 1))
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 4,
            1, 1), (4, 1, 1, 1), 0), primals_11, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 1, 1), (4, 1, 1, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_3[grid(16)](buf13, primals_12, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_12
    return (reinterpret_tensor(buf7, (4, 2, 1, 1), (2, 1, 1, 1), 0), buf13,
        primals_1, primals_3, primals_4, primals_6, primals_8, primals_9,
        primals_11, buf5, reinterpret_tensor(buf6, (1, 1024, 62, 62), (
        3936256, 3844, 62, 1), 0), buf9, reinterpret_tensor(buf10, (1, 1024,
        62, 62), (3936256, 3844, 62, 1), 0), reinterpret_tensor(buf11, (4, 
        4, 1, 1), (4, 1, 1, 1), 0))


def xcorr_fast(x, kernel):
    """group conv2d to calculate cross correlation, fast version
    """
    batch = kernel.size()[0]
    pk = kernel.view(-1, x.size()[1], kernel.size()[2], kernel.size()[3])
    px = x.view(1, -1, x.size()[2], x.size()[3])
    po = F.conv2d(px, pk, groups=batch)
    po = po.view(batch, -1, po.size()[2], po.size()[3])
    return po


class BAN(nn.Module):

    def __init__(self):
        super(BAN, self).__init__()

    def forward(self, z_f, x_f):
        raise NotImplementedError


class UPChannelBANNew(BAN):

    def __init__(self, feature_in=256, cls_out_channels=2):
        super(UPChannelBANNew, self).__init__()
        cls_output = cls_out_channels
        loc_output = 4
        self.template_cls_conv = nn.Conv2d(feature_in, feature_in *
            cls_output, kernel_size=3)
        self.template_loc_conv = nn.Conv2d(feature_in, feature_in *
            loc_output, kernel_size=3)
        self.search_cls_conv = nn.Conv2d(feature_in, feature_in, kernel_size=3)
        self.search_loc_conv = nn.Conv2d(feature_in, feature_in, kernel_size=3)
        self.loc_adjust = nn.Conv2d(loc_output, loc_output, kernel_size=1)

    def forward(self, input_0, input_1):
        primals_1 = self.template_cls_conv.weight
        primals_2 = self.template_cls_conv.bias
        primals_4 = self.template_loc_conv.weight
        primals_5 = self.template_loc_conv.bias
        primals_6 = self.search_cls_conv.weight
        primals_7 = self.search_cls_conv.bias
        primals_9 = self.search_loc_conv.weight
        primals_10 = self.search_loc_conv.bias
        primals_11 = self.loc_adjust.weight
        primals_12 = self.loc_adjust.bias
        primals_3 = input_0
        primals_8 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0], output[1]
