# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/rr/crr4uc5tissxvuw4bzh6fo6y7osbgp6kedo24pzjiyyorxmxyfkc.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.copy]
# Source node to ATen node mapping:
#   pad => copy
# Graph fragment:
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_1, %slice_2), kwargs = {})
#   %slice_scatter_default : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%empty, %copy, 2, 1, 5), kwargs = {})
#   %slice_scatter_default_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default, %slice_7, 2, 0, 1), kwargs = {})
#   %slice_scatter_default_2 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_1, %slice_12, 2, 5, 6), kwargs = {})
triton_poi_fused_copy_0 = async_compile.triton('triton_poi_fused_copy_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=[8, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_copy_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 6
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y0 = yindex
    x1 = xindex
    tmp0 = y0
    tmp1 = tl.full([1, 1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.broadcast_to((-4) + y0, [XBLOCK, YBLOCK])
    tmp4 = tl.full([1, 1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tl.broadcast_to(y0, [XBLOCK, YBLOCK])
    tmp8 = tmp7 >= tmp4
    tmp9 = tmp7 < tmp1
    tmp10 = tmp8 & tmp9
    tmp11 = tmp10 & tmp6
    tmp12 = tl.load(in_ptr0 + ((-4) + x1 + (4*y0)), tmp11 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp13 = float("nan")
    tmp14 = tl.where(tmp10, tmp12, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tmp3 >= tmp4
    tmp18 = tmp3 < tmp1
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp2
    tmp21 = tl.load(in_ptr0 + ((-20) + x1 + (4*y0)), tmp20 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.where(tmp19, tmp21, tmp13)
    tmp23 = tl.where(tmp5, tmp16, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = tl.broadcast_to(4 + y0, [XBLOCK, YBLOCK])
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x1 + (4*y0)), tmp31 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp33 = tl.where(tmp30, tmp32, tmp13)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp26, tmp33, tmp34)
    tmp36 = tmp0 >= tmp4
    tmp37 = tmp0 < tmp1
    tmp38 = tmp36 & tmp37
    tmp39 = tl.load(in_ptr0 + ((-4) + x1 + (4*y0)), tmp38 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tmp40 = tl.where(tmp38, tmp39, tmp13)
    tmp41 = tl.where(tmp26, tmp35, tmp40)
    tmp42 = tl.where(tmp2, tmp25, tmp41)
    tl.store(out_ptr0 + (y0 + (6*x1)), tmp42, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6v/c6vcpvedkedg5f7dbt5warppc4dneicv65dm4nutaxergetxh3x5.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%slice_scatter_default_2, %primals_2, %primals_3, [1], [0], [1], False, [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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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 = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 10
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (10, 1, 3), (3, 3, 1))
    assert_size_stride(primals_3, (10, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 1, 6), (6, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused_copy_0.run(primals_1, buf1, 6, 4, grid=grid(6, 4), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 10, 4), (40, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf3, primals_3, 160, grid=grid(160), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf3, (10, 4, 4), (4, 1, 40), 0), primals_2, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, 1, 3), (3, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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


class CAT_TokenEmbedding(nn.Module):

    def __init__(self, c_in=1, d_feature=10):
        super(CAT_TokenEmbedding, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_feature,
            kernel_size=3, padding=padding, padding_mode='circular')
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, mode='fan_in',
                    nonlinearity='leaky_relu')

    def forward(self, x: 'torch.Tensor'):
        x = x.unsqueeze(1)
        x = x.transpose(0, 2)
        x = self.tokenConv(x).permute(1, 2, 0)
        return x


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


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 as nn
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_copy_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 6
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y0 = yindex
    x1 = xindex
    tmp0 = y0
    tmp1 = tl.full([1, 1], 5, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.broadcast_to(-4 + y0, [XBLOCK, YBLOCK])
    tmp4 = tl.full([1, 1], 1, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tl.broadcast_to(y0, [XBLOCK, YBLOCK])
    tmp8 = tmp7 >= tmp4
    tmp9 = tmp7 < tmp1
    tmp10 = tmp8 & tmp9
    tmp11 = tmp10 & tmp6
    tmp12 = tl.load(in_ptr0 + (-4 + x1 + 4 * y0), tmp11 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp13 = float('nan')
    tmp14 = tl.where(tmp10, tmp12, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tmp3 >= tmp4
    tmp18 = tmp3 < tmp1
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp2
    tmp21 = tl.load(in_ptr0 + (-20 + x1 + 4 * y0), tmp20 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tl.where(tmp19, tmp21, tmp13)
    tmp23 = tl.where(tmp5, tmp16, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp2, tmp23, tmp24)
    tmp26 = tmp0 < tmp4
    tmp27 = tl.broadcast_to(4 + y0, [XBLOCK, YBLOCK])
    tmp28 = tmp27 >= tmp4
    tmp29 = tmp27 < tmp1
    tmp30 = tmp28 & tmp29
    tmp31 = tmp30 & tmp26
    tmp32 = tl.load(in_ptr0 + (12 + x1 + 4 * y0), tmp31 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp33 = tl.where(tmp30, tmp32, tmp13)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp26, tmp33, tmp34)
    tmp36 = tmp0 >= tmp4
    tmp37 = tmp0 < tmp1
    tmp38 = tmp36 & tmp37
    tmp39 = tl.load(in_ptr0 + (-4 + x1 + 4 * y0), tmp38 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp40 = tl.where(tmp38, tmp39, tmp13)
    tmp41 = tl.where(tmp26, tmp35, tmp40)
    tmp42 = tl.where(tmp2, tmp25, tmp41)
    tl.store(out_ptr0 + (y0 + 6 * x1), tmp42, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 10
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (10, 1, 3), (3, 3, 1))
    assert_size_stride(primals_3, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 1, 6), (6, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_copy_0[grid(6, 4)](primals_1, buf1, 6, 4, XBLOCK=4,
            YBLOCK=8, num_warps=1, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 10, 4), (40, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(160)](buf3, primals_3, 160,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf3, (10, 4, 4), (4, 1, 40), 0), primals_2, buf1


class CAT_TokenEmbeddingNew(nn.Module):

    def __init__(self, c_in=1, d_feature=10):
        super(CAT_TokenEmbeddingNew, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_feature,
            kernel_size=3, padding=padding, padding_mode='circular')
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, mode='fan_in',
                    nonlinearity='leaky_relu')

    def forward(self, input_0):
        primals_2 = self.tokenConv.weight
        primals_3 = self.tokenConv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
