# AOT ID: ['0_inference']
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/zi/czi22qsrjxuffkie2uocl6775msmtv4wj7qurpwqgeshrxwhiy3n.py
# Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   stack => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3, %unsqueeze_4, %unsqueeze_5, %unsqueeze_6, %unsqueeze_7, %unsqueeze_8], -1), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '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, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', '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_stack_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7, out_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (4*x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr0 + (1 + (4*x1)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 6, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr0 + (4*x1), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.load(in_ptr0 + (1 + (4*x1)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp18 - tmp19
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp15, tmp20, tmp21)
    tmp23 = tl.where(tmp9, tmp14, tmp22)
    tmp24 = tl.where(tmp4, tmp5, tmp23)
    tmp25 = 1.0
    tmp26 = tmp24 * tmp25
    tmp27 = tmp26 * tmp25
    tmp28 = tl_math.sin(tmp27)
    tmp29 = tl_math.cos(tmp27)
    tmp30 = 2.0
    tmp31 = tmp24 * tmp30
    tmp32 = tmp31 * tmp25
    tmp33 = tl_math.sin(tmp32)
    tmp34 = tl_math.cos(tmp32)
    tmp35 = 4.0
    tmp36 = tmp24 * tmp35
    tmp37 = tmp36 * tmp25
    tmp38 = tl_math.sin(tmp37)
    tmp39 = tl_math.cos(tmp37)
    tmp40 = 8.0
    tmp41 = tmp24 * tmp40
    tmp42 = tmp41 * tmp25
    tmp43 = tl_math.sin(tmp42)
    tmp44 = tl_math.cos(tmp42)
    tl.store(out_ptr0 + (9*x2), tmp24, xmask)
    tl.store(out_ptr1 + (9*x2), tmp28, xmask)
    tl.store(out_ptr2 + (9*x2), tmp29, xmask)
    tl.store(out_ptr3 + (9*x2), tmp33, xmask)
    tl.store(out_ptr4 + (9*x2), tmp34, xmask)
    tl.store(out_ptr5 + (9*x2), tmp38, xmask)
    tl.store(out_ptr6 + (9*x2), tmp39, xmask)
    tl.store(out_ptr7 + (9*x2), tmp43, xmask)
    tl.store(out_ptr8 + (9*x2), tmp44, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf9 = empty_strided_cuda((4, 4, 4, 6, 9), (864, 216, 54, 9, 1), torch.float32)
        buf0 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 0)  # alias
        buf1 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 1)  # alias
        buf2 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 2)  # alias
        buf3 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 3)  # alias
        buf4 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 4)  # alias
        buf5 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 5)  # alias
        buf6 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 6)  # alias
        buf7 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 7)  # alias
        buf8 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 1), 8)  # alias
        # Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(arg0_1, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, 384, grid=grid(384), stream=stream0)
        del arg0_1
    return (reinterpret_tensor(buf9, (4, 4, 216), (864, 216, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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
from torch import nn


class ThetaEncoder(nn.Module):

    def __init__(self, encoder_len):
        super(ThetaEncoder, self).__init__()
        self.encoder_len = encoder_len
        self.omega = 1

    def forward(self, theta):
        """
        :param theta: [B, lead_num, 2]
        :return: [B, lead_num, 12]
        """
        b, lead_num = theta.size(0), theta.size(1)
        sum_theta = theta[..., 0:1] + theta[..., 1:2]
        sub_theta = theta[..., 0:1] - theta[..., 1:2]
        before_encode = torch.cat([theta, sum_theta, sub_theta], dim=-1)
        out_all = [before_encode]
        for i in range(self.encoder_len):
            sin = torch.sin(before_encode * 2 ** i * self.omega)
            cos = torch.cos(before_encode * 2 ** i * self.omega)
            out_all += [sin, cos]
        after_encode = torch.stack(out_all, dim=-1).view(b, lead_num, -1)
        return after_encode


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


def get_init_inputs():
    return [[], {'encoder_len': 4}]

import torch
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 math as tl_math
from torch import 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_stack_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7, out_ptr8, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + 4 * x1, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 6, tl.int64)
    tmp18 = tl.load(in_ptr0 + 4 * x1, tmp15 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tl.load(in_ptr0 + (1 + 4 * x1), tmp15 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tmp18 - tmp19
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp15, tmp20, tmp21)
    tmp23 = tl.where(tmp9, tmp14, tmp22)
    tmp24 = tl.where(tmp4, tmp5, tmp23)
    tmp25 = 1.0
    tmp26 = tmp24 * tmp25
    tmp27 = tmp26 * tmp25
    tmp28 = tl_math.sin(tmp27)
    tmp29 = tl_math.cos(tmp27)
    tmp30 = 2.0
    tmp31 = tmp24 * tmp30
    tmp32 = tmp31 * tmp25
    tmp33 = tl_math.sin(tmp32)
    tmp34 = tl_math.cos(tmp32)
    tmp35 = 4.0
    tmp36 = tmp24 * tmp35
    tmp37 = tmp36 * tmp25
    tmp38 = tl_math.sin(tmp37)
    tmp39 = tl_math.cos(tmp37)
    tmp40 = 8.0
    tmp41 = tmp24 * tmp40
    tmp42 = tmp41 * tmp25
    tmp43 = tl_math.sin(tmp42)
    tmp44 = tl_math.cos(tmp42)
    tl.store(out_ptr0 + 9 * x2, tmp24, xmask)
    tl.store(out_ptr1 + 9 * x2, tmp28, xmask)
    tl.store(out_ptr2 + 9 * x2, tmp29, xmask)
    tl.store(out_ptr3 + 9 * x2, tmp33, xmask)
    tl.store(out_ptr4 + 9 * x2, tmp34, xmask)
    tl.store(out_ptr5 + 9 * x2, tmp38, xmask)
    tl.store(out_ptr6 + 9 * x2, tmp39, xmask)
    tl.store(out_ptr7 + 9 * x2, tmp43, xmask)
    tl.store(out_ptr8 + 9 * x2, tmp44, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf9 = empty_strided_cuda((4, 4, 4, 6, 9), (864, 216, 54, 9, 1),
            torch.float32)
        buf0 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 0)
        buf1 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 1)
        buf2 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 2)
        buf3 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 3)
        buf4 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 4)
        buf5 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 5)
        buf6 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 6)
        buf7 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 7)
        buf8 = reinterpret_tensor(buf9, (4, 4, 4, 6, 1), (864, 216, 54, 9, 
            1), 8)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(384)](arg0_1, buf0, buf1, buf2, buf3,
            buf4, buf5, buf6, buf7, buf8, 384, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf9, (4, 4, 216), (864, 216, 1), 0),


class ThetaEncoderNew(nn.Module):

    def __init__(self, encoder_len):
        super(ThetaEncoderNew, self).__init__()
        self.encoder_len = encoder_len
        self.omega = 1

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
