# 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_6/inductor_cache/7q/c7qqwxngmk2rjdu4zavva2ebr5pwnfks2kljqyih65hb54qc7eld.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cos, %mul_2, %mul_4, %sin_1],), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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], 
    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_cat_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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 64)
    x0 = xindex % 64
    x2 = xindex
    tmp0 = x1
    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 + (x0 + (64*x1)), tmp4 & xmask, other=0.0)
    tmp6 = 3.141592653589793
    tmp7 = tmp5 * tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.cos(tmp9)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tmp14 = tl.full([1], 8, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tmp13 & tmp15
    tmp17 = tl.load(in_ptr0 + (x0 + (64*((-4) + x1))), tmp16 & xmask, other=0.0)
    tmp18 = 1.0
    tmp19 = tmp18 - tmp17
    tmp20 = tmp17 * tmp6
    tmp21 = tmp20 * tmp8
    tmp22 = tl_math.cos(tmp21)
    tmp23 = tmp19 - tmp22
    tmp24 = 0.0
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp16, tmp25, tmp26)
    tmp28 = tmp0 >= tmp14
    tmp29 = tl.full([1], 12, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tmp28 & tmp30
    tmp32 = tl.load(in_ptr0 + (x0 + (64*((-8) + x1))), tmp31 & xmask, other=0.0)
    tmp33 = tmp32 * tmp6
    tmp34 = tmp33 * tmp8
    tmp35 = tl_math.sin(tmp34)
    tmp36 = tmp32 - tmp35
    tmp37 = tmp36 * tmp24
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp31, tmp37, tmp38)
    tmp40 = tmp0 >= tmp29
    tmp41 = tl.full([1], 16, tl.int64)
    tmp42 = tmp0 < tmp41
    tmp43 = tl.load(in_ptr0 + (x0 + (64*((-12) + x1))), tmp40 & xmask, other=0.0)
    tmp44 = tmp43 * tmp6
    tmp45 = tmp44 * tmp8
    tmp46 = tl_math.sin(tmp45)
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp40, tmp46, tmp47)
    tmp49 = tl.where(tmp31, tmp39, tmp48)
    tmp50 = tl.where(tmp16, tmp27, tmp49)
    tmp51 = tl.where(tmp4, tmp12, tmp50)
    tl.store(out_ptr0 + (x2), tmp51, 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)
        buf0 = empty_strided_cuda((16, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
    return (buf0, )


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)

from torch.nn import Module
import torch
import numpy as np


class Arc2(Module):

    def __init__(self, num_bends):
        super(Arc2, self).__init__()
        self.num_bends = num_bends
        self.register_buffer('range', torch.arange(0, float(num_bends)))

    def a(self, t):
        return torch.cos(np.pi * t / 2)

    def b(self, t):
        return torch.sin(np.pi * t / 2)

    def forward(self, t):
        return torch.cat([self.a(t), 0.0 * (1.0 - t - self.a(t)), 0.0 * (t -
            self.b(t)), self.b(t)])


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


def get_init_inputs():
    return [[], {'num_bends': 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.nn import Module
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1), tmp4 & xmask, other=0.0)
    tmp6 = 3.141592653589793
    tmp7 = tmp5 * tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.cos(tmp9)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tmp14 = tl.full([1], 8, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tmp13 & tmp15
    tmp17 = tl.load(in_ptr0 + (x0 + 64 * (-4 + x1)), tmp16 & xmask, other=0.0)
    tmp18 = 1.0
    tmp19 = tmp18 - tmp17
    tmp20 = tmp17 * tmp6
    tmp21 = tmp20 * tmp8
    tmp22 = tl_math.cos(tmp21)
    tmp23 = tmp19 - tmp22
    tmp24 = 0.0
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp16, tmp25, tmp26)
    tmp28 = tmp0 >= tmp14
    tmp29 = tl.full([1], 12, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tmp28 & tmp30
    tmp32 = tl.load(in_ptr0 + (x0 + 64 * (-8 + x1)), tmp31 & xmask, other=0.0)
    tmp33 = tmp32 * tmp6
    tmp34 = tmp33 * tmp8
    tmp35 = tl_math.sin(tmp34)
    tmp36 = tmp32 - tmp35
    tmp37 = tmp36 * tmp24
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp31, tmp37, tmp38)
    tmp40 = tmp0 >= tmp29
    tl.full([1], 16, tl.int64)
    tmp43 = tl.load(in_ptr0 + (x0 + 64 * (-12 + x1)), tmp40 & xmask, other=0.0)
    tmp44 = tmp43 * tmp6
    tmp45 = tmp44 * tmp8
    tmp46 = tl_math.sin(tmp45)
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp40, tmp46, tmp47)
    tmp49 = tl.where(tmp31, tmp39, tmp48)
    tmp50 = tl.where(tmp16, tmp27, tmp49)
    tmp51 = tl.where(tmp4, tmp12, tmp50)
    tl.store(out_ptr0 + x2, tmp51, 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)
        buf0 = empty_strided_cuda((16, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class Arc2New(Module):

    def __init__(self, num_bends):
        super(Arc2New, self).__init__()
        self.num_bends = num_bends
        self.register_buffer('range', torch.arange(0, float(num_bends)))

    def a(self, t):
        return torch.cos(np.pi * t / 2)

    def b(self, t):
        return torch.sin(np.pi * t / 2)

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