# 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_8/inductor_cache/tj/ctjxbm7pqjfzsc77y2u5tqjhlivfy4h26f473udvufoqyqcbkmmc.py
# Topologically Sorted Source Nodes: [backcast_harmonics_sin], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   backcast_harmonics_sin => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%permute_3, [2], True), kwargs = {})
triton_poi_fused_sum_0 = async_compile.triton('triton_poi_fused_sum_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_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_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (5*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rh/crh2pcnmhyua2shf5evp3uc76n5mcaqeq2rl2uhgsktuml2y6du6.py
# Topologically Sorted Source Nodes: [backcast], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   backcast => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_add_1', '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_add_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/5j/c5je2qjhvosr64yrccgldqfhjx5mqkfxlti7mt2geq6hl33dy2a2.py
# Topologically Sorted Source Nodes: [forecast], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   forecast => add_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %view_2), kwargs = {})
triton_poi_fused_add_2 = async_compile.triton('triton_poi_fused_add_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_add_2', '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_add_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (5, 4), (1, 5))
    assert_size_stride(arg2_1, (5, 4), (1, 5))
    assert_size_stride(arg3_1, (5, 4), (1, 5))
    assert_size_stride(arg4_1, (5, 4), (1, 5))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [backcast_harmonics_sin], Original ATen: [aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sum_0.run(arg2_1, buf0, 4, grid=grid(4), stream=stream0)
        del arg2_1
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [backcast_harmonics_cos], Original ATen: [aten.sum]
        triton_poi_fused_sum_0.run(arg1_1, buf1, 4, grid=grid(4), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [backcast], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(arg0_1, buf0, buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [forecast_harmonics_sin], Original ATen: [aten.sum]
        triton_poi_fused_sum_0.run(arg4_1, buf3, 4, grid=grid(4), stream=stream0)
        del arg4_1
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [forecast_harmonics_cos], Original ATen: [aten.sum]
        triton_poi_fused_sum_0.run(arg3_1, buf4, 4, grid=grid(4), stream=stream0)
        del arg3_1
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [forecast], Original ATen: [aten.add]
        triton_poi_fused_add_2.run(arg0_1, buf3, buf4, buf5, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del buf3
        del buf4
    return (buf2, buf5, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((5, 4), (1, 5), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((5, 4), (1, 5), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((5, 4), (1, 5), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((5, 4), (1, 5), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_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
import numpy as np
import torch as t


class SeasonalityBasis(t.nn.Module):
    """
    Harmonic functions to model seasonality.
    """

    def __init__(self, harmonics: 'int', backcast_size: 'int',
        forecast_size: 'int'):
        super().__init__()
        self.frequency = np.append(np.zeros(1, dtype=np.float32), np.arange
            (harmonics, harmonics / 2 * forecast_size, dtype=np.float32) /
            harmonics)[None, :]
        backcast_grid = -2 * np.pi * (np.arange(backcast_size, dtype=np.
            float32)[:, None] / forecast_size) * self.frequency
        forecast_grid = 2 * np.pi * (np.arange(forecast_size, dtype=np.
            float32)[:, None] / forecast_size) * self.frequency
        self.backcast_cos_template = t.nn.Parameter(t.tensor(np.transpose(
            np.cos(backcast_grid)), dtype=t.float32), requires_grad=False)
        self.backcast_sin_template = t.nn.Parameter(t.tensor(np.transpose(
            np.sin(backcast_grid)), dtype=t.float32), requires_grad=False)
        self.forecast_cos_template = t.nn.Parameter(t.tensor(np.transpose(
            np.cos(forecast_grid)), dtype=t.float32), requires_grad=False)
        self.forecast_sin_template = t.nn.Parameter(t.tensor(np.transpose(
            np.sin(forecast_grid)), dtype=t.float32), requires_grad=False)

    def forward(self, theta: 't.Tensor'):
        params_per_harmonic = theta.shape[1] // 4
        backcast_harmonics_cos = t.einsum('bp,pt->bt', theta[:, 2 *
            params_per_harmonic:3 * params_per_harmonic], self.
            backcast_cos_template)
        backcast_harmonics_sin = t.einsum('bp,pt->bt', theta[:, 3 *
            params_per_harmonic:], self.backcast_sin_template)
        backcast = backcast_harmonics_sin + backcast_harmonics_cos
        forecast_harmonics_cos = t.einsum('bp,pt->bt', theta[:, :
            params_per_harmonic], self.forecast_cos_template)
        forecast_harmonics_sin = t.einsum('bp,pt->bt', theta[:,
            params_per_harmonic:2 * params_per_harmonic], self.
            forecast_sin_template)
        forecast = forecast_harmonics_sin + forecast_harmonics_cos
        return backcast, forecast


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


def get_init_inputs():
    return [[], {'harmonics': 4, 'backcast_size': 4, 'forecast_size': 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
import numpy as np
import torch as t
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_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (5, 4), (1, 5))
    assert_size_stride(arg2_1, (5, 4), (1, 5))
    assert_size_stride(arg3_1, (5, 4), (1, 5))
    assert_size_stride(arg4_1, (5, 4), (1, 5))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sum_0[grid(4)](arg2_1, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del arg2_1
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        triton_poi_fused_sum_0[grid(4)](arg1_1, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_1[grid(16)](arg0_1, buf0, buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused_sum_0[grid(4)](arg4_1, buf3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del arg4_1
        buf4 = buf0
        del buf0
        triton_poi_fused_sum_0[grid(4)](arg3_1, buf4, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del arg3_1
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_2[grid(16)](arg0_1, buf3, buf4, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del buf3
        del buf4
    return buf2, buf5


class SeasonalityBasisNew(t.nn.Module):
    """
    Harmonic functions to model seasonality.
    """

    def __init__(self, harmonics: 'int', backcast_size: 'int',
        forecast_size: 'int'):
        super().__init__()
        self.frequency = np.append(np.zeros(1, dtype=np.float32), np.arange
            (harmonics, harmonics / 2 * forecast_size, dtype=np.float32) /
            harmonics)[None, :]
        backcast_grid = -2 * np.pi * (np.arange(backcast_size, dtype=np.
            float32)[:, None] / forecast_size) * self.frequency
        forecast_grid = 2 * np.pi * (np.arange(forecast_size, dtype=np.
            float32)[:, None] / forecast_size) * self.frequency
        self.backcast_cos_template = t.nn.Parameter(t.tensor(np.transpose(
            np.cos(backcast_grid)), dtype=t.float32), requires_grad=False)
        self.backcast_sin_template = t.nn.Parameter(t.tensor(np.transpose(
            np.sin(backcast_grid)), dtype=t.float32), requires_grad=False)
        self.forecast_cos_template = t.nn.Parameter(t.tensor(np.transpose(
            np.cos(forecast_grid)), dtype=t.float32), requires_grad=False)
        self.forecast_sin_template = t.nn.Parameter(t.tensor(np.transpose(
            np.sin(forecast_grid)), dtype=t.float32), requires_grad=False)

    def forward(self, input_0):
        arg1_1 = self.backcast_cos_template
        arg2_1 = self.backcast_sin_template
        arg3_1 = self.forecast_cos_template
        arg4_1 = self.forecast_sin_template
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0], output[1]
