# 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_6/inductor_cache/io/ciovproy5mib3wbdqs7zp5srve5lkjm5dvi5m4auusjtlrmibvmw.py
# Topologically Sorted Source Nodes: [x, matmul], Original ATen: [aten.diag_embed, aten.view]
# Source node to ATen node mapping:
#   matmul => view_1
#   x => full_default, where
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%view, %permute, %full_default), kwargs = {})
#   %view_1 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%where, [256, 4]), kwargs = {})
triton_poi_fused_diag_embed_view_0 = async_compile.triton('triton_poi_fused_diag_embed_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=[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_diag_embed_view_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_diag_embed_view_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
    x0 = xindex % 4
    x2 = xindex
    x1 = (xindex // 4)
    tmp3 = tl.load(in_ptr0 + (x0 + (4*(x1 // 4))), xmask)
    tmp0 = x0
    tmp1 = (x2 // 4) % 4
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/bf/cbf3zg4epebirdzbfwvyjuwojqp4mbcftvlfwl54ki4sk7xb44xr.py
# Topologically Sorted Source Nodes: [x_output], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_output => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %sin], -1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 15872
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 62
    x1 = (xindex // 62)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((62*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 62, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr0 + (1 + (62*x1) + ((-1) + x0)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr1 + (1 + ((-1) + x0)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp10, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp9, tmp18)
    tl.store(out_ptr0 + (x2), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qv/cqv35lxkhzfyer5p27zwvlcpo7u6h6qs6eakchev2zvzdbxipdvj.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.cos]
# Source node to ATen node mapping:
# Graph fragment:
#   %cos : [num_users=1] = call_function[target=torch.ops.aten.cos.default](args = (%getitem_1,), kwargs = {})
triton_poi_fused_cos_2 = async_compile.triton('triton_poi_fused_cos_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cos_2', 'mutated_arg_names': [], '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_cos_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 15616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x1 = (xindex // 61)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + x0 + (62*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (1 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.cos(tmp2)
    tl.store(out_ptr0 + (x2), tmp3, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 62), (62, 1))
    assert_size_stride(primals_3, (62, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, matmul], Original ATen: [aten.diag_embed, aten.view]
        stream0 = get_raw_stream(0)
        triton_poi_fused_diag_embed_view_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((256, 62), (62, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4, 62), (3968, 992, 248, 62, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_output], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf1, primals_3, buf2, 15872, grid=grid(15872), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 61), (3904, 976, 244, 61, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.cos]
        triton_poi_fused_cos_2.run(buf1, primals_3, buf3, 15616, grid=grid(15616), stream=stream0)
        del buf1
        del primals_3
    return (reinterpret_tensor(buf2, (4, 4, 992), (3968, 992, 1), 0), buf3, reinterpret_tensor(buf0, (4, 256), (1, 4), 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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 62), (62, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((62, ), (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
from torch import nn


class Time2Vec(nn.Module):

    def __init__(self, input_dim=4, embed_dim=248, act_function=torch.sin):
        assert embed_dim % input_dim == 0
        super(Time2Vec, self).__init__()
        self.enabled = embed_dim > 0
        if self.enabled:
            self.embed_dim = embed_dim // input_dim
            self.input_dim = input_dim
            self.embed_weight = nn.parameter.Parameter(torch.randn(self.
                input_dim, self.embed_dim))
            self.embed_bias = nn.parameter.Parameter(torch.randn(self.
                embed_dim))
            self.act_function = act_function

    def forward(self, x):
        if self.enabled:
            x = torch.diag_embed(x)
            x_affine = torch.matmul(x, self.embed_weight) + self.embed_bias
            x_affine_0, x_affine_remain = torch.split(x_affine, [1, self.
                embed_dim - 1], dim=-1)
            x_affine_remain = self.act_function(x_affine_remain)
            x_output = torch.cat([x_affine_0, x_affine_remain], dim=-1)
            x_output = x_output.view(x_output.size(0), x_output.size(1), -1)
        else:
            x_output = x
        return x_output


def get_inputs():
    return [torch.rand([4, 4, 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
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_diag_embed_view_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
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask)
    tmp0 = x0
    tmp1 = x2 // 4 % 4
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 15872
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 62
    x1 = xindex // 62
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (62 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 62, tl.int64)
    tmp13 = tl.load(in_ptr0 + (1 + 62 * x1 + (-1 + x0)), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr1 + (1 + (-1 + x0)), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl_math.sin(tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp10, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp9, tmp18)
    tl.store(out_ptr0 + x2, tmp19, xmask)


@triton.jit
def triton_poi_fused_cos_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 15616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x1 = xindex // 61
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + x0 + 62 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (1 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.cos(tmp2)
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 62), (62, 1))
    assert_size_stride(primals_3, (62,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_diag_embed_view_0[grid(1024)](primals_1, buf0, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 62), (62, 1), torch.float32)
        extern_kernels.mm(buf0, primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4, 62), (3968, 992, 248, 62, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(15872)](buf1, primals_3, buf2, 15872,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 61), (3904, 976, 244, 61, 1),
            torch.float32)
        triton_poi_fused_cos_2[grid(15616)](buf1, primals_3, buf3, 15616,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 992), (3968, 992, 1), 0
        ), buf3, reinterpret_tensor(buf0, (4, 256), (1, 4), 0)


class Time2VecNew(nn.Module):

    def __init__(self, input_dim=4, embed_dim=248, act_function=torch.sin):
        assert embed_dim % input_dim == 0
        super(Time2VecNew, self).__init__()
        self.enabled = embed_dim > 0
        if self.enabled:
            self.embed_dim = embed_dim // input_dim
            self.input_dim = input_dim
            self.embed_weight = nn.parameter.Parameter(torch.randn(self.
                input_dim, self.embed_dim))
            self.embed_bias = nn.parameter.Parameter(torch.randn(self.
                embed_dim))
            self.act_function = act_function

    def forward(self, input_0):
        primals_2 = self.embed_weight
        primals_3 = self.embed_bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
