# 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_1/inductor_cache/77/c773buhxkqj74y3lbtcuzrbfzcc4mm2eu7g7ocy36krblbxuszqn.py
# Topologically Sorted Source Nodes: [upsampled], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
#   upsampled => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + (4*x1)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/r5/cr5n2odkcxkryyb7m3oil332r3rrnsdt5bjo7jem2kiqb64aceb5.py
# Topologically Sorted Source Nodes: [out_deconv], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out_deconv => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%slice_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x3 = (xindex // 8)
    x1 = (xindex // 8) % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (9*x3)), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x4), tmp2, None)
''', 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, 64, 4), (256, 4, 1))
    assert_size_stride(primals_2, (64, 64, 10), (640, 10, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [upsampled], Original ATen: [aten._unsafe_index]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_index_0.run(primals_1, buf0, 2048, grid=grid(2048), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv_transpose1d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(4,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 9), (576, 9, 1))
        buf2 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_deconv], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, primals_3, buf2, 2048, grid=grid(2048), stream=stream0)
        del buf1
        del primals_3
    return (reinterpret_tensor(buf2, (4, 512), (512, 1), 0), primals_2, buf0, )


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, 64, 4), (256, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, 64, 10), (640, 10, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (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 TAE_decoder(nn.Module):
    """
    Class for temporal autoencoder decoder.
    filter_1 : filter size of the first convolution layer
    filter_lstm : hidden size of the lstm.
    """

    def __init__(self, n_hidden=64, pooling=8):
        super().__init__()
        self.pooling = pooling
        self.n_hidden = n_hidden
        self.up_layer = nn.Upsample(size=pooling)
        self.deconv_layer = nn.ConvTranspose1d(in_channels=self.n_hidden,
            out_channels=self.n_hidden, kernel_size=10, stride=1, padding=
            self.pooling // 2)

    def forward(self, features):
        upsampled = self.up_layer(features)
        out_deconv = self.deconv_layer(upsampled)[:, :, :self.pooling
            ].contiguous()
        out_deconv = out_deconv.view(out_deconv.shape[0], -1)
        return out_deconv


def get_inputs():
    return [torch.rand([4, 64, 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__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * x1), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x2, tmp5, None)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x3 = xindex // 8
    x1 = xindex // 8 % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 9 * x3), None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 4), (256, 4, 1))
    assert_size_stride(primals_2, (64, 64, 10), (640, 10, 1))
    assert_size_stride(primals_3, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(2048)](primals_1, buf0, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(4,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 9), (576, 9, 1))
        buf2 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        triton_poi_fused_clone_1[grid(2048)](buf1, primals_3, buf2, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return reinterpret_tensor(buf2, (4, 512), (512, 1), 0), primals_2, buf0


class TAE_decoderNew(nn.Module):
    """
    Class for temporal autoencoder decoder.
    filter_1 : filter size of the first convolution layer
    filter_lstm : hidden size of the lstm.
    """

    def __init__(self, n_hidden=64, pooling=8):
        super().__init__()
        self.pooling = pooling
        self.n_hidden = n_hidden
        self.up_layer = nn.Upsample(size=pooling)
        self.deconv_layer = nn.ConvTranspose1d(in_channels=self.n_hidden,
            out_channels=self.n_hidden, kernel_size=10, stride=1, padding=
            self.pooling // 2)

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