# 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_4/inductor_cache/iu/ciuxern2omgit5ovksuiwlddxkww6e3pkid4q2h3sauzn5rbd35z.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [2], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/l7/cl73m2z7ubizl4gjzahoztnfbxiinsybshrc4sjlnb7hovne23sz.py
# Topologically Sorted Source Nodes: [sigmoid, tanh, mul], Original ATen: [aten.sigmoid, aten.tanh, aten.mul]
# Source node to ATen node mapping:
#   mul => mul
#   sigmoid => sigmoid
#   tanh => tanh
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
triton_poi_fused_mul_sigmoid_tanh_1 = async_compile.triton('triton_poi_fused_mul_sigmoid_tanh_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: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (8*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + (8*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (8, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 8, 1), (8, 1, 1))
        del buf0
        buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_5, (4, 8), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, tanh, mul], Original ATen: [aten.sigmoid, aten.tanh, aten.mul]
        triton_poi_fused_mul_sigmoid_tanh_1.run(buf1, primals_3, buf2, buf3, buf4, buf5, 16, grid=grid(16), stream=stream0)
        del buf1
        del buf2
        del primals_3
    return (buf5, primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf3, buf4, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 math
import torch
import torch.nn as nn


class Conv2(nn.Module):
    """ A convolution layer with the stride of 2.

        Input:
            x: (N, 2L+2, in_channels) numeric tensor
            global_cond: (N, global_cond_channels) numeric tensor
        Output:
            y: (N, L, out_channels) numeric tensor
    """

    def __init__(self, in_channels, out_channels, global_cond_channels):
        super().__init__()
        ksz = 4
        self.out_channels = out_channels
        if 0 < global_cond_channels:
            self.w_cond = nn.Linear(global_cond_channels, 2 * out_channels,
                bias=False)
        self.conv_wide = nn.Conv1d(in_channels, 2 * out_channels, ksz, stride=2
            )
        wsize = 2.967 / math.sqrt(ksz * in_channels)
        self.conv_wide.weight.data.uniform_(-wsize, wsize)
        self.conv_wide.bias.data.zero_()

    def forward(self, x, global_cond):
        x1 = self.conv_wide(x.transpose(1, 2)).transpose(1, 2)
        if global_cond is not None:
            x2 = self.w_cond(global_cond).unsqueeze(1).expand(-1, x1.size(1
                ), -1)
        else:
            x2 = torch.zeros_like(x1)
        a, b = (x1 + x2).split(self.out_channels, dim=2)
        return torch.sigmoid(a) * torch.tanh(b)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4,
        'global_cond_channels': 4}]

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 libdevice
import math
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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 8 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 8, 1), (8, 1, 1))
        del buf0
        buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_5, (4, 8),
            (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_tanh_1[grid(16)](buf1, primals_3, buf2,
            buf3, buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del buf2
        del primals_3
    return buf5, primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4,
        4), (16, 1, 4), 0), buf3, buf4


class Conv2New(nn.Module):
    """ A convolution layer with the stride of 2.

        Input:
            x: (N, 2L+2, in_channels) numeric tensor
            global_cond: (N, global_cond_channels) numeric tensor
        Output:
            y: (N, L, out_channels) numeric tensor
    """

    def __init__(self, in_channels, out_channels, global_cond_channels):
        super().__init__()
        ksz = 4
        self.out_channels = out_channels
        if 0 < global_cond_channels:
            self.w_cond = nn.Linear(global_cond_channels, 2 * out_channels,
                bias=False)
        self.conv_wide = nn.Conv1d(in_channels, 2 * out_channels, ksz, stride=2
            )
        wsize = 2.967 / math.sqrt(ksz * in_channels)
        self.conv_wide.weight.data.uniform_(-wsize, wsize)
        self.conv_wide.bias.data.zero_()

    def forward(self, input_0, input_1):
        primals_5 = self.w_cond.weight
        primals_2 = self.conv_wide.weight
        primals_3 = self.conv_wide.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
