# 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_7/inductor_cache/es/ces44qnnsxil2dafm2xyy2gyquns7xgk6aon2uuwy4h5tpsyggbv.py
# Topologically Sorted Source Nodes: [add, mul, out], Original ATen: [aten.add, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   out => add_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %primals_6), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %getitem_1), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_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: '*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_mul_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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, 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
    x1 = (xindex // 4) % 16
    x3 = (xindex // 256)
    x4 = xindex % 256
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1) + (128*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + x0 + (8*x1) + (128*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 * tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + (x6), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (8, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1, ), (1, ))
    assert_size_stride(primals_9, (4, 1), (1, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cond], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (4, 8), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, mul, out], Original ATen: [aten.add, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(buf1, primals_5, primals_6, buf2, 1024, grid=grid(1024), stream=stream0)
        del buf1
        del primals_5
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_states], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_states_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9, (1, 4), (1, 1), 0), alpha=1, beta=1, out=buf5)
        del primals_10
    return (reinterpret_tensor(buf5, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf2, (256, 4), (4, 1), 0), buf4, primals_9, primals_7, primals_4, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
    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 _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


class FiLM(nn.Module):
    """ Feature-wise Linear Modulation (FiLM) layer"""

    def __init__(self, input_size, output_size, num_film_layers=1,
        layer_norm=False):
        """
        :param input_size: feature size of x_cond
        :param output_size: feature size of x_to_film
        :param layer_norm: true or false
        """
        super(FiLM, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_film_layers = num_film_layers
        self.layer_norm = nn.LayerNorm(output_size) if layer_norm else None
        film_output_size = self.output_size * num_film_layers * 2
        self.gb_weights = nn.Linear(self.input_size, film_output_size)
        self.gb_weights.bias.data.fill_(0)

    def forward(self, x_cond, x_to_film):
        gb = self.gb_weights(x_cond).unsqueeze(1)
        gamma, beta = torch.chunk(gb, 2, dim=-1)
        out = (1 + gamma) * x_to_film + beta
        if self.layer_norm is not None:
            out = self.layer_norm(out)
        return out


class ConditionalBottleNeck(nn.Module):
    """Down projection and up projection with FiLM layers within Transformer layer."""

    def __init__(self, config):
        super(ConditionalBottleNeck, self).__init__()
        self.emb_transf = nn.Linear(config.hidden_size, config.hidden_size)
        self.hidden_modulation = FiLM(config.hidden_size, config.hidden_size)
        self.down_proj_layer = nn.Linear(config.hidden_size, config.
            hidden_size // 3)
        self.up_proj_layer = nn.Linear(config.hidden_size // 3, config.
            hidden_size)

    def forward(self, x_cond, hidden_states):
        x_cond = self.emb_transf(x_cond)
        hidden_states = self.hidden_modulation(x_cond=x_cond, x_to_film=
            hidden_states)
        hidden_states = self.down_proj_layer(hidden_states)
        hidden_states = self.up_proj_layer(hidden_states)
        return hidden_states


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


def get_init_inputs():
    return [[], {'config': _mock_config(hidden_size=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
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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, 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
    x1 = xindex // 4 % 16
    x3 = xindex // 256
    x4 = xindex % 256
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1 + 128 * x3), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 8 * x1 + 128 * x3), xmask,
        eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 * tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + x6, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (4, 1), (1, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (4, 8), (1, 4
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(1024)](buf1, primals_5, primals_6,
            buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_5
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9,
            (1, 4), (1, 1), 0), alpha=1, beta=1, out=buf5)
        del primals_10
    return reinterpret_tensor(buf5, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
        ), primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf2, (256, 4), (4, 1), 0
        ), buf4, primals_9, primals_7, primals_4


class FiLM(nn.Module):
    """ Feature-wise Linear Modulation (FiLM) layer"""

    def __init__(self, input_size, output_size, num_film_layers=1,
        layer_norm=False):
        """
        :param input_size: feature size of x_cond
        :param output_size: feature size of x_to_film
        :param layer_norm: true or false
        """
        super(FiLM, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_film_layers = num_film_layers
        self.layer_norm = nn.LayerNorm(output_size) if layer_norm else None
        film_output_size = self.output_size * num_film_layers * 2
        self.gb_weights = nn.Linear(self.input_size, film_output_size)
        self.gb_weights.bias.data.fill_(0)

    def forward(self, x_cond, x_to_film):
        gb = self.gb_weights(x_cond).unsqueeze(1)
        gamma, beta = torch.chunk(gb, 2, dim=-1)
        out = (1 + gamma) * x_to_film + beta
        if self.layer_norm is not None:
            out = self.layer_norm(out)
        return out


class ConditionalBottleNeckNew(nn.Module):
    """Down projection and up projection with FiLM layers within Transformer layer."""

    def __init__(self, config):
        super(ConditionalBottleNeckNew, self).__init__()
        self.emb_transf = nn.Linear(config.hidden_size, config.hidden_size)
        self.hidden_modulation = FiLM(config.hidden_size, config.hidden_size)
        self.down_proj_layer = nn.Linear(config.hidden_size, config.
            hidden_size // 3)
        self.up_proj_layer = nn.Linear(config.hidden_size // 3, config.
            hidden_size)

    def forward(self, input_0, input_1):
        primals_1 = self.emb_transf.weight
        primals_2 = self.emb_transf.bias
        primals_4 = self.hidden_modulation.gb_weights.weight
        primals_5 = self.hidden_modulation.gb_weights.bias
        primals_7 = self.down_proj_layer.weight
        primals_8 = self.down_proj_layer.bias
        primals_9 = self.up_proj_layer.weight
        primals_10 = self.up_proj_layer.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
