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]


Measured latency_ms: 0.024011