# 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/us/cusgaoeq6tgjqeybtofnrkxq3rybigqcxz23tjeddnbyaohmsz26.py
# Topologically Sorted Source Nodes: [W_act], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   W_act => repeat
# Graph fragment:
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%slice_1, [1, 1, 1]), kwargs = {})
triton_poi_fused_repeat_0 = async_compile.triton('triton_poi_fused_repeat_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], 
    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_repeat_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_repeat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yj/cyjcqb7cevhaw23pwt3vwv2joobqvxvro7xbjpawpb6fndoft2ip.py
# Topologically Sorted Source Nodes: [b_act], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   b_act => repeat_1
# Graph fragment:
#   %repeat_1 : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%slice_3, [1, 1, 1]), kwargs = {})
triton_poi_fused_repeat_1 = async_compile.triton('triton_poi_fused_repeat_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_1', '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_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (5, 4), (4, 1))
    assert_size_stride(primals_2, (5, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [W_act], Original ATen: [aten.repeat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_repeat_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [b_act], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_1.run(primals_2, buf1, 4, grid=grid(4), stream=stream0)
        del primals_2
    return (buf0, buf1, )


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((5, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((5, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 numpy as np
import torch.nn as nn


class TransformerLinearXMCHead(nn.Module):
    """XMC head for Transformers

    Containing label weight embeddings and label bias embeddings
    """

    def __init__(self, hidden_size, num_labels):
        super().__init__()
        self.label_pad = num_labels
        self.num_labels = num_labels
        self.W = nn.Embedding(num_labels + 1, hidden_size, padding_idx=self
            .label_pad)
        self.b = nn.Embedding(num_labels + 1, 1, padding_idx=self.label_pad)
        self.random_init()

    @property
    def device(self):
        return self.W.weight.device

    def random_init(self):
        """Initialize the weight and bias embeddings

        Initialize label weight embedding with N(0, 0.02) while keeping PAD
        column to be 0. Initialize label bias embedding with 0.
        """
        mat = 0.02 * np.random.randn(self.label_pad, self.W.weight.shape[1])
        mat = np.hstack([mat, np.zeros([mat.shape[0], 1])])
        self.init_from(mat)

    def inherit(self, prev_head, C):
        prev_W = prev_head.W.weight[:-1, :].detach().numpy()
        prev_b = prev_head.b.weight[:-1, :].detach().numpy()
        cur_W = C * prev_W
        cur_b = C * prev_b
        mat = np.hstack([cur_W, cur_b])
        self.init_from(mat)

    def bootstrap(self, prob, **kwargs):
        """Initialize head with weights learned from linear model using transformer embeddings

        Args:
            prob (MLProblem): the multi-label problem to bootstrap with
            kwargs:
                Cp (float): the weight on positive samples. Default 100.0
                Cn (float): the weight on negative samples. Default 100.0
                threshold (float): the threshold to sparsify the model
        """
        Cp = kwargs.get('Cp', 100.0)
        Cn = kwargs.get('Cn', 100.0)
        threshold = kwargs.get('threshold', 0)
        mat = MLModel.train(prob, threshold=threshold, Cp=Cp, Cn=Cn)
        mat = mat.W.toarray().T
        self.init_from(mat)

    def init_from(self, mat):
        """Initialize the weight and bias embeddings with given matrix

        Args:
            mat (ndarray): matrix used for initialize, shape = (nr_labels, hidden_size + 1)
        """
        if not isinstance(mat, np.ndarray):
            raise ValueError('Expect ndarray to initialize label embedding')
        if mat.shape[0] != self.label_pad:
            raise ValueError('nr_labels mismatch!')
        mat = np.vstack([mat, np.zeros([1, mat.shape[1]])])
        self.W = nn.Embedding.from_pretrained(torch.FloatTensor(mat[:, :-1]
            ), freeze=False, sparse=True, padding_idx=self.label_pad)
        self.b = nn.Embedding.from_pretrained(torch.FloatTensor(mat[:, -1])
            .view((self.label_pad + 1, 1)), freeze=False, sparse=True,
            padding_idx=self.label_pad)

    def forward(self, pooled_output=None, output_indices=None, num_device=1):
        if output_indices is None:
            W_act = self.W.weight[:-1, :].repeat(num_device, 1, 1)
            b_act = self.b.weight[:-1].repeat(num_device, 1, 1)
        else:
            output_indices = output_indices
            W_act = self.W(output_indices)
            b_act = self.b(output_indices)
        return W_act, b_act


def get_inputs():
    return []


def get_init_inputs():
    return [[], {'hidden_size': 4, 'num_labels': 4}]

import torch
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 numpy as np
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


@triton.jit
def triton_poi_fused_repeat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (5, 4), (4, 1))
    assert_size_stride(primals_2, (5, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_repeat_1[grid(4)](primals_2, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_2
    return buf0, buf1


class TransformerLinearXMCHeadNew(nn.Module):
    """XMC head for Transformers

    Containing label weight embeddings and label bias embeddings
    """

    def __init__(self, hidden_size, num_labels):
        super().__init__()
        self.label_pad = num_labels
        self.num_labels = num_labels
        self.W = nn.Embedding(num_labels + 1, hidden_size, padding_idx=self
            .label_pad)
        self.b = nn.Embedding(num_labels + 1, 1, padding_idx=self.label_pad)
        self.random_init()

    @property
    def device(self):
        return self.W.weight.device

    def random_init(self):
        """Initialize the weight and bias embeddings

        Initialize label weight embedding with N(0, 0.02) while keeping PAD
        column to be 0. Initialize label bias embedding with 0.
        """
        mat = 0.02 * np.random.randn(self.label_pad, self.W.weight.shape[1])
        mat = np.hstack([mat, np.zeros([mat.shape[0], 1])])
        self.init_from(mat)

    def inherit(self, prev_head, C):
        prev_W = prev_head.W.weight[:-1, :].detach().numpy()
        prev_b = prev_head.b.weight[:-1, :].detach().numpy()
        cur_W = C * prev_W
        cur_b = C * prev_b
        mat = np.hstack([cur_W, cur_b])
        self.init_from(mat)

    def bootstrap(self, prob, **kwargs):
        """Initialize head with weights learned from linear model using transformer embeddings

        Args:
            prob (MLProblem): the multi-label problem to bootstrap with
            kwargs:
                Cp (float): the weight on positive samples. Default 100.0
                Cn (float): the weight on negative samples. Default 100.0
                threshold (float): the threshold to sparsify the model
        """
        Cp = kwargs.get('Cp', 100.0)
        Cn = kwargs.get('Cn', 100.0)
        threshold = kwargs.get('threshold', 0)
        mat = MLModel.train(prob, threshold=threshold, Cp=Cp, Cn=Cn)
        mat = mat.W.toarray().T
        self.init_from(mat)

    def init_from(self, mat):
        """Initialize the weight and bias embeddings with given matrix

        Args:
            mat (ndarray): matrix used for initialize, shape = (nr_labels, hidden_size + 1)
        """
        if not isinstance(mat, np.ndarray):
            raise ValueError('Expect ndarray to initialize label embedding')
        if mat.shape[0] != self.label_pad:
            raise ValueError('nr_labels mismatch!')
        mat = np.vstack([mat, np.zeros([1, mat.shape[1]])])
        self.W = nn.Embedding.from_pretrained(torch.FloatTensor(mat[:, :-1]
            ), freeze=False, sparse=True, padding_idx=self.label_pad)
        self.b = nn.Embedding.from_pretrained(torch.FloatTensor(mat[:, -1])
            .view((self.label_pad + 1, 1)), freeze=False, sparse=True,
            padding_idx=self.label_pad)

    def forward(self):
        primals_1 = self.W.weight
        primals_2 = self.b.weight
        output = call([primals_1, primals_2])
        return output[0], output[1]
