# 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/sb/csbujq4zm64oktpi7gz7gmu6ncokchjdmaiollfhsvidqxvzf3z5.py
# Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   crf_scores => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_2, %expand_1), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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=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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_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
    x2 = (xindex // 16) % 16
    x5 = xindex % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (x6), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(buf0, primals_3, primals_4, buf1, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del primals_3
        del primals_4
    return (buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), )


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


class CRF(nn.Module):
    """
    Conditional Random Field Module

    Parameters
    ----------
    hidden_dim : ``int``, required.
        the dimension of the input features.
    tagset_size : ``int``, required.
        the size of the target labels.
    a_num : ``int``, required.
        the number of annotators.
    task : ``str``, required.
        the model task
    if_bias: ``bool``, optional, (default=True).
        whether the linear transformation has the bias term.
    """

    def __init__(self, hidden_dim: 'int', tagset_size: 'int', a_num: 'int',
        task: 'str', if_bias: 'bool'=True):
        super(CRF, self).__init__()
        self.tagset_size = tagset_size
        self.a_num = a_num
        self.task = task
        if 'maMulVecCrowd' in self.task:
            self.maMulVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
        if 'maAddVecCrowd' in self.task:
            self.maAddVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
        if 'maCatVecCrowd' in self.task:
            self.maCatVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
            self.maCatVecCrowd_latent = nn.Parameter(torch.Tensor(self.
                tagset_size))
        if 'maMulMatCrowd' in self.task:
            self.maMulMatCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size, self.tagset_size))
        if 'maMulCRFCrowd' in self.task:
            self.maMulCRFCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size, self.tagset_size))
        if 'maMulScoreCrowd' in self.task:
            self.maMulScoreCrowd = nn.Parameter(torch.Tensor(self.a_num,
                self.tagset_size, self.tagset_size))
        if 'maCatVecCrowd' in self.task:
            self.hidden2tag = nn.Linear(hidden_dim + self.tagset_size, self
                .tagset_size, bias=if_bias)
        else:
            self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size, bias=
                if_bias)
        if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
            self.transitions = nn.Parameter(torch.Tensor(self.tagset_size,
                self.tagset_size))

    def rand_init(self):
        """
        random initialization
        """
        if 'maMulVecCrowd' in self.task:
            self.maMulVecCrowd.data.fill_(1)
        if 'maAddVecCrowd' in self.task:
            self.maAddVecCrowd.data.fill_(0)
        if 'maCatVecCrowd' in self.task:
            self.maCatVecCrowd.data.fill_(0)
            self.maCatVecCrowd_latent.data.fill_(0)
        if 'maMulMatCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulMatCrowd[i])
        if 'maMulCRFCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulCRFCrowd[i])
        if 'maMulScoreCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulScoreCrowd[i])
        utils.init_linear(self.hidden2tag)
        if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
            self.transitions.data.zero_()

    def forward(self, feats):
        """
        calculate the potential score for the conditional random field.

        Parameters
        ----------
        feats: ``torch.FloatTensor``, required.
            the input features for the conditional random field, of shape (*, hidden_dim).

        Returns
        -------
        output: ``torch.FloatTensor``.
            A float tensor of shape (ins_num, from_tag_size, to_tag_size)
        """
        seq_len, batch_size, hid_dim = feats.shape
        ins_num = seq_len * batch_size
        self.a_num * ins_num
        if 'maCatVecCrowd' in self.task:
            feats_expand = feats.expand(self.a_num, seq_len, batch_size,
                hid_dim)
            crowd_expand = self.maCatVecCrowd.unsqueeze(1).unsqueeze(2).expand(
                self.a_num, seq_len, batch_size, self.tagset_size)
            feats_cat = torch.cat([feats_expand, crowd_expand], 3)
            scores = self.hidden2tag(feats_cat).view(self.a_num, ins_num, 1,
                self.tagset_size)
        else:
            scores = self.hidden2tag(feats).view(1, ins_num, 1, self.
                tagset_size).expand(self.a_num, ins_num, 1, self.tagset_size)
        if 'maMulVecCrowd' in self.task:
            crowd_expand = self.maMulVecCrowd.unsqueeze(1).unsqueeze(2).expand(
                self.a_num, ins_num, 1, self.tagset_size)
            scores = torch.mul(scores, crowd_expand)
        if 'maAddVecCrowd' in self.task:
            crowd_expand = self.maAddVecCrowd.unsqueeze(1).unsqueeze(2).expand(
                self.a_num, ins_num, 1, self.tagset_size)
            scores = scores + crowd_expand
        if 'maMulMatCrowd' in self.task:
            crowd = F.log_softmax(self.maMulMatCrowd, dim=2)
            crowd = crowd.view(self.a_num, 1, self.tagset_size, self.
                tagset_size).expand(self.a_num, ins_num, self.tagset_size,
                self.tagset_size)
            scores = torch.matmul(scores, crowd)
        if 'maMulCRFCrowd' in self.task and 'latent' in self.task:
            transitions = self.transitions.view(1, 1, self.tagset_size,
                self.tagset_size)
            transitions = torch.matmul(transitions, self.maMulCRFCrowd
                ).transpose(0, 1).contiguous()
        elif 'maMulCRFCrowd' in self.task:
            transitions = self.maMulCRFCrowd.view(self.a_num, 1, self.
                tagset_size, self.tagset_size).expand(self.a_num, ins_num,
                self.tagset_size, self.tagset_size)
        else:
            transitions = self.transitions.view(1, 1, self.tagset_size,
                self.tagset_size).expand(self.a_num, ins_num, self.
                tagset_size, self.tagset_size)
        scores = scores.expand(self.a_num, ins_num, self.tagset_size, self.
            tagset_size)
        crf_scores = scores + transitions
        if 'maMulScoreCrowd' in self.task:
            crowd = self.maMulScoreCrowd.view(self.a_num, 1, self.
                tagset_size, self.tagset_size).expand(self.a_num, ins_num,
                self.tagset_size, self.tagset_size)
            crf_scores = torch.matmul(crf_scores, crowd).view(self.a_num,
                ins_num, self.tagset_size, self.tagset_size)
        return crf_scores

    def latent_forward(self, feats):
        """
        ignoring crowd components
        """
        seq_len, batch_size, _hid_dim = feats.shape
        if 'maCatVecCrowd' in self.task:
            crowd_zero = self.maCatVecCrowd_latent.view(1, 1, self.tagset_size
                ).expand(seq_len, batch_size, self.tagset_size)
            feats = torch.cat([feats, crowd_zero], 2)
        scores = self.hidden2tag(feats).view(-1, 1, self.tagset_size)
        ins_num = scores.size(0)
        crf_scores = scores.expand(ins_num, self.tagset_size, self.tagset_size
            ) + self.transitions.view(1, self.tagset_size, self.tagset_size
            ).expand(ins_num, self.tagset_size, self.tagset_size)
        return crf_scores


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


def get_init_inputs():
    return [[], {'hidden_dim': 4, 'tagset_size': 4, 'a_num': 4, 'task': [4, 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
import torch.nn.init
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_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
    x2 = xindex // 16 % 16
    x5 = xindex % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x6, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(1024)](buf0, primals_3, primals_4, buf1,
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        del primals_4
    return buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0)


class CRFNew(nn.Module):
    """
    Conditional Random Field Module

    Parameters
    ----------
    hidden_dim : ``int``, required.
        the dimension of the input features.
    tagset_size : ``int``, required.
        the size of the target labels.
    a_num : ``int``, required.
        the number of annotators.
    task : ``str``, required.
        the model task
    if_bias: ``bool``, optional, (default=True).
        whether the linear transformation has the bias term.
    """

    def __init__(self, hidden_dim: 'int', tagset_size: 'int', a_num: 'int',
        task: 'str', if_bias: 'bool'=True):
        super(CRFNew, self).__init__()
        self.tagset_size = tagset_size
        self.a_num = a_num
        self.task = task
        if 'maMulVecCrowd' in self.task:
            self.maMulVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
        if 'maAddVecCrowd' in self.task:
            self.maAddVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
        if 'maCatVecCrowd' in self.task:
            self.maCatVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size))
            self.maCatVecCrowd_latent = nn.Parameter(torch.Tensor(self.
                tagset_size))
        if 'maMulMatCrowd' in self.task:
            self.maMulMatCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size, self.tagset_size))
        if 'maMulCRFCrowd' in self.task:
            self.maMulCRFCrowd = nn.Parameter(torch.Tensor(self.a_num, self
                .tagset_size, self.tagset_size))
        if 'maMulScoreCrowd' in self.task:
            self.maMulScoreCrowd = nn.Parameter(torch.Tensor(self.a_num,
                self.tagset_size, self.tagset_size))
        if 'maCatVecCrowd' in self.task:
            self.hidden2tag = nn.Linear(hidden_dim + self.tagset_size, self
                .tagset_size, bias=if_bias)
        else:
            self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size, bias=
                if_bias)
        if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
            self.transitions = nn.Parameter(torch.Tensor(self.tagset_size,
                self.tagset_size))

    def rand_init(self):
        """
        random initialization
        """
        if 'maMulVecCrowd' in self.task:
            self.maMulVecCrowd.data.fill_(1)
        if 'maAddVecCrowd' in self.task:
            self.maAddVecCrowd.data.fill_(0)
        if 'maCatVecCrowd' in self.task:
            self.maCatVecCrowd.data.fill_(0)
            self.maCatVecCrowd_latent.data.fill_(0)
        if 'maMulMatCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulMatCrowd[i])
        if 'maMulCRFCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulCRFCrowd[i])
        if 'maMulScoreCrowd' in self.task:
            for i in range(self.a_num):
                nn.init.eye_(self.maMulScoreCrowd[i])
        utils.init_linear(self.hidden2tag)
        if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
            self.transitions.data.zero_()

    def latent_forward(self, feats):
        """
        ignoring crowd components
        """
        seq_len, batch_size, _hid_dim = feats.shape
        if 'maCatVecCrowd' in self.task:
            crowd_zero = self.maCatVecCrowd_latent.view(1, 1, self.tagset_size
                ).expand(seq_len, batch_size, self.tagset_size)
            feats = torch.cat([feats, crowd_zero], 2)
        scores = self.hidden2tag(feats).view(-1, 1, self.tagset_size)
        ins_num = scores.size(0)
        crf_scores = scores.expand(ins_num, self.tagset_size, self.tagset_size
            ) + self.transitions.view(1, self.tagset_size, self.tagset_size
            ).expand(ins_num, self.tagset_size, self.tagset_size)
        return crf_scores

    def forward(self, input_0):
        primals_2 = self.transitions
        primals_4 = self.hidden2tag.weight
        primals_3 = self.hidden2tag.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
