# 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/ao/caoovxtqrx42gvkmjirowqmmbh6kppvfh5ebrzzv4kzkgwm2umii.py
# Topologically Sorted Source Nodes: [w], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   w => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[64], 
    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_clone_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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1)), xmask)
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ah/cahcbdgzcypclgrmenrcgftl53kemvcm53v6yoxzwdqjyblrincb.py
# Topologically Sorted Source Nodes: [s], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   s => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_9,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[256], 
    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_clone_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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ue/cuezbfqwohbeaz3pb7qhlpx5cqgl5yrcj3nds7mhmzqk7fidklrx.py
# Topologically Sorted Source Nodes: [s], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   s => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_12,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
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, 16], 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', '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_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    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 % 4
    x3 = (xindex // 4)
    y0 = yindex % 4
    y1 = (yindex // 4)
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x3) + (16*x2) + (64*y1)), xmask & ymask)
    tl.store(out_ptr0 + (x5 + (16*y4)), tmp0, xmask & ymask)
''', 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), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [w], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [w], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0), out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 1, 4, 1), (64, 16, 4, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (4, 4, 16), (64, 16, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_4, reinterpret_tensor(buf2, (4, 4, 16), (64, 16, 1), 0), out=buf3)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf3, buf4, 16, 16, grid=grid(16, 16), stream=stream0)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf4, (4, 4, 16), (64, 16, 1), 0), out=buf5)
        del buf4
    return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 4, 1, 16), 0), reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 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


class Triaffine(nn.Module):
    """
    Triaffine layer for second-order scoring.

    This function has a tensor of weights `W` and bias terms if needed.
    The score `s(x, y, z)` of the vector triple `(x, y, z)` is computed as `x^T z^T W y`.
    Usually, `x` and `y` can be concatenated with bias terms.

    References:
        - Yu Zhang, Zhenghua Li and Min Zhang (ACL'20)
          Efficient Second-Order TreeCRF for Neural Dependency Parsing
          https://www.aclweb.org/anthology/2020.acl-main.302/
        - Xinyu Wang, Jingxian Huang, and Kewei Tu (ACL'19)
          Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
          https://www.aclweb.org/anthology/P19-1454/

    Args:
        n_in (int):
            The dimension of the input feature.
        bias_x (bool):
            If True, add a bias term for tensor x. Default: False.
        bias_y (bool):
            If True, add a bias term for tensor y. Default: False.
    """

    def __init__(self, n_in, bias_x=False, bias_y=False):
        super().__init__()
        self.n_in = n_in
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.weight = nn.Parameter(torch.Tensor(n_in + bias_x, n_in, n_in +
            bias_y))
        self.reset_parameters()

    def extra_repr(self):
        s = f'n_in={self.n_in}'
        if self.bias_x:
            s += f', bias_x={self.bias_x}'
        if self.bias_y:
            s += f', bias_y={self.bias_y}'
        return s

    def reset_parameters(self):
        nn.init.zeros_(self.weight)

    def forward(self, x, y, z):
        """
        Args:
            x (torch.Tensor): [batch_size, seq_len, n_in]
            y (torch.Tensor): [batch_size, seq_len, n_in]
            z (torch.Tensor): [batch_size, seq_len, n_in]

        Returns:
            s (torch.Tensor): [batch_size, seq_len, seq_len, seq_len]
        """
        if self.bias_x:
            x = torch.cat((x, torch.ones_like(x[..., :1])), -1)
        if self.bias_y:
            y = torch.cat((y, torch.ones_like(y[..., :1])), -1)
        w = torch.einsum('bzk,ikj->bzij', z, self.weight)
        s = torch.einsum('bxi,bzij,byj->bzxy', x, w, y)
        return s


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


def get_init_inputs():
    return [[], {'n_in': 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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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 % 4
    x3 = xindex // 4
    y0 = yindex % 4
    y1 = yindex // 4
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)


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), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0),
            out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 1, 4, 1), (64, 16, 4, 4, 1, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 16), (64, 16, 1), 0)
        del buf1
        extern_kernels.bmm(primals_4, reinterpret_tensor(buf2, (4, 4, 16),
            (64, 16, 1), 0), out=buf3)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 
            1, 1), 0)
        del buf2
        triton_poi_fused_clone_2[grid(16, 16)](buf3, buf4, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf4, (4, 4, 16),
            (64, 16, 1), 0), out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 4, 1, 16), 0
        ), reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 0)


class TriaffineNew(nn.Module):
    """
    Triaffine layer for second-order scoring.

    This function has a tensor of weights `W` and bias terms if needed.
    The score `s(x, y, z)` of the vector triple `(x, y, z)` is computed as `x^T z^T W y`.
    Usually, `x` and `y` can be concatenated with bias terms.

    References:
        - Yu Zhang, Zhenghua Li and Min Zhang (ACL'20)
          Efficient Second-Order TreeCRF for Neural Dependency Parsing
          https://www.aclweb.org/anthology/2020.acl-main.302/
        - Xinyu Wang, Jingxian Huang, and Kewei Tu (ACL'19)
          Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
          https://www.aclweb.org/anthology/P19-1454/

    Args:
        n_in (int):
            The dimension of the input feature.
        bias_x (bool):
            If True, add a bias term for tensor x. Default: False.
        bias_y (bool):
            If True, add a bias term for tensor y. Default: False.
    """

    def __init__(self, n_in, bias_x=False, bias_y=False):
        super().__init__()
        self.n_in = n_in
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.weight = nn.Parameter(torch.Tensor(n_in + bias_x, n_in, n_in +
            bias_y))
        self.reset_parameters()

    def extra_repr(self):
        s = f'n_in={self.n_in}'
        if self.bias_x:
            s += f', bias_x={self.bias_x}'
        if self.bias_y:
            s += f', bias_y={self.bias_y}'
        return s

    def reset_parameters(self):
        nn.init.zeros_(self.weight)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.weight
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
