# 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_6/inductor_cache/ez/cezmv74yrhrunjwqrletcmzzbnanma4ylsle3v7w345t7kxp622s.py
# Topologically Sorted Source Nodes: [k_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   k_1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), 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, 4], 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), 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, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2m/c2m2l4qnodgs3ot5g3cwne522qhk3gkewrbzygpibw7yj5zhwduc.py
# Topologically Sorted Source Nodes: [k_1, v_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   k_1 => add
#   v_1 => add_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_7, %primals_3), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_add_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, '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_1(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(in_out_ptr1 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/3w/c3wpg3vhtw2uvjzlz5viwm3dnhvkbjudvttnxifnse5cypcefklu.py
# Topologically Sorted Source Nodes: [sqrt, P_bar_1], Original ATen: [aten.sqrt, aten._softmax]
# Source node to ATen node mapping:
#   P_bar_1 => div_1, exp, sum_1
#   sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 4.4721360206604), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %scalar_tensor_default : [num_users=2] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (1,), kwargs = {dtype: torch.float32, device: cuda:0, pin_memory: False})
#   %ge_scalar : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%full_default, 0), kwargs = {})
#   %neg_default : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%scalar_tensor_default,), kwargs = {})
#   %where_self : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%ge_scalar, %scalar_tensor_default, %neg_default), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %where_self), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_self, %full_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, %mul_tensor_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_sqrt_2 = async_compile.triton('triton_per_fused__softmax_sqrt_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.persistent_reduction(
    size_hints=[64, 32],
    reduction_hint=ReductionHint.INNER,
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_sqrt_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 20
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (20*x0)), rmask & xmask, other=0.0)
    tmp1 = 4.4721360206604
    tmp2 = 0.0
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, float("-inf"))
    tmp11 = triton_helpers.max2(tmp10, 1)[:, None]
    tmp12 = tmp7 - tmp11
    tmp13 = tmp6 * tmp1
    tmp14 = tmp12 / tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp15 / tmp19
    tl.store(out_ptr2 + (r1 + (20*x0)), tmp20, rmask & xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (20, 4), (4, 1))
    assert_size_stride(primals_3, (20, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_1], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(primals_5, buf7, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_5
        buf8 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf8)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 20), (320, 80, 20, 1), 0); del buf1  # reuse
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 20), (320, 80, 20, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [k_1, v_1], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf2, buf9, primals_3, 1280, grid=grid(1280), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((16, 4, 20), (80, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_4, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (16, 4, 20), (80, 20, 1), 0), out=buf3)
        buf6 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [sqrt, P_bar_1], Original ATen: [aten.sqrt, aten._softmax]
        triton_per_fused__softmax_sqrt_2.run(buf3, buf6, 64, 20, grid=grid(64), stream=stream0)
        del buf3
        buf10 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 20), (80, 20, 1), 0), reinterpret_tensor(buf9, (16, 20, 4), (80, 1, 20), 0), out=buf10)
    return (reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(buf9, (16, 4, 20), (80, 20, 1), 0), reinterpret_tensor(primals_4, (16, 4, 4), (16, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
from torch import nn


def get_EF(input_size, dim, method='learnable', head_dim=None, bias=True):
    """
    Retuns the E or F matrix, initialized via xavier initialization.
    This is the recommended way to do it according to the authors of the paper.
    Includes a method for convolution, as well as a method for no additional params.
    """
    assert method == 'learnable' or method == 'convolution' or method == 'no_params', "The method flag needs to be either 'learnable', 'convolution', or 'no_params'!"
    if method == 'convolution':
        conv = nn.Conv1d(head_dim, head_dim, kernel_size=int(input_size /
            dim), stride=int(input_size / dim))
        return conv
    if method == 'no_params':
        mat = torch.zeros((input_size, dim))
        torch.nn.init.normal_(mat, mean=0.0, std=1 / dim)
        return mat
    lin = nn.Linear(input_size, dim, bias)
    torch.nn.init.xavier_normal_(lin.weight)
    return lin


class linformerAttention(nn.Module):

    def __init__(self, dim, dropout, input_size, dim_k=20, full_attention=
        False, parameter_sharing=None):
        super().__init__()
        self.dim = dim
        self.dropout = nn.Dropout(dropout)
        self.dim_k = dim_k
        self.full_attention = full_attention
        self.input_size = input_size
        self.print_dim = False
        self.E = get_EF(input_size, dim=self.dim_k, method='learnable',
            head_dim=self.dim)
        self.F = (get_EF(input_size, dim=self.dim_k, method='learnable',
            head_dim=self.dim) if parameter_sharing == 'none' or 
            parameter_sharing == 'headwise' else self.E)
        self.is_proj_tensor = isinstance(self.E, torch.Tensor)

    def forward(self, q, k, v):
        if self.print_dim:
            None
            None
            None
        if not self.full_attention:
            if self.is_proj_tensor:
                self.E = self.E
                b, h, *_ = q.shape
                projection_E = repeat(self.E, 'j d -> b h j d', b=b, h=h)
                k = torch.einsum('...di,...dj->...ij', k, projection_E)
            else:
                k = torch.einsum('...ij->...ji', k)
                k = self.E(k)
        if self.print_dim:
            None
            None
            None
        q = torch.einsum('...id,...dj->...ij', q, k)
        P_bar = q / torch.sqrt(torch.tensor(self.dim_k).type(q.type()))
        P_bar = P_bar.softmax(dim=-1)
        P_bar = self.dropout(P_bar)
        if not self.full_attention:
            if self.is_proj_tensor:
                self.F = self.F
                v = torch.matmul(v, self.F)
            else:
                v = torch.einsum('...ij->...ji', v)
                v = self.F(v)
        out = torch.einsum('...id,...jd->...ij', P_bar, v)
        return out


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


def get_init_inputs():
    return [[], {'dim': 4, 'dropout': 0.5, 'input_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import 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, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__softmax_sqrt_2(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 20
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 20 * x0), rmask & xmask, other=0.0)
    tmp1 = 4.4721360206604
    tmp2 = 0.0
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, float('-inf'))
    tmp11 = triton_helpers.max2(tmp10, 1)[:, None]
    tmp12 = tmp7 - tmp11
    tmp13 = tmp6 * tmp1
    tmp14 = tmp12 / tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp15 / tmp19
    tl.store(out_ptr2 + (r1 + 20 * x0), tmp20, rmask & xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (20, 4), (4, 1))
    assert_size_stride(primals_3, (20,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](primals_5, buf7, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_5
        buf8 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf8)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 20), (320, 80, 20, 1), 0)
        del buf1
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 20), (320, 80, 20, 1), 0)
        del buf8
        triton_poi_fused_add_1[grid(1280)](buf2, buf9, primals_3, 1280,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((16, 4, 20), (80, 20, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_4, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(buf2, (16, 4, 20), (80, 20, 1), 0),
            out=buf3)
        buf6 = buf2
        del buf2
        triton_per_fused__softmax_sqrt_2[grid(64)](buf3, buf6, 64, 20,
            XBLOCK=8, num_warps=2, num_stages=1)
        del buf3
        buf10 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 20), (80, 20, 1
            ), 0), reinterpret_tensor(buf9, (16, 20, 4), (80, 1, 20), 0),
            out=buf10)
    return reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf9, (16, 4, 20), (80, 20, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4, 4), (16, 1, 4), 0)


def get_EF(input_size, dim, method='learnable', head_dim=None, bias=True):
    """
    Retuns the E or F matrix, initialized via xavier initialization.
    This is the recommended way to do it according to the authors of the paper.
    Includes a method for convolution, as well as a method for no additional params.
    """
    assert method == 'learnable' or method == 'convolution' or method == 'no_params', "The method flag needs to be either 'learnable', 'convolution', or 'no_params'!"
    if method == 'convolution':
        conv = nn.Conv1d(head_dim, head_dim, kernel_size=int(input_size /
            dim), stride=int(input_size / dim))
        return conv
    if method == 'no_params':
        mat = torch.zeros((input_size, dim))
        torch.nn.init.normal_(mat, mean=0.0, std=1 / dim)
        return mat
    lin = nn.Linear(input_size, dim, bias)
    torch.nn.init.xavier_normal_(lin.weight)
    return lin


class linformerAttentionNew(nn.Module):

    def __init__(self, dim, dropout, input_size, dim_k=20, full_attention=
        False, parameter_sharing=None):
        super().__init__()
        self.dim = dim
        self.dropout = nn.Dropout(dropout)
        self.dim_k = dim_k
        self.full_attention = full_attention
        self.input_size = input_size
        self.print_dim = False
        self.E = get_EF(input_size, dim=self.dim_k, method='learnable',
            head_dim=self.dim)
        self.F = (get_EF(input_size, dim=self.dim_k, method='learnable',
            head_dim=self.dim) if parameter_sharing == 'none' or 
            parameter_sharing == 'headwise' else self.E)
        self.is_proj_tensor = isinstance(self.E, torch.Tensor)

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.E.weight
        primals_3 = self.E.bias
        primals_1 = input_0
        primals_4 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
