# 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_2/inductor_cache/44/c443e4qjhu2egd7bs5l5244as6qcrvoh4ircgpisnx4w4opzphoh.py
# Topologically Sorted Source Nodes: [left_index, right_index, setitem, setitem_1, or_, invert, setitem_2], Original ATen: [aten.lt, aten.ge, aten.lift_fresh, aten.index_put, aten.bitwise_or, aten.bitwise_not]
# Source node to ATen node mapping:
#   invert => bitwise_not
#   left_index => lt
#   or_ => bitwise_or
#   right_index => ge
#   setitem => full_default, index_put
#   setitem_1 => full_default_1, index_put_1
#   setitem_2 => full_default_2, index_put_2
# Graph fragment:
#   %lt : [num_users=3] = call_function[target=torch.ops.aten.lt.Scalar](args = (%primals_1, -0.25), kwargs = {})
#   %ge : [num_users=3] = call_function[target=torch.ops.aten.ge.Scalar](args = (%primals_1, 0.25), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%primals_1, [%lt], %full_default), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_1 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%ge], %full_default_1), kwargs = {})
#   %bitwise_or : [num_users=1] = call_function[target=torch.ops.aten.bitwise_or.Tensor](args = (%lt, %ge), kwargs = {})
#   %bitwise_not : [num_users=2] = call_function[target=torch.ops.aten.bitwise_not.default](args = (%bitwise_or,), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_1, [%bitwise_not], %full_default_2), kwargs = {})
triton_poi_fused_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0 = async_compile.triton('triton_poi_fused_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_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: '*i1', 3: '*i1', 4: '*i1', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, 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)
    tmp1 = -0.25
    tmp2 = tmp0 < tmp1
    tmp3 = -1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = 0.25
    tmp6 = tmp0 >= tmp5
    tmp7 = 1.0
    tmp8 = tl.where(tmp6, tmp7, tmp4)
    tmp9 = tmp2 | tmp6
    tmp10 = tmp9 == 0
    tmp11 = 0.0
    tmp12 = tl.where(tmp10, tmp11, tmp8)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp6, xmask)
    tl.store(out_ptr2 + (x0), tmp10, xmask)
    tl.store(in_out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [left_index, right_index, setitem, setitem_1, or_, invert, setitem_2], Original ATen: [aten.lt, aten.ge, aten.lift_fresh, aten.index_put, aten.bitwise_or, aten.bitwise_not]
        stream0 = get_raw_stream(0)
        triton_poi_fused_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0.run(buf5, primals_1, buf0, buf1, buf4, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del buf5
        del primals_2
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf0, buf1, buf4, reinterpret_tensor(primals_3, (64, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = 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])
    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._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 import nn
from torch.nn import 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_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, 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)
    tmp1 = -0.25
    tmp2 = tmp0 < tmp1
    tmp3 = -1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = 0.25
    tmp6 = tmp0 >= tmp5
    tmp7 = 1.0
    tmp8 = tl.where(tmp6, tmp7, tmp4)
    tmp9 = tmp2 | tmp6
    tmp10 = tmp9 == 0
    tmp11 = 0.0
    tmp12 = tl.where(tmp10, tmp11, tmp8)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)
    tl.store(out_ptr2 + x0, tmp10, xmask)
    tl.store(in_out_ptr0 + x0, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = buf3
        del buf3
        get_raw_stream(0)
        triton_poi_fused_bitwise_not_bitwise_or_ge_index_put_lift_fresh_lt_0[
            grid(16)](buf5, primals_1, buf0, buf1, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_1
        buf6 = 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(buf5, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf6)
        del buf5
        del primals_2
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf0, buf1, buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class Ternary(nn.Module):
    """
    Ternarize the input activations to -1, 0, 1.
    """

    def __init__(self, left=-0.25, right=0.25):
        super().__init__()
        self.left = left
        self.right = right

    def forward(self, input):
        input = input.clone()
        left_index = input.lt(self.left)
        right_index = input.ge(self.right)
        input[left_index] = -1
        input[right_index] = 1
        input[~(left_index | right_index)] = 0
        return input

    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class TernaryLinearNew(nn.Module):

    def __init__(self, in_features, out_features, ternarize_left=-0.25,
        ternarize_right=0.25, bias=True):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.Tensor(out_features, in_features))
        self.ternarize = Ternary(ternarize_left, ternarize_right)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        init.normal_(self.weight.data, 0, 1)

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