# AOT ID: ['0_inference']
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/zp/czptdn7jot5nhvvxjgbeji75wpdaf2gpuyywmstgg3fjdtzgdimv.py
# Topologically Sorted Source Nodes: [input_1, mul, sub, output, add, output_1], Original ATen: [aten.clamp, aten.mul, aten.sub, aten.round, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   input_1 => clamp_max, clamp_min
#   mul => mul
#   output => round_1
#   output_1 => div
#   sub => sub
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 4), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, 3.75), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 0.0), kwargs = {})
#   %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%sub,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%round_1, 0.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, 3.75), kwargs = {})
triton_poi_fused_add_clamp_div_mul_round_sub_0 = async_compile.triton('triton_poi_fused_add_clamp_div_mul_round_sub_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=[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_add_clamp_div_mul_round_sub_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_add_clamp_div_mul_round_sub_0(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 4.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = 3.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp6 - tmp1
    tmp8 = libdevice.nearbyint(tmp7)
    tmp9 = tmp8 + tmp1
    tmp10 = 0.26666666666666666
    tmp11 = tmp9 * tmp10
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (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: [input_1, mul, sub, output, add, output_1], Original ATen: [aten.clamp, aten.mul, aten.sub, aten.round, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_div_mul_round_sub_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing


def linear_dequantize(input, scale, zero_point, inplace=False):
    if inplace:
        input.add_(zero_point).div_(scale)
        return input
    return (input + zero_point) / scale


def linear_quantize(input, scale, zero_point, inplace=False):
    if inplace:
        input.mul_(scale).sub_(zero_point).round_()
        return input
    return torch.round(scale * input - zero_point)


def _prep_saturation_val_tensor(sat_val):
    is_scalar = not isinstance(sat_val, torch.Tensor)
    out = torch.tensor(sat_val) if is_scalar else sat_val.clone().detach()
    if not out.is_floating_point():
        out = out
    if out.dim() == 0:
        out = out.unsqueeze(0)
    return is_scalar, out


def asymmetric_linear_quantization_params(num_bits, saturation_min,
    saturation_max, integral_zero_point=True, signed=False):
    scalar_min, sat_min = _prep_saturation_val_tensor(saturation_min)
    scalar_max, sat_max = _prep_saturation_val_tensor(saturation_max)
    is_scalar = scalar_min and scalar_max
    if scalar_max and not scalar_min:
        sat_max = sat_max
    elif scalar_min and not scalar_max:
        sat_min = sat_min
    if any(sat_min > sat_max):
        raise ValueError('saturation_min must be smaller than saturation_max')
    n = 2 ** num_bits - 1
    sat_min = torch.min(sat_min, torch.zeros_like(sat_min))
    sat_max = torch.max(sat_max, torch.zeros_like(sat_max))
    diff = sat_max - sat_min
    diff[diff == 0] = n
    scale = n / diff
    zero_point = scale * sat_min
    if integral_zero_point:
        zero_point = zero_point.round()
    if signed:
        zero_point += 2 ** (num_bits - 1)
    if is_scalar:
        return scale.item(), zero_point.item()
    return scale, zero_point


def clamp(input, min, max, inplace=False):
    if inplace:
        input.clamp_(min, max)
        return input
    return torch.clamp(input, min, max)


class LinearQuantizeSTE(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, scale, zero_point, dequantize, inplace):
        if inplace:
            ctx.mark_dirty(input)
        output = linear_quantize(input, scale, zero_point, inplace)
        if dequantize:
            output = linear_dequantize(output, scale, zero_point, inplace)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output, None, None, None, None


class ClippedLinearQuantization(nn.Module):

    def __init__(self, num_bits, clip_val, dequantize=True, inplace=False):
        super(ClippedLinearQuantization, self).__init__()
        self.num_bits = num_bits
        self.clip_val = clip_val
        self.scale, self.zero_point = asymmetric_linear_quantization_params(
            num_bits, 0, clip_val, signed=False)
        self.dequantize = dequantize
        self.inplace = inplace

    def forward(self, input):
        input = clamp(input, 0, self.clip_val, self.inplace)
        input = LinearQuantizeSTE.apply(input, self.scale, self.zero_point,
            self.dequantize, self.inplace)
        return input

    def __repr__(self):
        inplace_str = ', inplace' if self.inplace else ''
        return '{0}(num_bits={1}, clip_val={2}{3})'.format(self.__class__.
            __name__, self.num_bits, self.clip_val, inplace_str)


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


def get_init_inputs():
    return [[], {'num_bits': 4, 'clip_val': 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.testing
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_add_clamp_div_mul_round_sub_0(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 4.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = 3.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp6 - tmp1
    tmp8 = libdevice.nearbyint(tmp7)
    tmp9 = tmp8 + tmp1
    tmp10 = 0.26666666666666666
    tmp11 = tmp9 * tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (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_add_clamp_div_mul_round_sub_0[grid(256)](arg0_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def linear_dequantize(input, scale, zero_point, inplace=False):
    if inplace:
        input.add_(zero_point).div_(scale)
        return input
    return (input + zero_point) / scale


def linear_quantize(input, scale, zero_point, inplace=False):
    if inplace:
        input.mul_(scale).sub_(zero_point).round_()
        return input
    return torch.round(scale * input - zero_point)


def _prep_saturation_val_tensor(sat_val):
    is_scalar = not isinstance(sat_val, torch.Tensor)
    out = torch.tensor(sat_val) if is_scalar else sat_val.clone().detach()
    if not out.is_floating_point():
        out = out
    if out.dim() == 0:
        out = out.unsqueeze(0)
    return is_scalar, out


def asymmetric_linear_quantization_params(num_bits, saturation_min,
    saturation_max, integral_zero_point=True, signed=False):
    scalar_min, sat_min = _prep_saturation_val_tensor(saturation_min)
    scalar_max, sat_max = _prep_saturation_val_tensor(saturation_max)
    is_scalar = scalar_min and scalar_max
    if scalar_max and not scalar_min:
        sat_max = sat_max
    elif scalar_min and not scalar_max:
        sat_min = sat_min
    if any(sat_min > sat_max):
        raise ValueError('saturation_min must be smaller than saturation_max')
    n = 2 ** num_bits - 1
    sat_min = torch.min(sat_min, torch.zeros_like(sat_min))
    sat_max = torch.max(sat_max, torch.zeros_like(sat_max))
    diff = sat_max - sat_min
    diff[diff == 0] = n
    scale = n / diff
    zero_point = scale * sat_min
    if integral_zero_point:
        zero_point = zero_point.round()
    if signed:
        zero_point += 2 ** (num_bits - 1)
    if is_scalar:
        return scale.item(), zero_point.item()
    return scale, zero_point


def clamp(input, min, max, inplace=False):
    if inplace:
        input.clamp_(min, max)
        return input
    return torch.clamp(input, min, max)


class LinearQuantizeSTE(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, scale, zero_point, dequantize, inplace):
        if inplace:
            ctx.mark_dirty(input)
        output = linear_quantize(input, scale, zero_point, inplace)
        if dequantize:
            output = linear_dequantize(output, scale, zero_point, inplace)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output, None, None, None, None


class ClippedLinearQuantizationNew(nn.Module):

    def __init__(self, num_bits, clip_val, dequantize=True, inplace=False):
        super(ClippedLinearQuantizationNew, self).__init__()
        self.num_bits = num_bits
        self.clip_val = clip_val
        self.scale, self.zero_point = asymmetric_linear_quantization_params(
            num_bits, 0, clip_val, signed=False)
        self.dequantize = dequantize
        self.inplace = inplace

    def __repr__(self):
        inplace_str = ', inplace' if self.inplace else ''
        return '{0}(num_bits={1}, clip_val={2}{3})'.format(self.__class__.
            __name__, self.num_bits, self.clip_val, inplace_str)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
