'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
import json
import math
import importlib
import torch
from torch import nn
from torch.autograd import Function

from ..op_builder import QuantizerBuilder

# Cuda modules will be imported if needed
quantizer_cuda_module = None


def ds_quantizer(input, groups=1, bit_num=8, sr=False, asym=False):
    # Load cuda modules if needed
    global quantizer_cuda_module
    if quantizer_cuda_module is None:
        quantizer_cuda_module = QuantizerBuilder().load()
    if sr:
        if asym:
            quantize_func = quantizer_cuda_module.ds_sr_quantize_asym_fp16 if input.dtype == torch.half else quantizer_cuda_module.ds_sr_quantize_asym_fp32
        else:
            quantize_func = quantizer_cuda_module.ds_sr_quantize_fp16 if input.dtype == torch.half else quantizer_cuda_module.ds_sr_quantize_fp32
    else:
        if asym:
            quantize_func = quantizer_cuda_module.ds_quantize_asym_fp16 if input.dtype == torch.half else quantizer_cuda_module.ds_quantize_asym_fp32
        else:
            quantize_func = quantizer_cuda_module.ds_quantize_fp16 if input.dtype == torch.half else quantizer_cuda_module.ds_quantize_fp32
    return quantize_func(input, groups, bit_num)