import torch
from torch import Tensor
import numpy as np
from torch.ao.quantization.experimental.apot_utils import float_to_apot, apot_to_float

# class to store APoT quantizer and
# implement quantize and dequantize
class APoTQuantizer():
    alpha: torch.Tensor
    gamma: torch.Tensor
    quantization_levels: torch.Tensor
    level_indices: torch.Tensor

    def __init__(
        self,
        alpha: torch.Tensor,
        gamma: torch.Tensor,
        quantization_levels: torch.Tensor,
            level_indices: torch.Tensor) -> None:
        self.alpha = alpha
        self.gamma = gamma
        self.quantization_levels = quantization_levels
        self.level_indices = level_indices

    r""" Quantizes fp Tensor to integer APoT representation.
    Conversion is based on the qparams from a specified APoT non-uniform observer.
    The approach follows the method outlined in the APoT paper: https://arxiv.org/pdf/1909.13144.pdf.
    Args:
        tensor2quantize: fp Tensor
    Returns:
        result: APoT Tensor representation of tensor2quantize
    """
    def quantize(self, tensor2quantize: Tensor):
        result = torch.tensor([])

        # map float_to_apot over tensor2quantize elements
        tensor2quantize = tensor2quantize.detach().apply_(lambda x: float_to_apot(x,
                                                                                  self.quantization_levels,
                                                                                  self.level_indices,
                                                                                  self.alpha))

        # convert to APoT int representation for dtype
        tensor2quantize = tensor2quantize.int()

        from torch.ao.quantization.experimental.APoT_tensor import TensorAPoT

        result = TensorAPoT(self, tensor2quantize)

        return result

    r""" Dequantizes integer Tensor to floating point (fp) representation
    based on the calculated quantization levels from a specified APoT non-uniform observer.
    The approach follows the method outlined in the APoT paper: https://arxiv.org/pdf/1909.13144.pdf.
    Args:
        apot_tensor: quantized APoT Tensor to dequantize
    Returns:
        result: fp representation of input Tensor
    """
    def dequantize(self, apot_tensor) -> Tensor:
        apot_tensor_data = apot_tensor.data

        # map apot_to_float over tensor2quantize elements
        result_temp = np.empty(apot_tensor_data.size())
        for ele in apot_tensor_data:
            new_ele = apot_to_float(ele, self.quantization_levels, self.level_indices)
            np.append(result_temp, new_ele)

        result = torch.from_numpy(result_temp).int()

        return result

    def q_apot_alpha(self) -> float:
        raise NotImplementedError

r""" Global method to create quantizer and call quantizer quantize_APoT
    Args:
        tensor2quantize: fp Tensor to quantize
        alpha: Tensor qparam alpha (clipping level)
        gamma: Tensor qparam gamma (scale factor for quantization levels)
        quantization levels: Tensor with fp quantization levels
        level indices: Tensor with integer quantization level indices
    Returns:
        result: ApoT Tensor representation of tensor2quantize
"""
def quantize_APoT(tensor2quantize: Tensor, alpha: Tensor, gamma: Tensor, quantization_levels: Tensor, level_indices: Tensor):
    quantizer = APoTQuantizer(alpha=alpha, gamma=gamma, quantization_levels=quantization_levels, level_indices=level_indices)
    result = quantizer.quantize(tensor2quantize)
    return result

r""" Global method to create quantizer and call quantizer dequantize_APoT
    Args:
        apot_tensor: APoT Tensor to dequantize
    Returns:
        result: fp Tensor dequantized from apot_tensor
"""
def dequantize_APoT(apot_tensor) -> Tensor:
    quantizer = apot_tensor.quantizer
    result = quantizer.dequantize(apot_tensor)
    return result