# Code coming from https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/bnn_pynq
# MIT License
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# Copyright (c) 2019 Xilinx
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.utils.prune as prune
from brevitas.core.bit_width import BitWidthImplType
from brevitas.core.quant import QuantType
from brevitas.core.restrict_val import FloatToIntImplType, RestrictValueType
from brevitas.core.scaling import ScalingImplType
from brevitas.core.zero_point import ZeroZeroPoint
from brevitas.inject import ExtendedInjector
from brevitas.nn import QuantConv2d, QuantIdentity, QuantLinear
from brevitas.quant import Int8WeightPerTensorFloat
from brevitas.quant.solver import ActQuantSolver, WeightQuantSolver
from dependencies import value
from torch.nn import AvgPool2d, BatchNorm1d, BatchNorm2d, Conv2d, MaxPool2d, Module, ModuleList

# -- Quantizers --


class CommonQuant(ExtendedInjector):
    bit_width_impl_type = BitWidthImplType.CONST
    scaling_impl_type = ScalingImplType.CONST
    restrict_scaling_type = RestrictValueType.FP
    zero_point_impl = ZeroZeroPoint
    float_to_int_impl_type = FloatToIntImplType.ROUND
    scaling_per_output_channel = False
    narrow_range = True
    signed = True

    @value
    def quant_type(bit_width):
        if bit_width is None:
            return QuantType.FP
        elif bit_width == 1:
            return QuantType.BINARY
        else:
            return QuantType.INT


class CommonIntWeightPerTensorQuant(Int8WeightPerTensorFloat):
    """
    Common per-tensor weight quantizer with bit-width set to None so that it is forced to be
    specified by each layer.
    """

    scaling_min_val = 2e-16
    bit_width = None


class CommonIntWeightPerChannelQuant(CommonIntWeightPerTensorQuant):
    """
    Common per-channel weight quantizer with bit-width set to None so that it is forced to be
    specified by each layer.
    """

    scaling_per_output_channel = True


class CommonWeightQuant(CommonQuant, WeightQuantSolver):
    scaling_const = 1.0


class CommonActQuant(CommonQuant, ActQuantSolver):
    min_val = -1.0
    max_val = 1.0


# -- Custom layer --


class TensorNorm(nn.Module):
    def __init__(self, eps=1e-4, momentum=0.1):
        super().__init__()

        self.eps = eps
        self.momentum = momentum
        self.weight = nn.Parameter(torch.rand(1))
        self.bias = nn.Parameter(torch.rand(1))
        self.register_buffer("running_mean", torch.zeros(1))
        self.register_buffer("running_var", torch.ones(1))
        self.reset_running_stats()

    def reset_running_stats(self):
        self.running_mean.zero_()
        self.running_var.fill_(1)
        init.ones_(self.weight)
        init.zeros_(self.bias)

    def forward(self, x):
        if self.training:
            mean = x.mean()
            unbias_var = x.var(unbiased=True)
            biased_var = x.var(unbiased=False)
            self.running_mean = (
                1 - self.momentum
            ) * self.running_mean + self.momentum * mean.detach()
            self.running_var = (
                1 - self.momentum
            ) * self.running_var + self.momentum * unbias_var.detach()
            inv_std = 1 / (biased_var + self.eps).pow(0.5)
            return (x - mean) * inv_std * self.weight + self.bias
        else:
            return (
                (x - self.running_mean) / (self.running_var + self.eps).pow(0.5)
            ) * self.weight + self.bias
