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# Source code for brevitas.core.quant.ternary

    # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
    # SPDX-License-Identifier: BSD-3-Clause

    from typing import Tuple

    import torch
    from torch import Tensor
    from torch.nn import Module

    import brevitas
    from brevitas.core.bit_width import BitWidthConst
    from brevitas.core.quant.delay import DelayWrapper
    from brevitas.core.utils import StatelessBuffer
    from brevitas.function.ops_ste import ternary_sign_ste

    class TernaryQuant(brevitas.jit.ScriptModule):
        """
        ScriptModule that implements scaled uniform ternary quantization of an input tensor.
        Quantization is performed with :func:`~brevitas.function.ops_ste.ternary_sign_ste`.

        Args:
            scaling_impl (Module): Module that returns a scale factor.
            threshold (float): Ternarization threshold w.r.t. to the scale factor.
            quant_delay_steps (int): Number of training steps to delay quantization for. Default: 0

        Returns:
            Tuple[Tensor, Tensor, Tensor, Tensor]: Quantized output in de-quantized format, scale,
                zero-point, bit_width.

        Examples:
            >>> from brevitas.core.scaling import ConstScaling
            >>> ternary_quant = TernaryQuant(ConstScaling(1.0), 0.5)
            >>> inp = torch.Tensor([0.04, -0.6, 3.3])
            >>> out, scale, zero_point, bit_width = ternary_quant(inp)
            >>> out
            tensor([ 0., -1.,  1.])
            >>> scale
            tensor(1.)
            >>> zero_point
            tensor(0.)
            >>> bit_width
            tensor(2.)

        Note:
            Maps to quant_type == QuantType.TERNARY == 'TERNARY' == 'ternary' in higher-level APIs.

        Note:
            Set env variable BREVITAS_JIT=1 to enable TorchScript compilation of this module.
        """

        __constants__ = ['threshold']

        def __init__(self, scaling_impl: Module, threshold: float, quant_delay_steps: int = None):
            super(TernaryQuant, self).__init__()
            self.scaling_impl = scaling_impl
            self.threshold = threshold
            self.bit_width = BitWidthConst(2)
            self.zero_point = StatelessBuffer(torch.tensor(0.0))
            self.delay_wrapper = DelayWrapper(quant_delay_steps)

        @brevitas.jit.script_method
        def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
            scale = self.scaling_impl(x)
            mask = x.abs().gt(self.threshold * scale)
            y = mask.float() * ternary_sign_ste(x)
            y = y * scale
            y = self.delay_wrapper(x, y)
            return y, scale, self.zero_point(), self.bit_width()