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# Source code for brevitas.core.quant.int_base

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

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

    import brevitas
    from brevitas.core.function_wrapper import RoundSte
    from brevitas.core.function_wrapper import TensorClamp
    from brevitas.core.quant.delay import DelayWrapper
    from brevitas.function.ops import max_int
    from brevitas.function.ops import min_int

    class IntQuant(brevitas.jit.ScriptModule):
        """
        ScriptModule that implements scale, shifted, uniform integer quantization of an input tensor,
        according to an input scale, zero-point and bit-width.

        Args:
            narrow_range (bool): Flag that determines whether restrict quantization to a narrow range or not.
            signed (bool): Flag that determines whether to quantize to a signed range or not.
            float_to_int_impl (Module): Module that performs the conversion from floating point to
                integer representation. Default: RoundSte()
            tensor_clamp_impl (Module): Module that performs clamping. Default: TensorClamp()
            quant_delay_steps (int): Number of training steps to delay quantization for. Default: 0

        Returns:
            Tensor: Quantized output in de-quantized format.

        Examples:
            >>> from brevitas.core.scaling import ConstScaling
            >>> int_quant = IntQuant(narrow_range=True, signed=True)
            >>> scale, zero_point, bit_width = torch.tensor(0.01), torch.tensor(0.), torch.tensor(4.)
            >>> inp = torch.Tensor([0.042, -0.053, 0.31, -0.44])
            >>> out = int_quant(scale, zero_point, bit_width, inp)
            >>> out
            tensor([ 0.0400, -0.0500,  0.0700, -0.0700])

        Note:
            Maps to quant_type == QuantType.INT == 'INT' == 'int' in higher-level APIs.

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

        __constants__ = ['signed', 'narrow_range']

        def __init__(
                self,
                narrow_range: bool,
                signed: bool,
                input_view_impl: Module,
                float_to_int_impl: Module = RoundSte(),
                tensor_clamp_impl: Module = TensorClamp(),
                quant_delay_steps: int = 0):
            super(IntQuant, self).__init__()
            self.float_to_int_impl = float_to_int_impl
            self.tensor_clamp_impl = tensor_clamp_impl
            self.signed = signed
            self.narrow_range = narrow_range
            self.delay_wrapper = DelayWrapper(quant_delay_steps)
            self.input_view_impl = input_view_impl

        @brevitas.jit.script_method
        def to_int(self, scale: Tensor, zero_point: Tensor, bit_width: Tensor, x: Tensor) -> Tensor:
            x = self.input_view_impl(x)
            y = x / scale
            y = y + zero_point
            min_int_val = self.min_int(bit_width)
            max_int_val = self.max_int(bit_width)
            y = self.float_to_int_impl(y)
            y = self.tensor_clamp_impl(y, min_val=min_int_val, max_val=max_int_val)
            return y

        @brevitas.jit.script_method
        def min_int(self, bit_width):
            return min_int(self.signed, self.narrow_range, bit_width)

        @brevitas.jit.script_method
        def max_int(self, bit_width):
            return max_int(self.signed, self.narrow_range, bit_width)

        @brevitas.jit.script_method
        def forward(self, scale: Tensor, zero_point: Tensor, bit_width: Tensor, x: Tensor) -> Tensor:
            y_int = self.to_int(scale, zero_point, bit_width, x)
            y = y_int - zero_point
            y = y * scale
            y = self.delay_wrapper(x, y)
            return y

    class DecoupledIntQuant(brevitas.jit.ScriptModule):
        """
        ScriptModule that implements scale, shifted, uniform integer quantization of an input tensor,
        according to an input pre-scale, scale, pre-zero-point, zero-point and bit-width.

        Args:
            narrow_range (bool): Flag that determines whether restrict quantization to a narrow range or not.
            signed (bool): Flag that determines whether to quantize to a signed range or not.
            float_to_int_impl (Module): Module that performs the conversion from floating point to
                integer representation. Default: RoundSte()
            tensor_clamp_impl (Module): Module that performs clamping. Default: TensorClamp()
            quant_delay_steps (int): Number of training steps to delay quantization for. Default: 0

        Returns:
            Tensor: Quantized output in de-quantized format.

        Examples:
            >>> from brevitas.core.scaling import ConstScaling
            >>> int_quant = DecoupledIntQuant(narrow_range=True, signed=True)
            >>> scale, zero_point, bit_width = torch.tensor(0.01), torch.tensor(0.), torch.tensor(4.)
            >>> pre_scale, pre_zero_point = torch.tensor(0.02), torch.tensor(0.)
            >>> inp = torch.Tensor([0.042, -0.053, 0.31, -0.44])
            >>> out = int_quant(pre_scale, pre_zero_point, scale, zero_point, bit_width, inp)
            >>> out
            tensor([ 0.0200, -0.0300,  0.0700, -0.0700])

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

        __constants__ = ['signed', 'narrow_range']

        def __init__(
                self,
                narrow_range: bool,
                signed: bool,
                input_view_impl: Module,
                float_to_int_impl: Module = RoundSte(),
                tensor_clamp_impl: Module = TensorClamp(),
                quant_delay_steps: int = 0):
            super(DecoupledIntQuant, self).__init__()
            self.float_to_int_impl = float_to_int_impl
            self.tensor_clamp_impl = tensor_clamp_impl
            self.signed = signed
            self.narrow_range = narrow_range
            self.delay_wrapper = DelayWrapper(quant_delay_steps)
            self.input_view_impl = input_view_impl

        @brevitas.jit.script_method
        def to_int(
                self, pre_scale: Tensor, pre_zero_point: Tensor, bit_width: Tensor,
                x: Tensor) -> Tensor:
            x = self.input_view_impl(x)
            y = x / pre_scale
            y = y + pre_zero_point
            min_int_val = self.min_int(bit_width)
            max_int_val = self.max_int(bit_width)
            y = self.float_to_int_impl(y)
            y = self.tensor_clamp_impl(y, min_val=min_int_val, max_val=max_int_val)
            return y

        @brevitas.jit.script_method
        def min_int(self, bit_width):
            return min_int(self.signed, self.narrow_range, bit_width)

        @brevitas.jit.script_method
        def max_int(self, bit_width):
            return max_int(self.signed, self.narrow_range, bit_width)

        @brevitas.jit.script_method
        def forward(
                self,
                pre_scale: Tensor,
                pre_zero_point: Tensor,
                scale: Tensor,
                zero_point: Tensor,
                bit_width: Tensor,
                x: Tensor) -> Tensor:
            y_int = self.to_int(pre_scale, pre_zero_point, bit_width, x)
            y = y_int - zero_point
            y = y * scale
            y = self.delay_wrapper(x, y)
            return y