from typing import Optional

import torch
from torch import Tensor, tensor

from torchsynth.config import SynthConfig
from torchsynth.module import (
    VCO, LFO, ModulationMixer,
)
from torchsynth.signal import Signal
from torchsynth.synth import AbstractSynth


class SinLFO(LFO):
    """A LFO that generates the sine waveform.
    (The implementation of this class is a modification of the code in TorchSynth) """

    def output(self, mod_signal: Optional[Signal] = None) -> Signal:
        # This module accepts signals at control rate!
        if mod_signal is not None:
            assert mod_signal.shape == (self.batch_size, self.control_buffer_size)

        frequency = self.make_control(mod_signal)
        argument = torch.cumsum(2 * torch.pi * frequency / self.control_rate, dim=1)
        argument = argument + self.p("initial_phase").unsqueeze(1)

        shapes = torch.stack(self.make_lfo_shapes(argument), dim=1).as_subclass(Signal)

        mode = torch.stack([self.p(lfo) for lfo in self.lfo_types], dim=1)
        mode[0] = tensor([1.0, 0., 0., 0., 0.])
        mode = torch.pow(mode, self.exponent)
        mode = mode / torch.sum(mode, dim=1, keepdim=True)
        return torch.matmul(mode.unsqueeze(1), shapes).squeeze(1).as_subclass(Signal)


class TriLFO(LFO):
    """A LFO that generates the triangle waveform.
    (The implementation of this class is a modification of the code in TorchSynth) """

    def output(self, mod_signal: Optional[Signal] = None) -> Signal:
        # This module accepts signals at control rate!
        if mod_signal is not None:
            assert mod_signal.shape == (self.batch_size, self.control_buffer_size)

        frequency = self.make_control(mod_signal)
        argument = torch.cumsum(2 * torch.pi * frequency / self.control_rate, dim=1)
        argument = argument + self.p("initial_phase").unsqueeze(1)

        shapes = torch.stack(self.make_lfo_shapes(argument), dim=1).as_subclass(Signal)

        mode = torch.stack([self.p(lfo) for lfo in self.lfo_types], dim=1)
        mode[0] = tensor([0.5, 0.5, 0., 0., 0.])
        mode = torch.pow(mode, self.exponent)
        mode = mode / torch.sum(mode, dim=1, keepdim=True)
        return torch.matmul(mode.unsqueeze(1), shapes).squeeze(1).as_subclass(Signal)


class SawLFO(LFO):
    """A LFO that generates the sawtooth waveform.
    (The implementation of this class is a modification of the code in TorchSynth) """

    def output(self, mod_signal: Optional[Signal] = None) -> Signal:
        # This module accepts signals at control rate!
        if mod_signal is not None:
            assert mod_signal.shape == (self.batch_size, self.control_buffer_size)

        frequency = self.make_control(mod_signal)
        argument = torch.cumsum(2 * torch.pi * frequency / self.control_rate, dim=1)
        argument = argument + self.p("initial_phase").unsqueeze(1)

        shapes = torch.stack(self.make_lfo_shapes(argument), dim=1).as_subclass(Signal)

        mode = torch.stack([self.p(lfo) for lfo in self.lfo_types], dim=1)
        mode[0] = tensor([0.5, 0., 0.5, 0., 0.])
        mode = torch.pow(mode, self.exponent)
        mode = mode / torch.sum(mode, dim=1, keepdim=True)
        return torch.matmul(mode.unsqueeze(1), shapes).squeeze(1).as_subclass(Signal)


class RSawLFO(LFO):
    """A LFO that generates the sawtooth waveform.
    (The implementation of this class is a modification of the code in TorchSynth) """

    def output(self, mod_signal: Optional[Signal] = None) -> Signal:
        # This module accepts signals at control rate!
        if mod_signal is not None:
            assert mod_signal.shape == (self.batch_size, self.control_buffer_size)

        frequency = self.make_control(mod_signal)
        argument = torch.cumsum(2 * torch.pi * frequency / self.control_rate, dim=1)
        argument = argument + self.p("initial_phase").unsqueeze(1)

        shapes = torch.stack(self.make_lfo_shapes(argument), dim=1).as_subclass(Signal)

        mode = torch.stack([self.p(lfo) for lfo in self.lfo_types], dim=1)
        mode[0] = tensor([0.5, 0., 0.0, 0.5, 0.])
        mode = torch.pow(mode, self.exponent)
        mode = mode / torch.sum(mode, dim=1, keepdim=True)
        return torch.matmul(mode.unsqueeze(1), shapes).squeeze(1).as_subclass(Signal)


class SquareLFO(LFO):
    """A LFO that generates the square waveform.
    (The implementation of this class is a modification of the code in TorchSynth) """

    def output(self, mod_signal: Optional[Signal] = None) -> Signal:
        # This module accepts signals at control rate!
        if mod_signal is not None:
            assert mod_signal.shape == (self.batch_size, self.control_buffer_size)

        frequency = self.make_control(mod_signal)
        argument = torch.cumsum(2 * torch.pi * frequency / self.control_rate, dim=1)
        argument = argument + self.p("initial_phase").unsqueeze(1)

        shapes = torch.stack(self.make_lfo_shapes(argument), dim=1).as_subclass(Signal)

        mode = torch.stack([self.p(lfo) for lfo in self.lfo_types], dim=1)
        mode[0] = tensor([0.5, 0., 0., 0., 0.5])
        mode = torch.pow(mode, self.exponent)
        mode = mode / torch.sum(mode, dim=1, keepdim=True)
        return torch.matmul(mode.unsqueeze(1), shapes).squeeze(1).as_subclass(Signal)