#!/usr/bin/python3
# -*- coding: utf-8 -*-
from typing import Union, Tuple

import numpy as np
import sherpa
import sherpa_onnx
import torch
import torchaudio
import wave


def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
    """
    :param wave_filename: Path to a wave file. It should be single channel and each sample should be 16-bit.
    Its sample rate does not need to be 16kHz.
    :return: Return a tuple containing:
    signal: A 1-D array of dtype np.float32 containing the samples, which are normalized to the range [-1, 1].
    sample_rate: sample rate of the wave file
    """

    with wave.open(wave_filename) as f:
        assert f.getnchannels() == 1, f.getnchannels()
        assert f.getsampwidth() == 2, f.getsampwidth()
        num_samples = f.getnframes()
        samples = f.readframes(num_samples)
        samples_int16 = np.frombuffer(samples, dtype=np.int16)
        samples_float32 = samples_int16.astype(np.float32)

        samples_float32 = samples_float32 / 32768
        return samples_float32, f.getframerate()


def decode_offline_recognizer(recognizer: sherpa.OfflineRecognizer,
                              filename: str,
                              ) -> str:
    s = recognizer.create_stream()

    s.accept_wave_file(filename)
    recognizer.decode_stream(s)

    text = s.result.text.strip()
    print("text: {}".format(text))
    return text.lower()


def decode_online_recognizer(recognizer: sherpa.OnlineRecognizer,
                             filename: str,
                             expected_sample_rate: int = 16000,
                             ) -> str:
    samples, actual_sample_rate = torchaudio.load(filename)
    if expected_sample_rate != actual_sample_rate:
        raise AssertionError(
            "expected sample rate: {}, but: actually: {}".format(expected_sample_rate, actual_sample_rate)
        )

    samples = samples[0].contiguous()

    s = recognizer.create_stream()

    tail_padding = torch.zeros(int(expected_sample_rate * 0.3), dtype=torch.float32)
    s.accept_waveform(expected_sample_rate, samples)
    s.accept_waveform(expected_sample_rate, tail_padding)
    s.input_finished()

    while recognizer.is_ready(s):
        recognizer.decode_stream(s)

    text = recognizer.get_result(s).text
    return text.strip().lower()


def decode_offline_recognizer_sherpa_onnx(recognizer: sherpa_onnx.OfflineRecognizer,
                                          filename: str,
                                          ) -> str:
    s = recognizer.create_stream()
    samples, sample_rate = read_wave(filename)
    s.accept_waveform(sample_rate, samples)
    recognizer.decode_stream(s)

    return s.result.text.lower()


def decode_online_recognizer_sherpa_onnx(recognizer: sherpa_onnx.OnlineRecognizer,
                                         filename: str,
                                         ) -> str:
    s = recognizer.create_stream()
    samples, sample_rate = read_wave(filename)
    s.accept_waveform(sample_rate, samples)

    tail_paddings = np.zeros(int(0.3 * sample_rate), dtype=np.float32)
    s.accept_waveform(sample_rate, tail_paddings)
    s.input_finished()

    while recognizer.is_ready(s):
        recognizer.decode_stream(s)

    return recognizer.get_result(s).lower()


def decode_by_recognizer(
    recognizer: Union[
        sherpa.OfflineRecognizer,
        sherpa.OnlineRecognizer,
        sherpa_onnx.OfflineRecognizer,
        sherpa_onnx.OnlineRecognizer,
    ],
    filename: str,
) -> str:
    if isinstance(recognizer, sherpa.OfflineRecognizer):
        return decode_offline_recognizer(recognizer, filename)
    elif isinstance(recognizer, sherpa.OnlineRecognizer):
        return decode_online_recognizer(recognizer, filename)
    elif isinstance(recognizer, sherpa_onnx.OfflineRecognizer):
        return decode_offline_recognizer_sherpa_onnx(recognizer, filename)
    elif isinstance(recognizer, sherpa_onnx.OnlineRecognizer):
        return decode_online_recognizer_sherpa_onnx(recognizer, filename)
    else:
        raise ValueError(f"Unknown recognizer type {type(recognizer)}")


if __name__ == "__main__":
    pass