#!/usr/bin/env python3
"""
 Copyright (C) 2021-2022 Intel Corporation

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

from argparse import ArgumentParser, SUPPRESS
from itertools import groupby
import json
import logging as log
from pathlib import Path
from time import perf_counter
import sys

import numpy as np
import wave

from openvino.inference_engine import IECore

ie = IECore()



class Wav2Vec:
    alphabet = [
        "<pad>", "<s>", "</s>", "<unk>", "|",
        "e", "t", "a", "o", "n", "i", "h", "s", "r", "d", "l", "u",
        "m", "w", "c", "f", "g", "y", "p", "b", "v", "k", "'", "x", "j", "q", "z"]
    words_delimiter = '|'
    pad_token = '<pad>'
    def __init__(self):
        self.nnet = ie.read_network("/home/intel/Documents/ASR/wav2vec2-base-ft-keyword-spotting-int8/ov_model.xml", "/home/intel/Documents/ASR/wav2vec2-base-ft-keyword-spotting-int8/ov_model.bin")

    @staticmethod
    def preprocess(sound):
        return (sound - np.mean(sound)) / (np.std(sound) + 1e-15)

    def infer(self, audio):
        exec_net = ie.load_network(self.nnet, "CPU")
        outss = exec_net.infer({"input_values": audio})
        # input_data = {next(iter(self.nnet.input_info)): audio}
        return outss

    def decode(self, logits):
        token_ids = np.squeeze(np.argmax(logits, -1))
        tokens = [self.decoding_vocab[idx] for idx in token_ids]
        tokens = [token_group[0] for token_group in groupby(tokens)]
        tokens = [t for t in tokens if t != self.pad_token]
        res_string = ''.join([t if t != self.words_delimiter else ' ' for t in tokens]).strip()
        res_string = ' '.join(res_string.split(' '))
        res_string = res_string.lower()
        return res_string

    def reshape(self, audio):
        self.nnet.reshape({next(iter(self.nnet.input_info)): audio.shape})

def main():
    model = Wav2Vec()
    start_time = perf_counter()
    with wave.open("/home/intel/Documents/ASR/applications.ai.conversational-ai.asr-grpc-security/client_sample_examples/python/audio_data_samples/how_are_you_doing.wav", 'rb') as wave_read:
        channel_num, sample_width, sampling_rate, pcm_length, compression_type, _ = wave_read.getparams()
        assert sample_width == 2, "Only 16-bit WAV PCM supported"
        assert compression_type == 'NONE', "Only linear PCM WAV files supported"
        assert channel_num == 1, "Only mono WAV PCM supported"
        assert sampling_rate == 16000, "Only 16 KHz audio supported"
        audio = np.frombuffer(wave_read.readframes(pcm_length * channel_num), dtype=np.int16).reshape((1, pcm_length))
        audio =  audio.astype(float) / np.iinfo(np.int16).max

    normalized_audio = model.preprocess(audio)
    model.reshape(normalized_audio)
    character_probs = model.infer(normalized_audio)
    print(type(character_probs))
    print(character_probs.keys())
    transcription = model.decode(character_probs["3761"])
    total_latency = (perf_counter() - start_time) * 1e3
    # log.info("Metrics report:")
    # log.info("\tLatency: {:.1f} ms".format(total_latency))
    print(transcription)
    print(total_latency)

if __name__ == '__main__':
    sys.exit(main() or 0)