# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
#               2024 Alibaba Inc
#
# 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.

import json
import torchaudio
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s %(levelname)s %(message)s')

def read_trans(list_file):
    trans = {}
    with open(list_file, 'r', encoding='utf8') as fin:
        for line in fin:
            sec = line.strip().split("\t")
            if len(sec) > 1:
                if sec[0] not in trans.keys():
                    trans[sec[0]] = sec[1]
    return trans

def read_scp(list_file):
    scp = {}
    with open(list_file, 'r', encoding='utf8') as fin:
        for line in fin:
            sec = line.strip().split(" ")
            if len(sec) > 1:
                if sec[0] not in scp.keys():
                    scp[sec[0]] = sec[1]
    return scp

def read_lists(list_file):
    lists = []
    with open(list_file, 'r', encoding='utf8') as fin:
        for line in fin:
            lists.append(line.strip())
    return lists


def read_json_lists(list_file):
    lists = read_lists(list_file)
    results = {}
    for fn in lists:
        with open(fn, 'r', encoding='utf8') as fin:
            results.update(json.load(fin))
    return results


def load_wav(wav, target_sr):
    audio, sample_rate = torchaudio.load(wav)
    audio = audio.mean(dim=0, keepdim=True)
    if sample_rate != target_sr:
        assert sample_rate > target_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr)
        audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech)
    return audio


def speed_change(waveform, sample_rate, speed_factor: str):
    effects = [
        ["tempo", speed_factor],  # speed_factor
        ["rate", f"{sample_rate}"]
    ]
    augmented_waveform, new_sample_rate = torchaudio.sox_effects.apply_effects_tensor(
        waveform,
        sample_rate,
        effects
    )
    return augmented_waveform, new_sample_rate