# MIT License
#
# Copyright (c) 2022 Ahmet Oner & Besim Alibegovic
# Portions Copyright (c) 2024 Team Audio
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import os
from io import StringIO
from threading import Lock
from typing import BinaryIO, Union

import torch
import whisper
from whisper.utils import ResultWriter, WriteTXT, WriteSRT, WriteVTT, WriteTSV, WriteJSON

ASR_ENGINE_OPTIONS = frozenset([
    "task",
    "language",
    "initial_prompt",
    "word_timestamps",
])

model_name = os.getenv("ASR_MODEL", "small")
model_path = os.getenv("ASR_MODEL_PATH", os.path.join(os.path.expanduser("~"), ".cache", "whisper"))

model_lock = Lock()

model = None
def load_model(next_model_name: str):
    with model_lock:
        global model_name, model

        if model and next_model_name == model_name:
            return model

        if torch.cuda.is_available():
            model = whisper.load_model(next_model_name, download_root=model_path).cuda()
        else:
            model = whisper.load_model(next_model_name, download_root=model_path)

        model_name = next_model_name

        return model


def transcribe(audio, asr_options, output):
    options_dict = {k: v for k, v in asr_options.items() if k in ASR_ENGINE_OPTIONS}

    with model_lock:
        result = model.transcribe(audio, **options_dict)

    output_file = StringIO()
    write_result(result, output_file, output)
    output_file.seek(0)

    return output_file


def language_detection(audio):
    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # detect the spoken language
    with model_lock:
        _, probs = model.detect_language(mel)
    detected_lang_code = max(probs, key=probs.get)

    return detected_lang_code


def write_result(
        result: dict, file: BinaryIO, output: Union[str, None]
):
    options = {
        'max_line_width': 1000,
        'max_line_count': 10,
        'highlight_words': False
    }
    if output == "srt":
        WriteSRT(ResultWriter).write_result(result, file=file, options=options)
    elif output == "vtt":
        WriteVTT(ResultWriter).write_result(result, file=file, options=options)
    elif output == "tsv":
        WriteTSV(ResultWriter).write_result(result, file=file, options=options)
    elif output == "json":
        WriteJSON(ResultWriter).write_result(result, file=file, options=options)
    elif output == "txt":
        WriteTXT(ResultWriter).write_result(result, file=file, options=options)
    else:
        return 'Please select an output method!'