# -*- coding: utf-8 -*-

"""
@Author     : Rong Ye
@Time       : May 2022
@Contact    : yerong@bytedance
@Description:
"""

import os
import shutil
import yaml
import torchaudio
import gradio as gr
from huggingface_hub import snapshot_download


LANGUAGE_CODES = {
    "German": "de",
    "Spanish": "es",
    "French": "fr",
    "Italian": "it",
    "Netherlands": "nl",
    "Portuguese": "pt",
    "Romanian": "ro",
    "Russian": "ru",
}

LANG_GEN_SETUPS = {
    "de": {"beam": 10, "lenpen": 0.7},
    "es": {"beam": 10, "lenpen": 0.7},
    "fr": {"beam": 10, "lenpen": 0.7},
    "it": {"beam": 10, "lenpen": 0.7},
    "nl": {"beam": 10, "lenpen": 0.7},
    "pt": {"beam": 10, "lenpen": 0.7},
    "ro": {"beam": 10, "lenpen": 0.7},
    "ru": {"beam": 10, "lenpen": 0.1},
}

os.system("git clone https://github.com/ReneeYe/ConST")
os.system('mv ConST/* ./')
os.system("pip3 install -r requirements.txt")
os.system("python3 setup.py install")
os.system("python3 ConST/setup.py build_ext --inplace")
os.system("mkdir -p data checkpoint")


huggingface_model_dir = snapshot_download(repo_id="ReneeYe/ConST_en2x_models")
print(huggingface_model_dir)

def convert_audio_to_16k_wav(audio_input):
    num_frames = torchaudio.info(audio_input.name).num_frames
    filename = audio_input.name.split("/")[-1]
    shutil.copy(audio_input.name, f'data/{filename}')
    return f'data/{filename}', num_frames


def prepare_tsv(file_name, n_frame, language, task="ST"):
    tgt_lang = LANGUAGE_CODES[language]
    with open("data/test_case.tsv", "w") as f:
        f.write("id\taudio\tn_frames\ttgt_text\tspeaker\tsrc_lang\ttgt_lang\tsrc_text\n")
        f.write(f"sample\t{file_name}\t{n_frame}\tThis is in {tgt_lang}.\tspk.1\ten\t{tgt_lang}\tThis is English.\n")


def get_vocab_and_yaml(language):
    tgt_lang = LANGUAGE_CODES[language]
    # get: spm_ende.model and spm_ende.txt, and save to data/xxx
    # if exist, no need to download
    shutil.copy(os.path.join(huggingface_model_dir, f"vocabulary/spm_en{tgt_lang}.model"), "./data")
    shutil.copy(os.path.join(huggingface_model_dir, f"vocabulary/spm_en{tgt_lang}.txt"), "./data")

    # write yaml file
    abs_path = os.popen("pwd").read().strip()
    yaml_dict = LANG_GEN_SETUPS["tgt_lang"]
    yaml_dict["input_channels"] = 1
    yaml_dict["use_audio_input"] = True
    yaml_dict["prepend_tgt_lang_tag"] = True
    yaml_dict["prepend_src_lang_tag"] = True
    yaml_dict["audio_root"] = os.path.join(abs_path, "data")
    yaml_dict["vocab_filename"] = f"spm_en{tgt_lang}.txt"
    yaml_dict["bpe_tokenizer"] = {"bpe": "sentencepiece",
                                  "sentencepiece_model": os.path.join(abs_path, f"data/spm_en{tgt_lang}.model")}
    with open("data/config.yaml", "w") as f:
        yaml.dump(yaml_dict, f)


def get_model(language):
    # download models to checkpoint/xxx
    return os.path.join(huggingface_model_dir, f"models/const_en{LANGUAGE_CODES[language]}.pt")


def generate(model_path):
    os.system(f"fairseq-generate data/ --gen-subset test_case --task speech_to_text --prefix-size 1 \
                --max-tokens 4000000 --max-source-positions 4000000 \
                --config-yaml config.yaml  --path {model_path} | tee temp.txt")
    output = os.popen("grep ^D temp.txt | sort -n -k 2 -t '-' | cut -f 3")
    return output.read().strip()


def remove_temp_files():
    os.remove("temp.txt")
    os.remove("data/test_case.tsv")


def run(audio_file, language):
    converted_audio_file, n_frame = convert_audio_to_16k_wav(audio_file)
    prepare_tsv(converted_audio_file, n_frame, language)
    get_vocab_and_yaml(language)
    model_path = get_model(language)
    generated_output = generate(model_path)
    remove_temp_files()
    return generated_output


def greet(audio_file, language):
    print(audio_file.name)
    return f"Hello {language}!!"


inputs = [
        gr.inputs.Audio(source="microphone", type="file", label="Record something (in English)..."),
        gr.inputs.Dropdown(list(LANGUAGE_CODES.keys()), default="German", label="From English to Languages X..."),
    ]

iface = gr.Interface(
    fn=run,
    inputs=inputs,
    outputs=[gr.outputs.Textbox(label="The translation")],
    examples=[['case1.wav', "German"],['case2.wav', "German"], ['case3.wav', "German"]],
    title="ConST: an end-to-end speech translator",
    description="End-to-end Speech Translation Live Demo for English to eight European languages.",
    article="ConST is an end-to-end speech translation model (see paper <a href='https://arxiv.org/abs/2205.02444', target='_blank'>here</a>). "
            "Its motivation is to use contrastive learning method to learn similar representations for semantically similar speech and text.",
    theme="seafoam",
    layout='vertical',
    # analytics_enabled=False,
    # flagging_dir='results/flagged/',
    # allow_flagging=True,
    # flagging_options=['Interesting!', 'Error: Claim Phrase Parsing', 'Error: Local Premise',
    #                   'Error: Require Commonsense', 'Error: Evidence Retrieval'],
    enable_queue=True
)
iface.launch(inline=False)