import gradio as gr
import logging
import sys
import tempfile
import numpy as np
import datetime

from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
from typing import Optional
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer

logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)


LARGE_MODEL_BY_LANGUAGE = {
    "Arabic": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic", "has_lm": False},
    "Chinese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn", "has_lm": False},
    #"Dutch": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch", "has_lm": False},
    "English": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-english", "has_lm": True},
    "Finnish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish", "has_lm": False},
    "French": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-french", "has_lm": True},
    "German": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-german", "has_lm": True},
    "Greek": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-greek", "has_lm": False},
    "Hungarian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian", "has_lm": False},
    "Italian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-italian", "has_lm": True},
    "Japanese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "has_lm": False},
    "Persian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-persian", "has_lm": False},
    "Polish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-polish", "has_lm": True},
    "Portuguese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese", "has_lm": True},
    "Russian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-russian", "has_lm": True},
    "Spanish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-spanish", "has_lm": True},
}

XLARGE_MODEL_BY_LANGUAGE = {
    "English": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-english", "has_lm": True},
    "Spanish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-spanish", "has_lm": True},
    "German": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-german", "has_lm": True},
    "Russian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-russian", "has_lm": True},
    "French": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-french", "has_lm": True},
    "Italian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-italian", "has_lm": True},
    #"Dutch": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-dutch", "has_lm": False},
    "Polish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-polish", "has_lm": True},
    "Portuguese": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-portuguese", "has_lm": True},
}


# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())

# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
LANGUAGES = sorted(XLARGE_MODEL_BY_LANGUAGE.keys())
CACHED_MODELS_BY_ID = {}


def run(input_file, language, decoding_type, history, model_size="300M"):

    logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")

    history = history or []

    if model_size == "300M":
        model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
    else:
        model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)

    if model is None:
        history.append({
            "error_message": f"Model size {model_size} not found for {language} language :("
        })
    elif decoding_type == "LM" and not model["has_lm"]:
        history.append({
            "error_message": f"LM not available for {language} language :("
        })
    else:

        # model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
        model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
        if model_instance is None:
            model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
            CACHED_MODELS_BY_ID[model["model_id"]] = model_instance

        if decoding_type == "LM":
            processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                           feature_extractor=processor.feature_extractor, decoder=processor.decoder)
        else:
            processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                           feature_extractor=processor.feature_extractor, decoder=None)

        transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]

        logger.info(f"Transcription for {input_file}: {transcription}")

        history.append({
            "model_id": model["model_id"],
            "language": language,
            "model_size": model_size,
            "decoding_type": decoding_type,
            "transcription": transcription,
            "error_message": None
        })

    html_output = "<div class='result'>"
    for item in history:
        if item["error_message"] is not None:
            html_output += f"<div class='result_item result_item_error'>{item['error_message']}</div>"
        else:
            url_suffix = " + LM" if item["decoding_type"] == "LM" else ""
            html_output += "<div class='result_item result_item_success'>"
            html_output += f'<strong><a target="_blank" href="https://huggingface.co/{item["model_id"]}">{item["model_id"]}{url_suffix}</a></strong><br/><br/>'
            html_output += f'{item["transcription"]}<br/>'
            html_output += "</div>"
    html_output += "</div>"

    return html_output, history


gr.Interface(
    run,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
        gr.inputs.Radio(label="Language", choices=LANGUAGES),
        gr.inputs.Radio(label="Decoding type", choices=["greedy", "LM"]),
        # gr.inputs.Radio(label="Model size", choices=["300M", "1B"]),
        "state"
    ],
    outputs=[
        gr.outputs.HTML(label="Outputs"),
        "state"
    ],
    title="🗣️NLP ASR Wav2Vec2 GR📄",
    description="",
    css="""
    .result {display:flex;flex-direction:column}
    .result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
    .result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
    .result_item_error {background-color:#ff7070;color:white;align-self:start}
    """,
    allow_screenshot=False,
    allow_flagging="never",
    theme="grass"
).launch(enable_queue=True)