"""
Shared utility methods for this module.
"""

from ctypes import Array
import datetime
import re

from transformers import AutoModelForSequenceClassification, AutoTokenizer, MBartForConditionalGeneration, MBartTokenizer, pipeline
from transformers import PegasusTokenizer, PegasusForConditionalGeneration



def lowercase_string(string: str) -> str:
    """Returns a lowercased string
    Args:
        string: String to lowercase
    Returns:
        String in lowercase
    """
    if isinstance(string, str):
        return string.lower()
    return None


from functools import lru_cache

@lru_cache
def get_sentiment_pipeline():
    model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
    return sentiment_pipeline

def score_sentiment(input: str):
    """Score sentiment of an input string with a pretrained Transformers Pipeline

    Args:
        input (str): Text to be scored

    Returns:
        tuple: (label, score)
    """
    sentiment_pipeline = get_sentiment_pipeline()
    result = sentiment_pipeline(input.lower())[0]
    # print("label:{0} input:{1}".format(result['label'], input))
    return result['label'], result['score']


@lru_cache
def get_summarization_pipeline_nl():

    undisputed_best_model = MBartForConditionalGeneration.from_pretrained(
        "ml6team/mbart-large-cc25-cnn-dailymail-nl"
    )
    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25")
    summarization_pipeline = pipeline(
        task="summarization",
        model=undisputed_best_model,
        tokenizer=tokenizer,
    )
    summarization_pipeline.model.config.decoder_start_token_id = tokenizer.lang_code_to_id[
        "nl_XX"
    ]
    return summarization_pipeline

def summarize_nl(input: str) -> str:
    summarization_pipeline = get_summarization_pipeline_nl()
    summary = summarization_pipeline(
        input,
        do_sample=True,
        top_p=0.75,
        top_k=50,
        # num_beams=4,
        min_length=50,
        early_stopping=True,
        truncation=True,
    )[0]["summary_text"]
    return summary



@lru_cache
def get_pegasus():
    model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
    tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
    return model, tokenizer

def summarize_en(input: str) -> str:

    model, tokenizer = get_pegasus()
    inputs = tokenizer(input, max_length=1024, return_tensors="pt")

    # Generate Summary
    summary_ids = model.generate(inputs["input_ids"])
    result = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 
    return result