from typing import  List, Tuple

import streamlit as st
import torch
from optimum.bettertransformer import BetterTransformer
from torch import nn, qint8
from torch.quantization import quantize_dynamic
from transformers import T5ForConditionalGeneration, T5Tokenizer


@st.cache_resource(show_spinner=False)
def get_resources(quantize: bool = True, no_cuda: bool = False) -> Tuple[T5ForConditionalGeneration, T5Tokenizer]:
    """Load a T5 model and its (slow) tokenizer"""
    tokenizer = T5Tokenizer.from_pretrained("BramVanroy/ul2-base-dutch-simplification-mai-2023", use_fast=False)
    model = T5ForConditionalGeneration.from_pretrained("BramVanroy/ul2-base-dutch-simplification-mai-2023")

    model = BetterTransformer.transform(model, keep_original_model=False)
    model.resize_token_embeddings(len(tokenizer))

    if torch.cuda.is_available() and not no_cuda:
        model = model.to("cuda")
    elif quantize:  # Quantization not supported on CUDA
        model = quantize_dynamic(model, {nn.Linear, nn.Dropout, nn.LayerNorm}, dtype=qint8)

    model.eval()

    return model, tokenizer


def batchify(iterable, batch_size=16):
    """Turn an iterable in a batch generator
    :param iterable: iterable to batchify
    :param batch_size: batch size
    """
    num_items = len(iterable)
    for idx in range(0, num_items, batch_size):
        yield iterable[idx : min(idx + batch_size, num_items)]


def simplify(
    texts: List[str], model: T5ForConditionalGeneration, tokenizer: T5Tokenizer, batch_size: int = 16
) -> List[str]:
    """Simplify a given set of texts with a given model and tokenizer. Yields results in batches of 'batch_size'
    :param texts: texts to simplify
    :param model: model to use for simplification
    :param tokenizer: tokenizer to use for simplification
    :param batch_size: batch size to yield results in
    """
    for batch_texts in batchify(texts, batch_size=batch_size):
        nlg_batch_texts = ["[NLG] " + text for text in batch_texts]
        encoded = tokenizer(nlg_batch_texts, return_tensors="pt", padding=True)
        encoded = {k: v.to(model.device) for k, v in encoded.items()}
        gen_kwargs = {
            "max_new_tokens": 128,
            "num_beams": 3,
        }

        with torch.no_grad():
            encoded = {k: v.to(model.device) for k, v in encoded.items()}
            generated = model.generate(**encoded, **gen_kwargs).cpu()

        yield batch_texts, tokenizer.batch_decode(generated, skip_special_tokens=True)