from typing import List, Tuple, Set, Dict

from huggingface_hub import hf_hub_download
import re
from PIL import Image
from transformers import NougatProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
from doctrfiles import DetectionResult
# Numpy image type
import numpy.typing as npt
from numpy import uint8
ImageType = npt.NDArray[uint8]


def run_nougat(inputs: List[Tuple[int, ImageType]])-> List[DetectionResult]:
    processor = NougatProcessor.from_pretrained("facebook/nougat-base")
    model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)

    detection_results =[]
    for index, np_img in inputs: 
        image = Image.fromarray(np_img)
        pixel_values = processor(image, return_tensors="pt").pixel_values

        # generate transcription (here we only generate 30 tokens)
        outputs = model.generate(
            pixel_values.to(device),
            min_length=1,
            max_new_tokens=30,
            bad_words_ids=[[processor.tokenizer.unk_token_id]],
        )

        sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
        sequence = processor.post_process_generation(sequence, fix_markdown=False)
        # note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
        text = sequence 
        detection_results.append(DetectionResult(score=1, text=text, index=index))
    
    return detection_results