import re
import re
from sentence_transformers import SentenceTransformer, util
import re
from unidecode import unidecode
from transformers import AutoTokenizer
import yaml
import fitz


def remove_accents(input_str):
    text_no_accents = unidecode(input_str)
    return text_no_accents


def remove_special_characters(text):
    text = re.sub(r'https?://\S+|www\.\S+', '', text)
    emoji_pattern = re.compile("["  
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F700-\U0001F77F"  # alchemical symbols
        u"\U0001F780-\U0001F7FF"  # Geometric Shapes Extended
        u"\U0001F800-\U0001F8FF"  # Supplemental Arrows-C
        u"\U0001F900-\U0001F9FF"  # Supplemental Symbols and Pictographs
        u"\U0001FA00-\U0001FA6F"  # Chess Symbols
        u"\U0001FA70-\U0001FAFF"  # Symbols and Pictographs Extended-A
        u"\U00002702-\U000027B0"  # Dingbats
        u"\U000024C2-\U0001F251" 
        "]+", flags=re.UNICODE)
    text = emoji_pattern.sub('', text) 
    text = re.sub(r'#\w+', '', text)  
    text = re.sub(r'[^\w\s\d.,!?\'"()-;]', '', text) 
    text = re.sub(r'\s+([.,!?;])', r'\1', text)
    text = re.sub(r'([.,!?;])(\S)', r'\1 \2', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def remove_special_characters_2(text):
    pattern = r"[^a-zA-Z0-9 ]+"
    text = re.sub(pattern, "", text)
    return text


def update_character_count(text):
    return f"{len(text)} characters"


with open("config.yaml", "r") as file:
    params = yaml.safe_load(file)

text_bc_model_path = params["TEXT_BC_MODEL_PATH"]

text_bc_tokenizer = AutoTokenizer.from_pretrained(text_bc_model_path)


def len_validator(text):
    min_tokens = 200
    lengt = len(text_bc_tokenizer.tokenize(text=text, return_tensors="pt"))
    if lengt < min_tokens:
        return f"Warning! Input length is {lengt}. Please input a text that is greater than {min_tokens} tokens long. Recommended length {min_tokens*2} tokens."
    else:
        return f"Input length ({lengt}) is satisified."


def extract_text_from_pdf(pdf_path):
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text()
    return text


WORD = re.compile(r"\w+")
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")