# /// script
# requires-python = "==3.12"
# dependencies = [
#     "huggingface-hub>= 0.34.4"
#     "datasets>=3.5.0",
# ]
# ///

import regex as re

import pandas as pd

from datasets import load_dataset, concatenate_datasets
from huggingface_hub import HfFileSystem


from u_func import *

#consider appendix
appendix = False
#which
a="_4"
#set years
start_year = 1930
#last year to process
stop_year = 1930
year_list = sorted(make_year_list(start_year,stop_year))
if appendix:
        year_list = [i+a for i in year_list]

def get_files_year(year:str)->list[str]:
    #get filenames in a year
    fs = HfFileSystem()
    # List files
    try:
        f_dict = fs.ls(f"datasets/chcaa/kb-books/data/{year}")
        f_names = [i.get("name") for i in f_dict]
        f_names = [re.sub("datasets/chcaa/kb-books/","",i) for i in f_names]
    except FileNotFoundError:
        f_names = "Not Found"
    return f_names


def main():
    #load in wordset to check against
    danish_words=list(pd.read_csv("./ods_fullforms_2020-08-26.csv", sep  = "\t")["A"])
    danish_words = [i.lower() for i in danish_words]
    danish_words = list(set(danish_words))
    
    

    for year in year_list:
        print(f"STARTING YEAR {year}...")
        #load in data
        #year_path = os.path.join("..","data",year)
        #year_data = load_from_disk(year_path)
        year_files = get_files_year(year)

        if year_files == "Not Found":
            print(f"{year} does not exist")
            continue

        year_data = load_dataset("chcaa/kb-books", split="train",
                    data_files=year_files)
       
        #count tokens
        #drop image
        year_data = year_data.remove_columns("page_image")

        #shuffling and sharding is to try and increase efficiency of the processes
        #if a single process gets stuck with an unlucky draw of pages the available cpu
        #power is not fully utilized

        #shuffle dataset
        year_data = year_data.shuffle(seed=1956)
        #flatten indices
        year_data = year_data.flatten_indices()
        #shard the year
        year_data = shard_ds(year_data)
        #print(year_data)
        yd_list = []
        for i in year_data:
            i = i.map(
                                  count_tokens_and_words,
                                  fn_kwargs = {"colname":"page_text",
                                               "wordlist":danish_words},
                                  num_proc = 16)
            yd_list.append(i)
        #print(yd_list)
        #reassemble
        year_data = concatenate_datasets(yd_list)

        cols = year_data.column_names
        wanted_cols = ["page_id","title","published",
                       "page_text_n_tokens",
                       "page_text_rel_a_words",
                       "page_text_rel_words",
                       "page_text_punkt_tokens",
                       "page_text_a_tokens",
                       "file_name"]
        unwanted_cols = [i for i in cols if i not in wanted_cols]
        #remove unnecessary columns
        year_data = year_data.remove_columns(column_names=unwanted_cols)  
        #save by year
        year_data.to_parquet(f"./metadata/{year}.parquet")


if __name__ == "__main__":
    main()