from datasets import Dataset, load_dataset, disable_progress_bars, concatenate_datasets

from tqdm import tqdm

from page_mix import *

import re
disable_progress_bars()

def update_processed_docs(ds,document,msg):
    #save current problem, if 
    if ds["problem"] == "no":
        old_p = ""
    else:
        old_p = ds["problem"]

    if ds["filename"] == document:
        ds.update({"problem":old_p + msg})
    else:
        pass
    return ds

def empty_docs(documents):
    ds=[]
    for i in documents:
        ds.append({"filename":i,"above_ref_line":0.0,"processed":False,"problem":"empty"})
    ds = Dataset.from_list(ds)

    return ds


def main():
    quality_list = []

    #assemble dataset from representative pages
    meta_data = load_dataset("./repr_meta",split="train")
    #get reference line to from "good quality" documents
    #by taking the median of its alphabetic character ratio dist
    ref_docs = Dataset.from_parquet("good_qual_10k_counts.parquet")
    reference_line = np.median(ref_docs["text_rel_a_words"])


    #separate years
    year_list = sorted(list(set(meta_data["published"])))

    for year in tqdm(year_list,desc="YEARS "):

        #filter each year
        year_meta_data = meta_data.filter(lambda ds,year:ds["published"] == year, num_proc= 10, fn_kwargs={"year":year})

        document_list = sorted(list(set(year_meta_data["file_name"])))

        for document in tqdm(document_list,
                                leave=False,
                                desc="DOCS "):
            
            #compare page alphabethic ratios to ref line
            doc_meta_data = year_meta_data.filter(lambda ds,document:ds["file_name"] == document, fn_kwargs={"document":document})

            rp = compare_to_ref_line(doc_meta_data,
                                     ref_val=reference_line,
                                     column_to_compare="page_text_rel_a_words")
            #assigning for inspection
            quality_list.append({"filename":document,"above_ref_line":rp,"processed":True,"problem":"no"})

    #create dataset
    quality_ds = Dataset.from_list(quality_list)

    ######
    #Error docs
    with open("./repr_meta.log") as f:
        lines = f.readlines()

    re_pat= r"ERROR:.*pdf"
   
    epe = [i for i in lines if "EmptyDocumentError" in i]
    nme = [i for i in lines if "NoMixError" in i]
    lpe = [i for i in lines if "LowProportionError" in i]
    small_doc = [i for i in lines if "small_doc:True" in i]

    #pair the errors with filenames
    nme_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in nme]

    epe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in epe]

    lpe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in lpe]

    small_doc_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in small_doc]

    #append vals from logs
    for i in tqdm(small_doc_fns,leave=False,desc="small_docs"):
        quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"small_doc;"})

    for i in tqdm(nme_fns,leave=False,desc="no_mix"):
        quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"no_mix;"})

    for i in tqdm(lpe_fns,leave=False,desc="low_prop"):
        quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"low_prop;"})

    empty_document = empty_docs(epe_fns)

    quality_ds = concatenate_datasets([quality_ds,empty_document])
    #print(epe_fns)
    
    #save results
    quality_ds.to_json("../ocr_metadata.json")

            
if __name__ == "__main__":
    main()