"""
alac 77
"""

import glob
import inspect
import math
import os
import re
import sys
import datetime
import time
import warnings
import PyPDF2
import click
import numpy as np
import pandas as pd
import selenium
from tqdm.autonotebook import tqdm
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import Select
from selenium.webdriver.chrome.options import Options

pd.set_option("mode.chained_assignment", None)
pd.set_option("display.notebook_repr_html", True)
pd.set_option('display.expand_frame_repr', False) 
pd.set_option('display.max_rows', 100)
pd.set_option('compute.use_bottleneck', True)
pd.set_option('compute.use_numexpr', True)
pd.set_option('display.max_categories', 16)
pd.set_option('display.precision',2)


tqdm.pandas()
warnings.filterwarnings('ignore')

# WRITE

def write(conf, outputs):
   """
   Writes (outputs) to file at (conf.OUTPUT_PATH)

   Args:
      conf (pd.Series): Configuration object with paths and settings
      outputs (pd.Series|pd.DataFrame): Description
   Returns:
      outputs: DataFrame written to file at conf.OUTPUT_PATH
      DataFrame
   """

   if conf.OUTPUT_EXT == ".xls":
      try:
         with pd.ExcelWriter(conf.OUTPUT_PATH) as writer:
            outputs.to_excel(writer, sheet_name="outputs", engine="openpyxl")
      except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
         try:
            with pd.ExcelWriter(conf.OUTPUT_PATH, engine="xlwt") as writer:
               outputs.to_excel(writer, sheet_name="outputs")
         except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
            outputs.to_json(os.path.splitext(conf.OUTPUT_PATH)[
                            0] + "-cases.json.zip", orient='table')
            click.echo(conf, f"Fallback export to {os.path.splitext(conf.OUTPUT_PATH)[0]}-cases.json.zip due to Excel engine failure, usually caused by exceeding max row limit for .xls/.xlsx files!")
   if conf.OUTPUT_EXT == ".xlsx":
      try:
         with pd.ExcelWriter(conf.OUTPUT_PATH) as writer:
            outputs.to_excel(writer, sheet_name="outputs", engine="openpyxl")
      except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
         try:
            with pd.ExcelWriter(conf.OUTPUT_PATH[0:-1]) as writer:
               outputs.to_excel(writer, sheet_name="outputs", engine="xlsxwriter")
         except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
            outputs.to_json(os.path.splitext(conf.OUTPUT_PATH)[
                            0] + ".json.zip", orient='table', compression="zip")
            click.echo(conf, f"Fallback export to {os.path.splitext(conf.OUTPUT_PATH)}.json.zip due to Excel engine failure, usually caused by exceeding max row limit for .xls/.xlsx files!")
   elif conf.OUTPUT_EXT == ".pkl":
      if conf.COMPRESS:
         outputs.to_pickle(conf.OUTPUT_PATH + ".xz", compression="xz")
      else:
         outputs.to_pickle(conf.OUTPUT_PATH)
   elif conf.OUTPUT_EXT == ".xz":
      outputs.to_pickle(conf.OUTPUT_PATH, compression="xz")
   elif conf.OUTPUT_EXT == ".json":
      if conf.COMPRESS:
         outputs.to_json(conf.OUTPUT_PATH + ".zip",
                         orient='table', compression="zip")
      else:
         outputs.to_json(conf.OUTPUT_PATH, orient='table')
   elif conf.OUTPUT_EXT == ".csv":
      if conf.COMPRESS:
         outputs.to_csv(conf.OUTPUT_PATH + ".zip",
                        escapechar='\\', compression="zip")
      else:
         outputs.to_csv(conf.OUTPUT_PATH, escapechar='\\')
   elif conf.OUTPUT_EXT == ".txt":
      outputs.to_string(conf.OUTPUT_PATH)
   elif conf.OUTPUT_EXT == ".dta":
      outputs.to_stata(conf.OUTPUT_PATH)
   elif conf.OUTPUT_EXT == ".parquet":
      if conf.COMPRESS:
         outputs.to_parquet(conf.OUTPUT_PATH, compression="brotli")
      else:
         outputs.to_parquet(conf.OUTPUT_PATH)
   else:
      pass
   return outputs

def archive(conf):
   """
   Write full text archive to file.pkl.xz

   Args:
      conf (pd.Series): Configuration object with paths and settings

   Returns:
      DataFrame (written to file at conf.OUTPUT_PATH)
   """
   start_time = time.time()

   if conf.LOG or conf['DEBUG']:
      click.echo("Writing full text archive from cases...")

   if not conf.IS_FULL_TEXT:
      allpagestext = pd.Series(conf.QUEUE).progress_map(lambda x: getPDFText(x))
   else:
      allpagestext = pd.Series(conf.QUEUE)

   if (conf.LOG or conf['DEBUG']) and conf.IS_FULL_TEXT == False:
      click.echo("Exporting archive to file at output path...")

   outputs = pd.DataFrame({
      'Path': conf.QUEUE if not conf.IS_FULL_TEXT else np.nan,
      'AllPagesText': allpagestext,
      'Timestamp': start_time,
   })

   outputs.fillna('', inplace=True)
   outputs = outputs.convert_dtypes()

   if conf.DEDUPE:
      old = conf.QUEUE.shape[0]
      outputs = outputs.drop_duplicates()
      dif = outputs.shape[0] - old
      if dif > 0 and conf.LOG:
         click.echo(f"Removed {dif} duplicate cases from queue.")
   if not conf.NO_WRITE and conf.OUTPUT_EXT == ".xz":
      outputs.to_pickle(conf.OUTPUT_PATH, compression="xz")
   if not conf.NO_WRITE and conf.OUTPUT_EXT == ".pkl":
      if conf.COMPRESS:
         outputs.to_pickle(conf.OUTPUT_PATH + ".xz", compression="xz")
      else:
         outputs.to_pickle(conf.OUTPUT_PATH)
   if not conf.NO_WRITE and conf.OUTPUT_EXT == ".csv":
      if conf.COMPRESS:
         outputs.to_csv(conf.OUTPUT_PATH + ".zip",
                        escapechar='\\', compression="zip")
      else:
         outputs.to_csv(conf.OUTPUT_PATH, escapechar='\\')
   if not conf.NO_WRITE and conf.OUTPUT_EXT == ".parquet":
      if conf.COMPRESS:
         outputs.to_parquet(conf.OUTPUT_PATH + ".parquet", compression="brotli")
      else:
         outputs.to_parquet(conf.OUTPUT_PATH + ".parquet", compression="brotli")
   if not conf.NO_WRITE and conf.OUTPUT_EXT == ".json":
      if conf.COMPRESS:
         outputs.to_json(conf.OUTPUT_PATH + ".zip",
                         orient='table', compression="zip")
      else:
         outputs.to_json(conf.OUTPUT_PATH, orient='table')
   complete(conf, outputs)
   return outputs

def table(conf):
   """
   Route config to export function corresponding to conf.TABLE

   Args:
      conf (pd.Series): Configuration object with paths and settings
   Returns:
      DataFrame written to file at conf.OUTPUT_PATH
      DataFrame
   """
   a = []

   if conf.MAKE == "multiexport":
      a = cases(conf)
   elif conf.TABLE == "cases":
      a = cases(conf)
   elif conf.TABLE == "fees":
      a = fees(conf)
   elif conf.TABLE == "charges":
      a = charges(conf)
   elif conf.TABLE == "disposition":
      a = charges(conf)
   elif conf.TABLE == "filing":
      a = charges(conf)
   else:
      a = None
   return a

def fees(conf):
   """
   Return fee sheet with case number as DataFrame from batch


   Args:
      conf (pd.Series): Configuration object with paths and settings

   Returns:
      fees = pd.DataFrame({
         CaseNumber: Full case number with county number,
         Code: 4-digit fee code,
         Payor: 3-4-digit Payor code,
         AmtDue (float): Amount Due,
         AmtPaid (float): Amount Paid,
         Balance (float): Current Balance,
         AmtHold: (float): Amount Hold
      })

   """

   fees = pd.DataFrame()

   if conf.DEDUPE:
      old = conf.QUEUE.shape[0]
      conf.QUEUE = conf.QUEUE.drop_duplicates()
      dif = conf.QUEUE.shape[0] - old
      if dif > 0 and conf.LOG:
         click.secho(f"Removed {dif} duplicate cases from queue.",
                     fg='bright_yellow', bold=True)

   if not conf['NO_BATCH']:
      batches = batcher(conf)
   else:
      batches = [conf.QUEUE]

   for i, c in enumerate(batches):
      if i > 0:
         click.echo(f"Finished batch {i}. Now reading batch {i+1} of {len(batches)}")
      b = pd.DataFrame()

      if conf.IS_FULL_TEXT:
         b['AllPagesText'] = c
      else:
         tqdm.pandas(desc="PDF => Text")
         b['AllPagesText'] = c.progress_map(lambda x: getPDFText(x))
      b['CaseNumber'] = b['AllPagesText'].map(lambda x: getCaseNumber(x))
      tqdm.pandas(desc="Fee Sheets")
      b['FeeOutputs'] = b['AllPagesText'].progress_map(lambda x: getFeeSheet(x))
      feesheet = b['FeeOutputs'].map(lambda x: x[6])
      try:
         feesheet['AmtDue'] = feesheet['AmtDue'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['AmtPaid'] = feesheet['AmtPaid'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['Balance'] = feesheet['Balance'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['AmtHold'] = feesheet['AmtHold'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
      except:
         pass

      feesheet = feesheet.dropna()
      fees = fees.dropna()
      feesheet = feesheet.tolist()  # -> [df, df, df]
      feesheet = pd.concat(feesheet, axis=0, ignore_index=True)
      fees = pd.concat([fees, feesheet], axis=0,ignore_index=True)
      fees.fillna('', inplace=True)

   if not conf.NO_WRITE:
      write(conf, fees)

   complete(conf, fees)
   return fees

def stack(dflist, *old_df): # add list of dfs to old_df
      try:
         dflist = dflist.dropna()
      except:
         pass
      try:
         dflist = dflist.tolist()  # -> [df, df, df]
      except:
         pass
      dfliststack = pd.concat(dflist, axis=0, ignore_index=True)
      if not old_df:
         return dfliststack
      else:
         out = pd.concat([old_df, dfliststack], axis=0,ignore_index=True)
         out = out.dropna()
         out = out.fillna('', inplace=True)
         return out

def charges(conf):
   def cleanCat(x):
      if len(x) > 1:
         if "MISDEMEANOR" in x:
            return "MISDEMEANOR"
         elif "FELONY" in x:
            return "FELONY"
         elif "VIOLATION" in x:
            return "VIOLATION"
         else:
            return x[1]
      elif len(x) == 1:
         return x[0]
      else:
         return pd.NaT
   def segmentCharge(text):
      cite_split = re.split(r'[A-Z0-9]{3}-[A-Z0-9]{3}-[A-Z0-9]{3}\({0,1}[A-Z]{0,1}\){0,1}\.{0,1}\d{0,1}', text)
      if len(cite_split) > 1:
         return [cite_split[0][9:], cite_split[1]]
      else:
         return ['','']

   cf = conf
   cf.LOG = False
   cf.NO_WRITE = True # no write for intermediate map() calls
   df = map(conf, getCaseNumber, getRawCharges)
   df = df.explode('RAWCHARGES') # num :: [ch, ch] -> num :: ch, num :: ch
   df['RAWCHARGES'] = df['RAWCHARGES'].convert_dtypes() # obj -> str
   
   df['Sort'] = df['RAWCHARGES'].str.get(9).astype(str) # charge sorter slices at first char after Code: if digit -> Disposition 

   df = df.dropna() # drop pd.NaT before bool() ambiguity TypeError

   df['Disposition'] = df['Sort'].str.isdigit().astype(bool)
   df['Filing'] = df['Disposition'].map(lambda x: not x).astype(bool)
   df['Felony'] = df['RAWCHARGES'].str.contains("FELONY").astype(bool)
   df['Conviction'] = df['RAWCHARGES'].map(lambda x: "GUILTY PLEA" in x or "CONVICTED" in x).astype(bool)

   df['Num'] = df.RAWCHARGES.str.slice(0,3)
   df['Code'] = df.RAWCHARGES.str.slice(4,9)
   df['CourtActionDate'] = df['RAWCHARGES'].str.findall(r'\d{1,2}/\d\d/\d\d\d\d') # -> [x]
   df['CourtActionDate'] = df['CourtActionDate'].map(lambda x: x[0] if len(x)>0 else '') # [x] -> x
   df['Cite'] = df['RAWCHARGES'].str.findall(r'[A-Z0-9]{3}-[A-Z0-9]{3}-[A-Z0-9]{3}\({0,1}[A-Z]{0,1}\){0,1}\.{0,1}\d{0,1}') # -> [x]
   df['Cite'] = df['Cite'].map(lambda x: x[0] if len(x)>0 else '').astype(str) # [x] -> x

   df = df.dropna()

   df['CourtAction'] = df['RAWCHARGES'].str.findall(r'(BOUND|GUILTY PLEA|WAIVED TO GJ|DISMISSED|TIME LAPSED|NOL PROSS|CONVICTED|INDICTED|DISMISSED|FORFEITURE|TRANSFER|REMANDED|WAIVED|ACQUITTED|WITHDRAWN|PETITION|PRETRIAL|COND\. FORF\.)')
   df['CourtAction'] = df['CourtAction'].map(lambda x: x[0] if len(x)>0 else x)

   # split at cite - different parse based on filing/disposition
   df['SegmentedCharges'] = df.RAWCHARGES.map(lambda x: segmentCharge(x))
   # whatever segment wasn't the description (now same for disposition and filing)
   df['OtherSegment'] = df.index.map(lambda x: (df['SegmentedCharges'].iloc[x])[1] if not df['Disposition'].iloc[x] else (df['SegmentedCharges'].iloc[x])[0]).astype(str).str.replace("\d{1,2}/\d\d/\d\d\d\d","",regex=True).str.strip()

   df['Description'] = df.index.map(lambda x: (df['SegmentedCharges'].iloc[x])[0] if not df['Disposition'].iloc[x] else (df['SegmentedCharges'].iloc[x])[1]).astype(str).str.strip().astype("string")
   df['Category'] = df['OtherSegment'].str.findall(r'(ALCOHOL|BOND|CONSERVATION|DOCKET|DRUG|GOVERNMENT|HEALTH|MUNICIPAL|OTHER|PERSONAL|PROPERTY|SEX|TRAFFIC)')
   df['TypeDescription'] = df['OtherSegment'].str.findall(r'(BOND|FELONY|MISDEMEANOR|OTHER|TRAFFIC|VIOLATION)')

   # VRR

   df['A_S_C_NON_DISQ'] = df['Description'].str.contains(r'(A ATT|ATTEMPT|S SOLICIT|CONSP)')
   df['CERV_MATCH'] = df['Code'].str.contains(r'(OSUA|EGUA|MAN1|MAN2|MANS|ASS1|ASS2|KID1|KID2|HUT1|HUT2|BUR1|BUR2|TOP1|TOP2|TPCS|TPCD|TPC1|TET2|TOD2|ROB1|ROB2|ROB3|FOR1|FOR2|FR2D|MIOB|TRAK|TRAG|VDRU|VDRY|TRAO|TRFT|TRMA|TROP|CHAB|WABC|ACHA|ACAL)')
   df['PARDON_DISQ_MATCH'] = df['Code'].str.contains(r'(RAP1|RAP2|SOD1|SOD2|STSA|SXA1|SXA2|ECHI|SX12|CSSC|FTCS|MURD|MRDI|MURR|FMUR|PMIO|POBM|MIPR|POMA|INCE)')
   df['PERM_DISQ_MATCH'] = df['RAWCHARGES'].str.contains(r'(CM\d\d|CMUR)|(CAPITAL)')
   df['CERV'] = df.index.map(
      lambda x: df['CERV_MATCH'].iloc[x] == True and df['A_S_C_NON_DISQ'].iloc[x] == False and df['Felony'].iloc[
         x] == True).astype(bool)
   df['Pardon'] = df.index.map(
      lambda x: df['PARDON_DISQ_MATCH'].iloc[x] == True and df['A_S_C_NON_DISQ'].iloc[x] == False and df['Felony'].iloc[
         x] == True).astype(bool)
   df['Permanent'] = df.index.map(
      lambda x: df['PERM_DISQ_MATCH'].iloc[x] == True and df['A_S_C_NON_DISQ'].iloc[x] == False and df['Felony'].iloc[
         x] == True).astype(bool)

   # type conversions
   df['Category'] = df['Category'].map(lambda x: cleanCat(x))
   df['TypeDescription'] = df['TypeDescription'].map(lambda x: cleanCat(x))
   df['CaseNumber'] = df['getCaseNumber']

   df = df.drop(columns=['Sort','SegmentedCharges','OtherSegment','RAWCHARGES','A_S_C_NON_DISQ','PARDON_DISQ_MATCH','PERM_DISQ_MATCH','CERV_MATCH','getCaseNumber'])
   df = df.dropna()
   df = df.fillna('')


   if conf.TABLE == "filing":
      is_disp = df['Disposition']
      is_filing = is_disp.map(lambda x: False if x == True else True)
      df = df[is_filing]
      df.drop(columns=['CourtAction', 'CourtActionDate'], inplace=True)

   if conf.TABLE == "disposition":
      is_disp = df.Disposition.map(lambda x: True if x == True else False)
      df = df[is_disp]

   if conf.NO_WRITE == False:
      write(conf, df)

   complete(conf, df)
   return df

def cases(conf):
   """
   Return [cases, fees, charges] tables as List of DataFrames from batch
   See API docs for table-specific output tokens

   Args:
      conf (pd.Series): Configuration object with paths and settings

   Returns:
      list = [cases, fees, charges]:
         out[0] = cases table (see alac.caseinfo().__str__ for outputs)
         out[1] = fees table (see alac.fees().__str__ for outputs)
         out[2] = charges table (see alac.charges().__str__ for outputs)
   """

   arch = pd.DataFrame()
   cases = pd.DataFrame()
   fees = pd.DataFrame()
   chargestabs = pd.DataFrame()

   start_time = time.time()
   temp_no_write_arc = False
   temp_no_write_tab = False

   if conf.DEDUPE:
      old = conf.QUEUE.shape[0]
      conf.QUEUE = conf.QUEUE.drop_duplicates()
      dif = conf.QUEUE.shape[0] - old
      if dif > 0 and conf.LOG:
         click.secho(f"Removed {dif} duplicate cases from queue.",
                     fg='bright_yellow', bold=True)

   if not conf['NO_BATCH']:
      batches = batcher(conf)
   else:
      batches = [conf.QUEUE]

   for i, c in enumerate(batches):
      if i > 0:
         click.echo(f"Finished batch {i}. Now reading batch {i+1} of {len(batches)}")
      b = pd.DataFrame()
      if conf.IS_FULL_TEXT:
         b['AllPagesText'] = c
      else:
         tqdm.pandas(desc="PDF => Text")
         b['AllPagesText'] = pd.Series(c).progress_map(lambda x: getPDFText(x))
      b['CaseInfoOutputs'] = b['AllPagesText'].map(lambda x: getCaseInfo(x))
      b['CaseNumber'] = b['CaseInfoOutputs'].map(lambda x: x[0])
      b['Name'] = b['CaseInfoOutputs'].map(lambda x: x[1])
      b['Alias'] = b['CaseInfoOutputs'].map(lambda x: x[2])
      b['DOB'] = b['CaseInfoOutputs'].map(lambda x: x[3])
      b['Race'] = b['CaseInfoOutputs'].map(lambda x: x[4])
      b['Sex'] = b['CaseInfoOutputs'].map(lambda x: x[5])
      b['Address'] = b['CaseInfoOutputs'].map(lambda x: x[6])
      b['Phone'] = b['CaseInfoOutputs'].map(lambda x: x[7])

      tqdm.pandas(desc="Charges")
      chargestabs = charges(conf)


      tqdm.pandas(desc="Fee Sheets")
      b['FeeOutputs'] = b['AllPagesText'].progress_map(lambda x: getFeeSheet(x))
      b['TotalAmtDue'] = b['FeeOutputs'].map(lambda x: x[0])
      b['TotalBalance'] = b['FeeOutputs'].map(lambda x: x[1])
      b['PaymentToRestore'] = b['AllPagesText'].map(
          lambda x: getPaymentToRestore(x))
      b['FeeCodesOwed'] = b['FeeOutputs'].map(lambda x: x[3])
      b['FeeCodes'] = b['FeeOutputs'].map(lambda x: x[4])
      b['FeeSheet'] = b['FeeOutputs'].map(lambda x: x[5])

      feesheet = b['FeeOutputs'].map(lambda x: x[6])
      feesheet = feesheet.dropna()
      feesheet = feesheet.tolist()  # -> [df, df, df]
      feesheet = pd.concat(feesheet, axis=0, ignore_index=True)  # -> batch df
      fees = pd.concat([fees, feesheet],axis=0, ignore_index=True)
      chargestabs = chargestabs.dropna()

      try:
         feesheet['AmtDue'] = feesheet['AmtDue'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['AmtPaid'] = feesheet['AmtPaid'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['Balance'] = feesheet['Balance'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         feesheet['AmtHold'] = feesheet['AmtHold'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
      except:
         pass
      try:
         b['Phone'] = b['Phone'].map(lambda x: pd.to_numeric(x, 'coerce'))
         b['TotalAmtDue'] = b['TotalAmtDue'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         b['TotalBalance'] = b['TotalBalance'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
         b['PaymentToRestore'] = b['TotalBalance'].map(
             lambda x: pd.to_numeric(x, 'coerce'))
      except:
         pass
      cases = cases.convert_dtypes()
      fees = fees.convert_dtypes()
      chargestabs = chargestabs.convert_dtypes()

      if bool(conf.OUTPUT_PATH) and i > 0 and not conf.NO_WRITE:
         if os.path.getsize(conf.OUTPUT_PATH) > 1000:
            temp_no_write_arc = True
      if bool(conf.OUTPUT_PATH) and i > 0 and not conf.NO_WRITE:
         if os.path.getsize(conf.OUTPUT_PATH) > 1000:
            temp_no_write_tab = True
      if i == len(batches) - 1:
         temp_no_write_arc = False
         temp_no_write_tab = False

      if (i % 5 == 0 or i == len(batches) - 1) and not conf.NO_WRITE and temp_no_write_arc == False:
         if bool(conf.OUTPUT_PATH) and len(conf.OUTPUT_EXT) > 2:
            q = pd.Series(conf.QUEUE) if conf.IS_FULL_TEXT == False else pd.NaT
            ar = pd.DataFrame({
               'Path': q,
               'AllPagesText': b['AllPagesText'],
               'Timestamp': start_time
            }, index=range(0, conf.COUNT))
            try:
               arch = pd.concat([arch, ar], ignore_index=True, axis=0)
            except:
               pass
            arch.fillna('', inplace=True)
            arch.dropna(inplace=True)
            arch.to_pickle(conf.OUTPUT_PATH, compression="xz")

      b.drop(
         columns=['AllPagesText', 'CaseInfoOutputs',
             'FeeOutputs', 'FeeSheet'],
         inplace=True)
      if conf.DEDUPE:
         old = conf.QUEUE.shape[0]
         cases = cases.drop_duplicates()
         dif = cases.shape[0] - old
         if dif > 0 and conf.LOG:
            click.secho(f"Removed {dif} duplicate cases from queue.",
                        fg='bright_yellow', bold=True)

      b.fillna('', inplace=True)
      cases = pd.concat([cases, b], axis=0, ignore_index=True)
      if conf.MAKE == "cases":
         write(conf, cases)
      if conf.MAKE != "cases" and conf.NO_WRITE == False and temp_no_write_tab == False and (i % 5 == 0 or i == len(batches) - 1):
         if conf.OUTPUT_EXT == ".xls":
            try:
               with pd.ExcelWriter(conf.OUTPUT_PATH, engine="openpyxl") as writer:
                  cases.to_excel(writer, sheet_name="cases")
                  fees.to_excel(writer, sheet_name="fees")
                  chargestabs.to_excel(writer, sheet_name="charges")
            except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
               click.echo(f"openpyxl engine failed! Trying xlsxwriter...")
               with pd.ExcelWriter(conf.OUTPUT_PATH, engine="xlsxwriter") as writer:
                  cases.to_excel(writer, sheet_name="cases")
                  fees.to_excel(writer, sheet_name="fees")
                  chargestabs.to_excel(writer, sheet_name="charges")
         elif conf.OUTPUT_EXT == ".xlsx":
            try:
               with pd.ExcelWriter(conf.OUTPUT_PATH, engine="openpyxl") as writer:
                  cases.to_excel(writer, sheet_name="cases")
                  fees.to_excel(writer, sheet_name="fees")
                  chargestabs.to_excel(writer, sheet_name="charges")
            except (ImportError, IndexError, ValueError, ModuleNotFoundError, FileNotFoundError):
               try:
                  if conf.LOG:
                     click.echo(f"openpyxl engine failed! Trying xlsxwriter...")
                  with pd.ExcelWriter(conf.OUTPUT_PATH, engine="xlsxwriter") as writer:
                     cases.to_excel(writer, sheet_name="cases")
                     fees.to_excel(writer, sheet_name="fees")
                     chargestabs.to_excel(writer, sheet_name="charges")
               except (ImportError, FileNotFoundError, IndexError, ValueError, ModuleNotFoundError):
                  try:
                     cases.to_json(os.path.splitext(conf.OUTPUT_PATH)[
                                   0] + "-cases.json.zip", orient='table')
                     fees.to_json(os.path.splitext(conf.OUTPUT_PATH)[
                                  0] + "-fees.json.zip", orient='table')
                     chargestabs.to_json(os.path.splitext(conf.OUTPUT_PATH)[
                                     0] + "-charges.json.zip", orient='table')
                     click.echo(f"""Fallback export to {os.path.splitext(conf.OUTPUT_PATH)[0]}-cases.json.zip due to Excel engine failure, usually caused by exceeding max row limit for .xls/.xlsx files!""")
                  except (ImportError, FileNotFoundError, IndexError, ValueError):
                     click.echo("Failed to export!")
         elif conf.OUTPUT_EXT == ".json":
            if conf.COMPRESS:
               cases.to_json(conf.OUTPUT_PATH, orient='table', compression="zip")
            else:
               cases.to_json(conf.OUTPUT_PATH, orient='table')
         elif conf.OUTPUT_EXT == ".csv":
            if conf.COMPRESS:
               cases.to_csv(conf.OUTPUT_PATH, escapechar='\\', compression="zip")
            else:
               cases.to_csv(conf.OUTPUT_PATH, escapechar='\\')
         elif conf.OUTPUT_EXT == ".md":
            cases.to_markdown(conf.OUTPUT_PATH)
         elif conf.OUTPUT_EXT == ".txt":
            cases.to_string(conf.OUTPUT_PATH)
         elif conf.OUTPUT_EXT == ".dta":
            cases.to_stata(conf.OUTPUT_PATH)
         elif conf.OUTPUT_EXT == ".parquet":
            if conf.COMPRESS:
               cases.to_parquet(conf.OUTPUT_PATH, compression="brotli")
            else:
               cases.to_parquet(conf.OUTPUT_PATH)
         else:
            pd.Series([cases, fees, chargestabs]).to_string(conf.OUTPUT_PATH)

   complete(conf, cases, fees, chargestabs)
   return [cases, fees, chargestabs]

def map(conf, *args, bar=True):
   """
   Return DataFrame from config object and custom column 'getter' functions like below:

      def getter(full_case_text: str):
         out = re.search(...)
         ...
         return out
   
   Creates DataFrame with cols: CaseNumber, getter_1(), getter_2(), ...
   Getter functions must take case text as first parameter. Subsequent paramters can be set in map() after the getter parameter. Getter functions must return string, float, or int outputs to map().

   Example:
      >>  a = alac.map(conf,
                   alac.getAmtDueByCode, 'D999', 
                   alac.getAmtPaidByCode, 'D999', 
                   alac.getName, 
                   alac.getDOB)
      >>  print(a)

   Args:
      conf (pd.Series): Configuration object with paths and settings

      *args:  def getter(text: str) -> float, 
            def getter(text: str) -> int,
            def getter(text: str) -> str,
            def getter(text: str) -> bool, # check / debug

   
   Returns:
      out = pd.DataFrame({
            'CaseNumber': (str) full case number with county,
            'getter_1': (float) outputs of getter_1(),
            'getter_2': (int) outputs of getter_2(),
            'getter_3': (str) outputs of getter_2() 
         })
   
   """
   start_time = time.time()
   df_out = pd.DataFrame()
   temp_no_write_tab = False

   if conf.DEDUPE: # remove duplicates from queue
      old = conf.QUEUE.shape[0]
      conf.QUEUE = conf.QUEUE.drop_duplicates()
      dif = conf.QUEUE.shape[0] - old
      if dif > 0 and conf.LOG:
         click.secho(f"Removed {dif} duplicate cases from queue.", fg='bright_yellow', bold=True)

   if not conf.NO_BATCH: # split into batches
      batches = batcher(conf)
   else:
      batches = [conf.QUEUE]

   # sort args into functions and their parameters
   func = pd.Series(args).map(lambda x: 1 if inspect.isfunction(x) else 0)
   funcs = func.index.map(lambda x: args[x] if func[x] > 0 else np.nan)
   no_funcs = func.index.map(lambda x: args[x] if func[x] == 0 else np.nan)
   countfunc = func.sum()
   column_getters = pd.DataFrame(columns=['Name', 'Method', 'Arguments'], index=(range(0, countfunc)))

   # call methods, return outputs with pandas-friendly dtype
   def ExceptionWrapper(getter, text, *args):
      if args:
         outputs = pd.Series(getter(text, args))
      else:
         outputs = pd.Series(getter(text))
      return outputs.values

   # set name of methods to name w/o "get", i.e. getName() -> 'Name' column in df_out
   for i, x in enumerate(funcs):
      if inspect.isfunction(x):
         try:
            column_getters.Name[i] = str(x.__name__).replace("get","").upper()
         except:
            column_getters.Name[i] = str(x).replace("get","").upper()
         column_getters.Method[i] = x

   for i, x in enumerate(args):
      if not inspect.isfunction(x):
         column_getters.Arguments.iloc[i - 1] = x

   # run batch
   for i, b in enumerate(batches):
      if i > 0:
         click.echo(f"Finished batch {i}. Now reading batch {i+1} of {len(batches)}")
      b = pd.DataFrame()

      # stop slow writes on big files between batches
      if bool(conf.OUTPUT_PATH) and i > 0 and not conf.NO_WRITE:
         if os.path.getsize(conf.OUTPUT_PATH) > 500: 
            temp_no_write_tab = True
      if i == len(conf.QUEUE) - 1:
         temp_no_write_tab = False

      # get text
      if conf.IS_FULL_TEXT:
         allpagestext = conf.QUEUE
      else:
         if bar:
            tqdm.pandas(desc="PDF => Text")
            allpagestext = pd.Series(conf.QUEUE).progress_map(lambda x: getPDFText(x))
         else:
            allpagestext = pd.Series(conf.QUEUE).map(lambda x: getPDFText(x))

      # retrieve getter
      for i in column_getters.index:
         name = column_getters.Name[i]
         arg = column_getters.Arguments[i]
         getter = column_getters.Method[i]

      # map getter 
      for i, getter in enumerate(column_getters.Method.tolist()):
         arg = column_getters.Arguments[i]
         name = column_getters.Name[i]
         if bar:
            if arg == pd.NaT: 
               tqdm.pandas(desc=name)
               col = allpagestext.progress_map(lambda x: getter(x, arg))
            else: 
               tqdm.pandas(desc=name)
               col = allpagestext.progress_map(lambda x: getter(x))
         else:
            if arg == pd.NaT: 
               col = allpagestext.map(lambda x: getter(x, arg))
            else: 
               col = allpagestext.map(lambda x: getter(x))
         new_df_to_concat = pd.DataFrame({name: col})
         df_out = pd.concat([df_out, new_df_to_concat], axis=1)
         df_out = df_out.dropna(axis=1)
         df_out = df_out.dropna(axis=0)
         df_out = df_out.convert_dtypes()

      # fix empty -> str error
      for col in column_getters.columns:
         column_getters[col] = column_getters[col].dropna()
         column_getters[col] = column_getters[col].map(lambda x: "" if x == "Series([], Name: AmtDue, dtype: float64)" or x == "Series([], Name: AmtDue, dtype: object)" else x)
      # write
      if conf.NO_WRITE == False and temp_no_write_tab == False and (i % 5 == 0 or i == len(conf.QUEUE) - 1):
         write(conf, df_out)  # rem alac

   if not conf.NO_WRITE:
      write(conf, df_out)  # rem alac

   return df_out

# FETCH


def fetch(listpath, path, cID, uID, pwd, qmax=0, qskip=0, speed=1, no_log=False, no_update=False, debug=False):
   """
   Use headers NAME, PARTY_TYPE, SSN, DOB, COUNTY, DIVISION, CASE_YEAR, and FILED_BEFORE in an Excel spreadsheet to submit a list of queries for Alacorder to fetch.
   
   USE WITH CHROME (TESTED ON MACOS) 
   KEEP YOUR COMPUTER POWERED ON AND CONNECTED TO THE INTERNET.
   
   Args:
      listpath: (path-like obj) Query template path / input path
      path: (path-like obj) Path to output/downloads directory 
      cID (str): Alacourt.com Customer ID
      uID (str): Alacourt.com User ID
      pwd (str): Alacourt.com Password
      qmax (int, optional): Max queries to pull from inputs
      qskip (int, optional): Skip top n queries in inputs
      speed (int, optional): fetch rate multiplier
      no_log (bool, optional): Do not print logs to console
      no_update (bool, optional): Do not update input query file with completion status
      debug (bool, optional): Print detailed logs to console

   Returns:
      [driver, query_out, query_writer]:
         driver[0]: Google Chrome WebDriver() object 
         query_out[1]: (pd.Series) fetch queue
         query_writer[2]: (pd.DataFrame) Updated input query file
   """

   rq = readPartySearchQuery(listpath, qmax, qskip, no_log)

   query = pd.DataFrame(rq[0]) # for fetch - only search columns
   query_writer = pd.DataFrame(rq[1]) # original sheet for write completion 
   incomplete = query.RETRIEVED_ON.map(lambda x: True if x == "" else False)
   query = query[incomplete]

   options = webdriver.ChromeOptions()
   options.add_experimental_option('prefs', {
      "download.default_directory": path, #Change default directory for downloads
      "download.prompt_for_download": False, #To auto download the file
      "download.directory_upgrade": True,
      "plugins.always_open_pdf_externally": True #It will not display PDF directly in chrome
   })

   # start browser session, login
   if not no_log:
      click.secho("Starting browser... Do not close while in progress!",fg='bright_yellow',bold=True)
   driver = webdriver.Chrome(options=options)
   login(driver, cID, uID, pwd, speed)
   if not no_log:
      click.secho("Authentication successful. Fetching cases via party search...",fg='bright_green')

   # search, retrieve from URL, download to path
   for i, n in enumerate(query.index):
      if driver.current_url == "https://v2.alacourt.com/frmlogin.aspx":
            login(driver, cID, uID, pwd, speed, no_log)
      driver.implicitly_wait(4/speed)
      results = party_search(driver, name=query.NAME[n], party_type=query.PARTY_TYPE[n], ssn=query.SSN[n], dob=query.DOB[n], county=query.COUNTY[n], division=query.DIVISION[n], case_year=query.CASE_YEAR[n], filed_before=query.FILED_BEFORE[n], filed_after=query.FILED_AFTER[n], speed=speed, no_log=no_log)
      driver.implicitly_wait(4/speed)
      if len(results) == 0:
         query_writer['RETRIEVED_ON'][n] = str(math.floor(time.time()))
         query_writer['CASES_FOUND'][n] = "0"
         if not no_log:
            click.echo(f"{query.NAME[n]}: Found no results.")
         continue
      results = pd.Series(results)
      tqdm.pandas(desc=query.NAME[n])
      results.progress_map(lambda x: downloadPDF(driver, x))
      if not no_update:
         query_writer['RETRIEVED_ON'][n] = str(math.floor(time.time()))
         query_writer['CASES_FOUND'][n] = str(len(results))
         query_writer = query_writer.convert_dtypes()
         query_writer.to_excel(listpath,sheet_name="PartySearchQuery",index=False)
   return [driver, query_writer]


def party_search(driver, name = "", party_type = "", ssn="", dob="", county="", division="", case_year="", filed_before="", filed_after="", speed=1, no_log=False, debug=False):
   """
   Collect PDFs via SJIS Party Search Form from Alacourt.com
   Returns list of URLs for downloadPDF() to download
   
   Args:
      driver (WebDriver): selenium/chrome web driver object 
      name (str, optional): Name (LAST FIRST)
      party_type (str, optional): "Defendants" | "Plaintiffs" | "ALL"
      ssn (str, optional): Social Security Number
      dob (str, optional): Date of Birth
      county (str, optional): County
      division (str, optional): "All Divisions"
         "Criminal Only"
         "Civil Only"
         "CS - CHILD SUPPORT"
         "CV - CIRCUIT - CIVIL"
         "CC - CIRCUIT - CRIMINAL"
         "DV - DISTRICT - CIVIL"
         "DC - DISTRICT - CRIMINAL"
         "DR - DOMESTIC RELATIONS"
         "EQ - EQUITY-CASES"
         "MC - MUNICIPAL-CRIMINAL"
         "TP - MUNICIPAL-PARKING"
         "SM - SMALL CLAIMS"
         "TR - TRAFFIC"
      case_year (str, optional): YYYY
      filed_before (str, optional): M/DD/YYYY
      filed_after (str, optional): M/DD/YYYY
      speed (int, optional): fetch rate multiplier
      no_log (bool, optional): Do not print logs.
      debug (bool, optional): Print detailed logs.
   
   Returns:
      URL list to PDFs
   """
   speed = speed * 1.5


   if "frmIndexSearchForm" not in driver.current_url:
      driver.get("https://v2.alacourt.com/frmIndexSearchForm.aspx")

   driver.implicitly_wait(5/speed)


   # connection error 
   try:
      party_name_box = driver.find_element(by=By.NAME,value="ctl00$ContentPlaceHolder1$txtName")
   except selenium.common.exceptions.NoSuchElementException:
      if not no_log:
         click.secho("Connection error. Attempting reconnection...",fg='red')
      driver.refresh()
      driver.implicitly_wait(10/speed)
      party_name_box = driver.find_element(by=By.NAME,value="ctl00$ContentPlaceHolder1$txtName")
      if not no_log:
         click.secho("Successfully connected and logged into Alacourt!",fg='green',bold=True)

   # field search

   if name != "":
      party_name_box.send_keys(name)
   if ssn != "":
      ssn_box = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$txtSSN")
      ssn_box.send_keys(ssn)
   if dob != "":
      date_of_birth_box = driver.find_element(by=By.NAME,value="ctl00$ContentPlaceHolder1$txtDOB")
      date_of_birth_box.send_keys(dob)
   if party_type != "":
      party_type_select = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$rdlPartyType")
      pts = Select(party_type_select)
      if party_type == "plaintiffs":
         pts.select_by_visible_text("Plaintiffs")
      if party_type == "defendants":
         pts.select_by_visible_text("Defendants")
      if party_type == "all":
         pts.select_by_visible_text("ALL")

   if county != "":
      county_select = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$ddlCounties")
      scounty = Select(county_select)
      scounty.select_by_visible_text(county)
   if division != "":
      division_select = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$UcddlDivisions1$ddlDivision")
      sdivision = Select(division_select)
      sdivision.select_by_visible_text(division)
   if case_year != "":
      case_year_select = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$ddlCaseYear")
      scase_year = Select(case_year_select)
      scase_year.select_by_visible_text(case_year)
   no_records_select = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$ddlNumberOfRecords")
   sno_records = Select(no_records_select)
   sno_records.select_by_visible_text("1000")
   if filed_before != "":
      filed_before_box = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$txtFrom")
      filed_before_box.send_keys(filed_before)
   if filed_after != "":
      filed_after_box = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$txtTo")
      filed_after_box.send_keys(filed_after)

   driver.implicitly_wait(1/speed)

   # submit search
   search_button = driver.find_element(by=By.ID,value="searchButton")

   driver.implicitly_wait(1/speed)
   try:
      search_button.click()
   except:
      driver.implicitly_wait(5/speed)
      time.sleep(5)

   if debug:
      click.echo("Submitted party search form...")

   driver.implicitly_wait(1/speed)

   # count pages
   try:
      page_counter = driver.find_element(by=By.ID,value="ContentPlaceHolder1_dg_tcPageXofY").text
      pages = int(page_counter.strip()[-1])

   except:
      pages = 1

   # count results
   try:
      results_indicator = driver.find_element(by=By.ID, value="ContentPlaceHolder1_lblResultCount")
      results_count = int(results_indicator.text.replace("Search Results: ","").replace(" records returned.","").strip())
      if results_count == 1000 and debug or no_log:
         click.echo(f"Max records (1000) returned for party {name}!")
   except:
      pass

   if debug:
      click.echo(f"Found {results_count} results, fetching URLs and downloading PDFs...")


   # get PDF links from each page
   pdflinks = []
   i = 0
   for i in range(0,pages):
      driver.implicitly_wait(0.5/speed)
      hovers = driver.find_elements(By.CLASS_NAME, "menuHover")
      for x in hovers:
         try:
            a = x.get_attribute("href")
            if "PDF" in a:
               pdflinks.append(a)
         except:
            pass
      driver.implicitly_wait(0.5/speed)
      try:
         pager_select = Select(driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder1$dg$ctl18$ddlPages"))
         next_pg = int(pager_select.text) + 1
         driver.implicitly_wait(0.5/speed)
      except:
         try:
            driver.implicitly_wait(0.5/speed)
            time.sleep(0.5/speed)
            next_button = driver.find_element(by=By.ID, value = "ContentPlaceHolder1_dg_ibtnNext")
            next_button.click()
         except:
            continue
   return pdflinks

def downloadPDF(driver, url, no_log=False):
   """With (driver), download PDF at (url)
   
   Args:
      driver (WebDriver): Google Chrome selenium.WebDriver() object
      url (TYPE): Description
      no_log (bool, optional): Description
   
   Deleted Parameters:
      speed (int, optional): fetch rate multiplier
   """
   a = driver.get(url)
   driver.implicitly_wait(0.5)
   time.sleep(1)


def login(driver, cID, username, pwd, speed, no_log=False, path=""):
   """Login to Alacourt.com using (driver) and auth (cID, username, pwd) at (speed) for browser download to directory at (path)
   
   Args:
      driver (WebDriver): Google Chrome selenium.WebDriver() object
      cID (str): Alacourt.com Customer ID
      username (str): Alacourt.com User ID
      pwd (str): Alacourt.com Password
      speed (TYPE): fetch rate multiplier
      no_log (bool, optional): Do not print logs
      path (str, optional): Set browser download path 
   
   Returns:
      driver (WebDriver): Google Chrome selenium.WebDriver() object
   """
   if driver == None:
      options = webdriver.ChromeOptions()
      options.add_experimental_option('prefs', {
         "download.default_directory": path, #Change default directory for downloads
         "download.prompt_for_download": False, #To auto download the file
         "download.directory_upgrade": True,
         "plugins.always_open_pdf_externally": True #It will not display PDF directly in chrome
      })
      driver = webdriver.Chrome(options=options)

   if not no_log:
      click.echo("Connecting to Alacourt...")


   login_screen = driver.get("https://v2.alacourt.com/frmlogin.aspx")

   if not no_log:
      click.echo("Logging in...")

   driver.implicitly_wait(0.5/speed)
   
   cID_box = driver.find_element(by=By.NAME, 
      value="ctl00$ContentPlaceHolder$txtCusid")
   username_box = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder$txtUserId")
   pwd_box = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder$txtPassword")
   login_button = driver.find_element(by=By.ID, value="ContentPlaceHolder_btLogin")

   cID_box.send_keys(cID)
   username_box.send_keys(username)
   pwd_box.send_keys(pwd)

   driver.implicitly_wait(1/speed)

   login_button.click()

   driver.implicitly_wait(1/speed)

   try:
      continueLogIn = driver.find_element(by=By.NAME, value="ctl00$ContentPlaceHolder$btnContinueLogin")
      continueLogIn.click()
   except:
      pass


   driver.get("https://v2.alacourt.com/frmIndexSearchForm.aspx")

   if not no_log:
      click.secho("Successfully connected and logged into Alacourt!",fg='bright_green')


   driver.implicitly_wait(0.5/speed)

   return driver


def readPartySearchQuery(path, qmax=0, qskip=0, speed=1, no_log=False):
   """Reads and interprets query template spreadsheets for `alacorder fetch` to queue from. Use headers NAME, PARTY_TYPE, SSN, DOB, COUNTY, DIVISION, CASE_YEAR, and FILED_BEFORE in an Excel spreadsheet, CSV, or JSON file to submit a list of queries for Alacorder to fetch.
   
   Args:
      path (TYPE): Description
      qmax (int, optional): Description
      qskip (int, optional): Description
      speed (int, optional): Description
      no_log (bool, optional): Description
   
   Returns:
      [query_out, writer_df]:
         query_out: (pd.DataFrame) queue object for alac.fetch()
         writer_df: (pd.DataFrame) progress log to be written back to (path)
   
   Raises:
      Exception: Connection error!
   """
   good = os.path.exists(path)
   ext = os.path.splitext(path)[1]
   if ext == ".xlsx" or ".xls":
      query = pd.read_excel(path, dtype=pd.StringDtype())
   if ext == ".csv":
      query = pd.read_csv(path, dtype=pd.StringDtype())
   if ext == ".json":
      query = pd.read_json(path, orient='table', dtype=pd.StringDtype())
   if qskip > 0:
      query = query.truncate(before=qskip)
   if qmax > 0:
      query = query.truncate(after=qmax+qskip)

   writer_df = pd.DataFrame(query)

   if "RETRIEVED_ON" not in writer_df.columns:
      writer_df['RETRIEVED_ON'] = pd.NaT
      writer_df['CASES_FOUND'] = pd.NaT

   query_out = pd.DataFrame(columns=["NAME", "PARTY_TYPE", "SSN", "DOB", "COUNTY", "DIVISION", "CASE_YEAR", "NO_RECORDS", "FILED_BEFORE", "FILED_AFTER", "RETRIEVED_ON", "CASES_FOUND"])

   clist = []
   for c in query.columns:
      if c.upper().strip().replace(" ","_") in ["NAME", "PARTY", "DATE_OF_BIRTH", "BIRTHDATE", "PARTY_TYPE", "SSN", "DOB", "COUNTY", "DIVISION", "CASE_YEAR", "NO_RECORDS", "FILED_BEFORE", "FILED_AFTER", "RETRIEVED_ON", "CASES_FOUND"]:
         c = c.replace("DATE_OF_BIRTH","DOB").replace("BIRTHDATE","DOB").replace("PARTY","PARTY_TYPE").replace("PARTY_TYPE_TYPE","PARTY_TYPE").strip()
         clist += [c]
         query_out[c.upper().strip().replace(" ","_")] = query[c]
   clist = pd.Series(clist).drop_duplicates().tolist()
   if clist == []:
      raise Exception("Invalid template! Use headers NAME, PARTY_TYPE, SSN, DOB, COUNTY, DIVISION, CASE_YEAR, and FILED_BEFORE in a spreadsheet or JSON file to submit a list of queries for Alacorder to fetch.")
      click.echo(f"Field columns {clist} identified in query file.")

   query_out = query_out.fillna('')
   return [query_out, writer_df]

# CONFIG


def init(conf):
   """
   Start export function corresponding to conf.MAKE, conf.TABLE
   
   Args:
      conf (pd.Series): Configuration object with paths and settings
   
   Returns:
      DataFrame written to file at conf.OUTPUT_PATH
      DataFrame
   """
   a = []
   if conf.FETCH == True:
      fetch(conf.INPUT_PATH, conf.OUTPUT_PATH, fetch_cID=conf.ALA_CUSTOMER_ID, fetch_uID=conf.ALA_USER_ID, fetch_pwd=conf.ALA_PASSWORD, fetch_qmax=conf.FETCH_QMAX, fetch_qskip=conf.FETCH_QSKIP,fetch_speed=conf.FETCH_SPEED)
   elif conf.MAKE == "multiexport" and (conf.TABLE == "" or conf.TABLE == "all"):
      a = cases(conf)
   elif conf.MAKE == "archive":
      a = archive(conf)
   elif conf.TABLE == "cases":
      a = cases(conf)
   elif conf.TABLE == "fees":
      a = fees(conf)
   elif conf.TABLE == "charges":
      a = charges(conf)
   elif conf.TABLE == "disposition":
      a = charges(conf)
   elif conf.TABLE == "filing":
      a = charges(conf)
   else:
      a = None
   return a


def setinputs(path, debug=False, fetch=False):
   """Verify and configure input path. Must use set() to finish configuration even if NO_WRITE mode. Call setoutputs() with no arguments.  
   
   Args:
      path (str): Path to PDF directory, compressed archive, or query template sheet,
      debug (bool, optional): Print detailed logs,
      fetch (bool, optional): Configure template sheet for web PDF retrieval 
   
   Returns:
      inp = pd.Series({
         INPUT_PATH: (path-like obj) path to input file or directory,
         IS_FULL_TEXT: (bool) origin is case text (False = PDF directory or query template),
         QUEUE: (list) paths or case texts (dep. on IS_FULL_TEXT) for export function,
         FOUND: (int) total cases found in input path,
         GOOD: (bool) configuration succeeded,
         PICKLE: (pd.DataFrame) original archive file (if appl.),
         ECHO: (IFrame(str)) log data for console 
      })
   """
   if fetch == True or (os.path.splitext(path)[1] in [".xlsx",".xls",".csv",".json"]):
      queue = readPartySearchQuery(path)
      out = pd.Series({
         'INPUT_PATH': path,
         'IS_FULL_TEXT': False,
         'QUEUE': queue,
         'FOUND': 0,
         'GOOD': True,
         'PICKLE': '',
         'ECHO': ''
      })
      return out
      
   else:
      found = 0
      is_full_text = False
      good = False
      pickle = None
      if not debug:
         warnings.filterwarnings('ignore')

      if isinstance(path, pd.core.frame.DataFrame) or isinstance(path, pd.core.series.Series):
         if "AllPagesText" in path.columns and path.shape[0] > 0:
            queue = path['AllPagesText']
            is_full_text = True
            found = len(queue)
            good = True
            pickle = path
            path = "NONE"

      elif isinstance(path, str) and path != "NONE":
         queue = pd.Series()
         if os.path.isdir(path):  # if PDF directory -> good
            queue = pd.Series(glob.glob(path + '**/*.pdf', recursive=True))
            if queue.shape[0] > 0:
               found = len(queue)
               good = True
         elif os.path.isfile(path) and os.path.splitext(path)[1] == ".xz": 
            good = True
            pickle = pd.read_pickle(path, compression="xz")
            queue = pickle['AllPagesText']
            is_full_text = True
            found = len(queue)
         elif os.path.isfile(path) and (os.path.splitext(path)[1] == ".zip"):
            nzpath = path.replace(".zip","")
            nozipext = os.path.splitext(nzpath)[1]
            if debug:
               click.echo(f"NZPATH: {nozipext}, NOZIPEXT: {nozipext}, PATH: {path}")
            if nozipext == ".json":
               pickle = pd.read_json(path, orient='table',compression="zip")
               queue = pickle['AllPagesText']
               is_full_text = True
               found = len(queue)
               good = True
            if nozipext == ".csv":
               pickle = pd.read_csv(path, escapechar='\\',compression="zip")
               queue = pickle['AllPagesText']
               is_full_text = True
               good = True
               found = len(queue)
            if nozipext == ".parquet":
               pickle = pd.read_parquet(path,compression="zip")
               queue = pickle['AllPagesText']
               is_full_text = True
               found = len(queue)
               good = True
            if nozipext == ".pkl":
               pickle = pd.read_pickle(path,compression="zip")
               queue = pickle['AllPagesText']
               is_full_text = True
               found = len(queue)
               good = True
         elif os.path.isfile(path) and os.path.splitext(path)[1] == ".json":
            try:
               pickle = pd.read_json(path, orient='table')
            except:
               pickle = pd.read_json(path, orient='table',compression="zip")
            queue = pickle['AllPagesText']
            is_full_text = True
            found = len(queue)
            good = True
         elif os.path.isfile(path) and os.path.splitext(path)[1] == ".csv":
            pickle = pd.read_csv(path, escapechar='\\')
            queue = pickle['AllPagesText']
            is_full_text = True
            found = len(queue)
            good = True
         elif os.path.isfile(path) and os.path.splitext(path)[1] == ".pkl":
            pickle = pd.read_pickle(path)
            queue = pickle['AllPagesText']
            is_full_text = True
            found = len(queue)
            good = True
         elif os.path.isfile(path) and os.path.splitext(path)[1] == ".parquet":
            pickle = pd.read_parquet(path)
            queue = pickle['AllPagesText']
            is_full_text = True
            found = len(queue)
            good = True
         else:
            good = False
      else:
         good = False

      if good:
         echo = click.style(f"Found {found} cases in input.", italic=True, fg='bright_yellow')
      else:
         echo = click.style(
            f"""Alacorder failed to configure input! Try again with a valid PDF directory or full text archive path, or run 'python -m alacorder --help' in command line for more details.""",
            fg='red', bold=True)
      out = pd.Series({
         'INPUT_PATH': path,
         'IS_FULL_TEXT': is_full_text,
         'QUEUE': pd.Series(queue),
         'FOUND': found,
         'GOOD': good,
         'PICKLE': pickle,
         'ECHO': echo
      })
      return out


def setoutputs(path="", debug=False, archive=False, table="", fetch=False):
   """Verify and configure output path. Must use set(inconf, outconf) to finish configuration.

   Args:
      path (str): Path to PDF directory, compressed archive, or query template sheet,
      debug (bool, optional): Print detailed logs,
      archive (bool, optional): Create full text archive,
      table (str, optional): Create table ('cases', 'fees', 'charges', 'filing', 'disposition') 

   Returns:
   out = pd.Series({
      'OUTPUT_PATH': (path-like obj) path to output file or directory,
      'ZIP_OUTPUT_PATH': (path-like obj) path to output file or directory with .zip if zip compression enabled,
      'OUTPUT_EXT': (str) output file extension,
      'MAKE': (str) table, archive, or directory to be made at init(),
      'GOOD': (bool) configuration succeeded,
      'EXISTING_FILE': (bool) existing file at output path,
      'ECHO': (IFrame(str)) log data 
   )}
   """
   good = False
   make = ""
   compress = False
   exists = False
   echo = ""
   ext = ""
   if not debug:
      warnings.filterwarnings('ignore')

   if fetch:
      if os.path.isdir(path):  # if PDF directory -> good
         make = "pdf_directory"
         good = True
      else:
         good = False
   else:
      if ".zip" in path or ".xz" in path:
         compress=True
      
      nzpath = path.replace(".zip","")

      # if no output -> set default
      if path == "" and archive == False:
         path = "NONE"
         ext = "NONE"
         make == "multiexport" if table != "cases" and table != "charges" and table != "fees" and table != "disposition" and table != "filing" else "singletable"
         good = True
         exists = False
         echo = click.style(f"Output successfully configured.", italic=True, fg='bright_yellow')
      if path == "" and archive == True:
         path = "NONE"
         ext = "NONE"
         make == "archive"
         good = True
         exists = False
         echo = click.style(
               f"""Output successfully configured for {"table" if make == "multiexport" or make == "singletable" else "archive"} export.""",
               italic=True, fg='bright_yellow')

      # if path
      if isinstance(path, str) and path != "NONE" and make != "pdf_directory":
         exists = os.path.isfile(path)
         ext = os.path.splitext(path)[1]
         if ext == ".zip":  # if vague due to compression, assume archive
            ext = os.path.splitext(os.path.splitext(path)[0])[1]
            compress = True
            good = True

         if ext == ".xz" or ext == ".parquet" or ext == ".pkl":  # if output is existing archive
            make = "archive"
            compress = True
            good = True
         elif ext == ".xlsx" or ext == ".xls":  # if output is multiexport
            make = "multiexport"
            good = True
         elif archive == False and (ext == ".csv" or ext == ".dta" or ext == ".json" or ext == ".txt"):
            make = "singletable"
            good = True
         elif archive == True and (ext == ".csv" or ext == ".dta" or ext == ".json" or ext == ".txt"):
            make = "archive"
            good = True

         if good:
            if archive or make == "archive":
               echo = "Output path successfully configured for archive export."
            elif make == "pdf_directory":
               echo = "Output path successfully configured to store case detail PDF retrieved from Alacourt."
   out = pd.Series({
      'OUTPUT_PATH': nzpath,
      'ZIP_OUTPUT_PATH': path,
      'OUTPUT_EXT': ext,
      'MAKE': make,
      'GOOD': good,
      'EXISTING_FILE': exists,
      'ECHO': echo,
      'COMPRESS': compress
   })
   return out


# add fetch_cID etc. to output Series
def set(inputs, outputs=None, count=0, table='', overwrite=False, log=True, dedupe=False, no_write=False, no_prompt=False, debug=False, no_batch=True, compress=False, fetch=False, fetch_cID="", fetch_uID="", fetch_pwd="",fetch_qmax=0, fetch_qskip=0, fetch_speed=1):
   """Verify and configure task from setinputs() and setoutputs() configuration objects and **kwargs. Must call init() or export function to begin task. 
   DO NOT USE TO CALL ALAC.FETCH() OR OTHER BROWSER-DEPENDENT METHODS. 
   
   Args:
      inputs (obj): configuration object from setinputs(),
      outputs (None, optional): configuration object from setoutputs(),
      count (int, optional): (int) total cases in queue,
      table (str, optional): table export setting,  
      overwrite (bool, optional): overwrite without prompting, 
      log (bool, optional): print logs to console,
      dedupe (bool, optional): remove duplicates from archive export,
      no_write (bool, optional): don't write to output file,
      no_prompt (bool, optional): don't prompt user for input,
      debug (bool, optional): print detailed logs,
      no_batch (bool, optional): don't split task into batches,
      compress (bool, optional): compress output file (Excel files not supported)
   
   Returns:
      
   out = pd.Series({
      'GOOD': (bool) configuration succeeded,
      'ECHO':  (IFrame(str)) log data,
      'TIME': timestamp at configuration,

      'QUEUE': (list) paths or case texts to process,
      'COUNT': (int) total in queue,
      'IS_FULL_TEXT': (bool) origin is case text (False = PDF directory or query template),
      'MAKE': (str) table, archive, or directory to be made at init(),
      'TABLE': Table export selection if appl. ('cases', 'fees', 'charges', 'filing', 'disposition')

      'INPUT_PATH': (path-like obj) path to input file or directory,
      'OUTPUT_PATH': (path-like obj) path to output file or directory,
      'OUTPUT_EXT': (str) output file extension,

      'OVERWRITE': (bool) existing file at output path will be overwritten,
      'FOUND': (int) cases found in inputs,

      'DEDUPE': (bool) remove duplicate cases from exported archives
      'LOG': (bool) print logs to console,
      'DEBUG': (bool) print detailed logs,
      'NO_PROMPT': (bool) don't prompt user for input,
      'NO_WRITE': (bool) don't write file to output path,
      'NO_BATCH': (bool) don't split task into batches,
      'COMPRESS': (bool) compress output if supported 

      'FETCH': fetch,
      'ALA_CUSTOMER_ID': fetch_cID,
      'ALA_USER_ID': fetch_uID,
      'ALA_PASSWORD': fetch_pwd
   })

   """

   echo = ""
   will_overwrite = False
   good = True

   if not debug:
      sys.tracebacklimit = 0
      warnings.filterwarnings('ignore')
   else:
      sys.tracebacklimit = 10

   # DEDUPE
   content_len = len(inputs.QUEUE)
   if dedupe and not fetch:
      queue = inputs.QUEUE.drop_duplicates()
      dif = content_len - queue.shape[0]
      if (log or debug) and dif > 0:
         click.secho(f"Removed {dif} duplicate cases from queue.", fg='bright_yellow', bold=True)
   else:
      queue = inputs.QUEUE


   # COUNT
   content_len = inputs['FOUND']
   if content_len > count and count != 0:
      ind = count - 1
      queue = inputs.QUEUE[0:ind]
   elif count > content_len and content_len > 0:
      count = inputs.QUEUE.shape[0]
   elif count < content_len and count == 0:
      count = content_len
   else:
      queue = inputs.QUEUE

   count += 1

   echo = echo_conf(inputs.INPUT_PATH, outputs.MAKE, outputs.OUTPUT_PATH, overwrite, no_write, dedupe, no_prompt, compress)

   if outputs.COMPRESS == True:
      compress = True

   cftime = time.time()

   out = pd.Series({
      'GOOD': good,
      'ECHO': echo,
      'TIME': cftime,

      'QUEUE': queue,
      'COUNT': count,
      'IS_FULL_TEXT': bool(inputs.IS_FULL_TEXT),
      'MAKE': outputs.MAKE,
      'TABLE': table,

      'INPUT_PATH': inputs.INPUT_PATH,
      'OUTPUT_PATH': outputs.OUTPUT_PATH,
      'OUTPUT_EXT': outputs.OUTPUT_EXT,

      'OVERWRITE': will_overwrite,
      'FOUND': inputs.FOUND,

      'DEDUPE': dedupe,
      'LOG': log,
      'DEBUG': debug,
      'NO_PROMPT': no_prompt,
      'NO_WRITE': no_write,
      'NO_BATCH': no_batch,
      'COMPRESS': compress,

      'FETCH': fetch,
      'ALA_CUSTOMER_ID': fetch_cID,
      'ALA_USER_ID': fetch_uID,
      'ALA_PASSWORD': fetch_pwd
   })

   return out


def batcher(conf):
   """Splits conf.QUEUE objects into batches
   
   Args:
      conf (pd.Series): Configuration object with paths and settings
   
   Returns:
      batches: (numpy.array) list of pd.Series()
   """

   q = conf['QUEUE']
   if not conf.IS_FULL_TEXT:
      if conf.FOUND < 1000:
         batchsize = 250
      elif conf.FOUND > 10000:
         batchsize = 2500
      else:
         batchsize = 1000
      batches = np.array_split(q, 3)
   else:
      batches = np.array_split(q, 1)
   return batches



def setpaths(input_path, output_path=None, count=0, table='', overwrite=False, log=True, dedupe=False, no_write=False, no_prompt=False, debug=False, no_batch=True, compress=False, fetch=False, fetch_cID="", fetch_uID="", fetch_pwd="", fetch_qmax="", fetch_qskip="", fetch_speed=1): # DOC
   """Substitute paths for setinputs(), setoutputs() configuration objects for most tasks. Must call init() or export function to begin task. 
   DO NOT USE TO CALL ALAC.FETCH() OR OTHER BROWSER-DEPENDENT METHODS. 
   
   Args:
      input_path (str): (path-like obj) path to input file or directory,
      output_path (None, optional): (path-like obj) path to output file or directory,
      count (int, optional): (int) total cases in queue,
      table (str, optional): table export setting,  
      overwrite (bool, optional): overwrite without prompting, 
      log (bool, optional): print logs to console,
      dedupe (bool, optional): remove duplicates from archive export,
      no_write (bool, optional): don't write to output file or directory,
      no_prompt (bool, optional): don't prompt user for input,
      debug (bool, optional): print detailed logs,
      no_batch (bool, optional): don't split task into batches,
      compress (bool, optional): compress output file (Excel files not supported)
   
   Returns:
      
   out = pd.Series({
      'GOOD': (bool) configuration succeeded,
      'ECHO':  (IFrame(str)) log data,
      'TIME': timestamp at configuration,

      'QUEUE': (list) paths or case texts to process,
      'COUNT': (int) total in queue,
      'IS_FULL_TEXT': (bool) origin is case text (False = PDF directory or query template),
      'MAKE': (str) table, archive, or directory to be made at init(),
      'TABLE': Table export selection if appl. ('cases', 'fees', 'charges', 'filing', 'disposition')

      'INPUT_PATH': (path-like obj) path to input file or directory,
      'OUTPUT_PATH': (path-like obj) path to output file or directory,
      'OUTPUT_EXT': (str) output file extension,

      'OVERWRITE': (bool) existing file at output path will be overwritten,
      'FOUND': (int) cases found in inputs,

      'DEDUPE': (bool) remove duplicate cases from exported archives
      'LOG': (bool) print logs to console,
      'DEBUG': (bool) print detailed logs,
      'NO_PROMPT': (bool) don't prompt user for input,
      'NO_WRITE': (bool) don't write file to output path,
      'NO_BATCH': (bool) don't split task into batches,
      'COMPRESS': (bool) compress output if supported,
   })

   """

   a = setinputs(input_path, fetch=fetch)
   if log:
      click.secho(a.ECHO)
   b = setoutputs(output_path, fetch=fetch)
   if b.MAKE == "archive": #
      compress = True
   if log:
      click.secho(b.ECHO)
   c = set(a, b, count=count, table=table, overwrite=overwrite, log=log, dedupe=dedupe, no_write=no_write, no_prompt=no_prompt, debug=debug, no_batch=no_batch, compress=compress, fetch=fetch, fetch_cID=fetch_cID, fetch_uID=fetch_uID, fetch_pwd=fetch_pwd, fetch_qmax=fetch_qmax, fetch_qskip=fetch_qskip, fetch_speed=fetch_speed)
   if log:
      click.secho(c.ECHO)
   return c


def setinit(input_path, output_path=None, archive=False,count=0, table='', overwrite=False, log=True, dedupe=False, no_write=False, no_prompt=False, debug=False, no_batch=True, compress=False, fetch=False, fetch_cID="",fetch_uID="", fetch_pwd="", fetch_qmax=0, fetch_qskip=0, fetch_speed=1): # DOC
   """
   Initialize tasks from paths without calling setinputs(), setoutputs(), or set().
   Note additional fetch flags for auth info if task involves alac.fetch()
   
   Args:
      input_path (str): (path-like obj) path to input file or directory,
      output_path (None, optional): (path-like obj) path to output file or directory,
      archive (bool, optional): make compressed archive,
      count (int, optional): (int) total cases in queue,
      table (str, optional): table export setting,  
      overwrite (bool, optional): overwrite without prompting, 
      log (bool, optional): print logs to console,
      dedupe (bool, optional): remove duplicates from archive export,
      no_write (bool, optional): don't write to output file or directory,
      no_prompt (bool, optional): don't prompt user for input,
      debug (bool, optional): print detailed logs,
      no_batch (bool, optional): don't split task into batches,
      compress (bool, optional): compress output file (Excel files not supported)
      fetch_cID (str): Alacourt.com Customer ID
      fetch_uID (str): Alacourt.com User ID
      fetch_pwd (str): Alacourt.com Password
      fetch_qmax (int, optional): Max queries to pull from inputs
      fetch_qskip (int, optional): Skip top n queries in inputs
      fetch_speed (int, optional): Fetch rate multiplier

   Returns: [out, init_out]
      
   out = pd.Series({
      'GOOD': (bool) configuration succeeded,
      'ECHO':  (IFrame(str)) log data,
      'TIME': timestamp at configuration,

      'QUEUE': (list) paths or case texts to process,
      'COUNT': (int) total in queue,
      'IS_FULL_TEXT': (bool) origin is case text (False = PDF directory or query template),
      'MAKE': (str) table, archive, or directory to be made at init(),
      'TABLE': Table export selection if appl. ('cases', 'fees', 'charges', 'filing', 'disposition')

      'INPUT_PATH': (path-like obj) path to input file or directory,
      'OUTPUT_PATH': (path-like obj) path to output file or directory,
      'OUTPUT_EXT': (str) output file extension,

      'OVERWRITE': (bool) existing file at output path will be overwritten,
      'FOUND': (int) cases found in inputs,

      'DEDUPE': (bool) remove duplicate cases from exported archives
      'LOG': (bool) print logs to console,
      'DEBUG': (bool) print detailed logs,
      'NO_PROMPT': (bool) don't prompt user for input,
      'NO_WRITE': (bool) don't write file to output path,
      'NO_BATCH': (bool) don't split task into batches,
      'COMPRESS': (bool) compress output if supported 
   })

   init_out = pd.DataFrame() # depends on init() configuration

   """
   
   if fetch:
      fetch_no_log = not log
      if not isinstance(input_path, pd.core.series.Series) and not isinstance(output_path, pd.core.series.Series):
         fetch(input_path, output_path, fetch_cID, fetch_uID, fetch_pwd, fetch_qmax, fetch_qskip, fetch_speed, fetch_no_log)
      else:
         input_path = setinputs(input_path)
         output_path = setoutputs(output_path)
         fetch(input_path, output_path, fetch_cID, fetch_uID, fetch_pwd, fetch_qmax, fetch_qskip, fetch_speed, fetch_no_log)
   else:
      if not isinstance(input_path, pd.core.series.Series) and input_path != None:
         input_path = setinputs(input_path)

      if not isinstance(output_path, pd.core.series.Series) and output_path != None:
         output_path = setoutputs(output_path)

      a = set(input_path, output_path, count=count, table=table, overwrite=overwrite, log=log, dedupe=dedupe, no_write=no_write, no_prompt=no_prompt,debug=debug, no_batch=no_batch, compress=compress)
      
      if archive == True:
         a.MAKE = "archive"
      
      b = init(a)

      return b

# GETTERS

def getPDFText(path: str) -> str:
   """Returns PyPDF2 extract_text() outputs for all pages from path
   
   Args:
      path (str): Description
   
   Returns:
      str: Description
   """
   text = ""
   pdf = PyPDF2.PdfReader(path)
   for pg in pdf.pages:
      text += pg.extract_text()
   return text


def getCaseNumber(text: str):
   """Returns full case number with county number prefix from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      county: str = re.search(r'(?:County\: )(\d{2})(?:Case)', str(text)).group(1).strip()
      case_num: str = county + "-" + re.search(r'(\w{2}\-\d{4}-\d{6}.\d{2})', str(text)).group(1).strip()
      return case_num
   except (IndexError, AttributeError):
      return ""


def getName(text: str):
   """Returns name from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   name = ""
   if bool(re.search(r'(?a)(VS\.|V\.{1})(.+)(Case)*', text, re.MULTILINE)):
      name = re.search(r'(?a)(VS\.|V\.{1})(.+)(Case)*', text, re.MULTILINE).group(2).replace("Case Number:",
                                                                        "").strip()
   else:
      if bool(re.search(r'(?:DOB)(.+)(?:Name)', text, re.MULTILINE)):
         name = re.search(r'(?:DOB)(.+)(?:Name)', text, re.MULTILINE).group(1).replace(":", "").replace(
            "Case Number:", "").strip()
   return name


def getDOB(text: str):
   """Returns DOB from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   dob = ""
   if bool(re.search(r'(\d{2}/\d{2}/\d{4})(?:.{0,5}DOB\:)', str(text), re.DOTALL)):
      dob: str = re.search(r'(\d{2}/\d{2}/\d{4})(?:.{0,5}DOB\:)', str(text), re.DOTALL).group(1)
   return dob


def getTotalAmtDue(text: str):
   """Returns total amt due from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      trowraw = re.findall(r'(Total.*\$.*)', str(text), re.MULTILINE)[0]
      totalrow = re.sub(r'[^0-9|\.|\s|\$]', "", trowraw)
      if len(totalrow.split("$")[-1]) > 5:
         totalrow = totalrow.split(" . ")[0]
      tdue = totalrow.split("$")[1].strip().replace("$", "").replace(",", "").replace(" ", "")
   except IndexError:
      tdue = ""
   return tdue


def getAddress(text: str):
   """Returns address from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      street_addr = re.search(r'(Address 1\:)(.+)(?:Phone)*?', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      street_addr = ""
   try:
      zip_code = re.search(r'(Zip\: )(.+)', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      zip_code = ""
   try:
      city = re.search(r'(City\: )(.*)(State\: )(.*)', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      city = ""
   try:
      state = re.search(r'(?:City\: ).*(?:State\: ).*', str(text), re.MULTILINE).group(4).strip()
   except (IndexError, AttributeError):
      state = ""
   address = street_addr + " " + city + ", " + state + " " + zip_code
   if len(address) < 5:
      address = ""
   address = address.replace("00000-0000", "").replace("%", "").strip()
   address = re.sub(r'([A-Z]{1}[a-z]+)', '', address)
   return address


def getRace(text: str):
   """Return race from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   racesex = re.search(r'(B|W|H|A)\/(F|M)(?:Alias|XXX)', str(text))
   race = racesex.group(1).strip()
   return race


def getSex(text: str):
   """Return sex from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   racesex = re.search(r'(B|W|H|A)\/(F|M)(?:Alias|XXX)', str(text))
   sex = racesex.group(2).strip()
   return sex


def getNameAlias(text: str):
   """Return name from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   name = ""
   if bool(re.search(r'(?a)(VS\.|V\.{1})(.{5,1000})(Case)*', text, re.MULTILINE)):
      name = re.search(r'(?a)(VS\.|V\.{1})(.{5,1000})(Case)*', text, re.MULTILINE).group(2).replace("Case Number:",
                                                                             "").strip()
   else:
      if bool(re.search(r'(?:DOB)(.{5,1000})(?:Name)', text, re.MULTILINE)):
         name = re.search(r'(?:DOB)(.{5,1000})(?:Name)', text, re.MULTILINE).group(1).replace(":", "").replace(
            "Case Number:", "").strip()
   try:
      alias = re.search(r'(SSN)(.{5,75})(Alias)', text, re.MULTILINE).group(2).replace(":", "").replace("Alias 1",
                                                                                "").strip()
   except (IndexError, AttributeError):
      alias = ""
   if alias == "":
      return name
   else:
      return name + "\r" + alias


def getCaseInfo(text: str):
   """Returns case information from case text -> cases table
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   case_num = ""
   name = ""
   alias = ""
   race = ""
   sex = ""

   try:
      county: str = re.search(r'(?:County\: )(\d{2})(?:Case)', str(text)).group(1).strip()
      case_num: str = county + "-" + re.search(r'(\w{2}\-\d{4}-\d{6}.\d{2})', str(text)).group(1).strip()
   except (IndexError, AttributeError):
      pass

   if bool(re.search(r'(?a)(VS\.|V\.{1})(.{5,1000})(Case)*', text, re.MULTILINE)):
      name = re.search(r'(?a)(VS\.|V\.{1})(.{5,1000})(Case)*', text, re.MULTILINE).group(2).replace("Case Number:",
                                                                             "").strip()
   else:
      if bool(re.search(r'(?:DOB)(.{5,1000})(?:Name)', text, re.MULTILINE)):
         name = re.search(r'(?:DOB)(.{5,1000})(?:Name)', text, re.MULTILINE).group(1).replace(":", "").replace(
            "Case Number:", "").strip()
   try:
      alias = re.search(r'(SSN)(.{5,75})(Alias)', text, re.MULTILINE).group(2).replace(":", "").replace("Alias 1",
                                                                                "").strip()
   except (IndexError, AttributeError):
      pass
   else:
      pass
   try:
      dob: str = re.search(r'(\d{2}/\d{2}/\d{4})(?:.{0,5}DOB\:)', str(text), re.DOTALL).group(1)
      phone: str = re.search(r'(?:Phone\:)(.*?)(?:Country)', str(text), re.DOTALL).group(1).strip()
      phone = re.sub(r'[^0-9]', '', phone)
      if len(phone) < 7:
         phone = ""
      if len(phone) > 10 and phone[-3:] == "000":
         phone = phone[0:9]
   except (IndexError, AttributeError):
      dob = ""
      phone = ""
   try:
      racesex = re.search(r'(B|W|H|A)\/(F|M)(?:Alias|XXX)', str(text))
      race = racesex.group(1).strip()
      sex = racesex.group(2).strip()
   except (IndexError, AttributeError):
      pass
   try:
      street_addr = re.search(r'(Address 1\:)(.+)(?:Phone)*?', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      street_addr = ""
   try:
      zip_code = re.search(r'(Zip\: )(.+)', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      zip_code = ""
   try:
      city = re.search(r'(City\: )(.*)(State\: )(.*)', str(text), re.MULTILINE).group(2).strip()
   except (IndexError, AttributeError):
      city = ""
   try:
      state = re.search(r'(?:City\: ).*(?:State\: ).*', str(text), re.MULTILINE).group(4).strip()
   except (IndexError, AttributeError):
      state = ""

   address = street_addr + " " + city + ", " + state + " " + zip_code
   if len(address) < 5:
      address = ""
   address = address.replace("00000-0000", "").replace("%", "").strip()
   address = re.sub(r'([A-Z]{1}[a-z]+)', '', address)
   case = [case_num, name, alias, dob, race, sex, address, phone]
   return case


def getPhone(text: str):
   """Return phone number from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      phone: str = re.search(r'(?:Phone\:)(.*?)(?:Country)', str(text), re.DOTALL).group(1).strip()
      phone = re.sub(r'[^0-9]', '', phone)
      if len(phone) < 7:
         phone = ""
      if len(phone) > 10 and phone[-3:] == "000":
         phone = phone[0:9]
   except (IndexError, AttributeError):
      phone = ""
   return phone


def getFeeSheet(text: str):
   """
   Return fee sheet and fee summary outputs from case text
   List: [tdue, tbal, d999, owe_codes, codes, allrowstr, feesheet]
   feesheet = feesheet[['CaseNumber', 'FeeStatus', 'AdminFee', 'Total', 'Code', 'Payor', 'AmtDue', 'AmtPaid', 'Balance', 'AmtHold']]
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   actives = re.findall(r'(ACTIVE.*\$.*)', str(text))
   if len(actives) == 0:
      return [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
   else:
      try:
         trowraw = re.findall(r'(Total.*\$.*)', str(text), re.MULTILINE)[0]
         totalrow = re.sub(r'[^0-9|\.|\s|\$]', "", trowraw)
         if len(totalrow.split("$")[-1]) > 5:
            totalrow = totalrow.split(" . ")[0]
         tbal = totalrow.split("$")[3].strip().replace("$", "").replace(",", "").replace(" ", "")
         tdue = totalrow.split("$")[1].strip().replace("$", "").replace(",", "").replace(" ", "")
         tpaid = totalrow.split("$")[2].strip().replace("$", "").replace(",", "").replace(" ", "")
         thold = totalrow.split("$")[4].strip().replace("$", "").replace(",", "").replace(" ", "")
      except IndexError:
         totalrow = ""
         tbal = ""
         tdue = ""
         tpaid = ""
         thold = ""
      fees = pd.Series(actives, dtype=str)
      fees_noalpha = fees.map(lambda x: re.sub(r'[^0-9|\.|\s|\$]', "", x))
      srows = fees.map(lambda x: x.strip().split(" "))
      drows = fees_noalpha.map(lambda x: x.replace(",", "").split("$"))
      coderows = srows.map(lambda x: str(x[5]).strip() if len(x) > 5 else "")
      payorrows = srows.map(lambda x: str(x[6]).strip() if len(x) > 6 else "")
      amtduerows = drows.map(lambda x: str(x[1]).strip() if len(x) > 1 else "")
      amtpaidrows = drows.map(lambda x: str(x[2]).strip() if len(x) > 2 else "")
      balancerows = drows.map(lambda x: str(x[-1]).strip() if len(x) > 5 else "")
      amtholdrows = drows.map(lambda x: str(x[3]).strip() if len(x) > 5 else "")
      amtholdrows = amtholdrows.map(lambda x: x.split(" ")[0].strip() if " " in x else x)
      adminfeerows = fees.map(lambda x: x.strip()[7].strip() if 'N' else '')

      feesheet = pd.DataFrame({'CaseNumber': getCaseNumber(text), 'Total': '', 'FeeStatus': 'ACTIVE', 'AdminFee': adminfeerows.tolist(), 'Code': coderows.tolist(), 'Payor': payorrows.tolist(), 'AmtDue': amtduerows.tolist(), 'AmtPaid': amtpaidrows.tolist(), 'Balance': balancerows.tolist(), 'AmtHold': amtholdrows.tolist() })
      totalrdf = pd.DataFrame({'CaseNumber': getCaseNumber(text), 'Total': 'TOTAL', 'FeeStatus': '', 'AdminFee': '', 'Code': '', 'Payor': '', 'AmtDue': tdue, 'AmtPaid': tpaid, 'Balance': tbal, 'AmtHold': thold },index=[0])

      feesheet = feesheet.dropna()
      feesheet = pd.concat([feesheet, totalrdf], axis = 0, ignore_index=True)
      feesheet['Code'] = feesheet['Code'].astype("category")
      feesheet['Payor'] = feesheet['Payor'].astype("category")

      try:
         d999 = feesheet[feesheet['Code'] == 'D999']['Balance']
      except (TypeError, IndexError):
         d999 = ""

      owe_codes = " ".join(feesheet['Code'][feesheet.Balance.str.len() > 0])
      codes = " ".join(feesheet['Code'])
      allrows = actives
      allrows.append(totalrow)
      allrowstr = "\n".join(allrows)

      feesheet = feesheet[
         ['CaseNumber', 'FeeStatus', 'AdminFee', 'Total', 'Code', 'Payor', 'AmtDue', 'AmtPaid', 'Balance',
          'AmtHold']]

      return [tdue, tbal, d999, owe_codes, codes, allrowstr, feesheet]


def getFeeCodes(text: str):
   """Return fee codes from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   return getFeeSheet(text)[4]


def getFeeCodesOwed(text: str):
   """Return fee codes with positive balance owed from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   return getFeeSheet(text)[3]


def getTotals(text: str):
   """Return totals from case text -> List: [totalrow,tdue,tpaid,tdue,thold]
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      trowraw = re.findall(r'(Total.*\$.*)', str(text), re.MULTILINE)[0]
      totalrow = re.sub(r'[^0-9|\.|\s|\$]', "", trowraw)
      if len(totalrow.split("$")[-1]) > 5:
         totalrow = totalrow.split(" . ")[0]
      tbal = totalrow.split("$")[3].strip().replace("$", "").replace(",", "").replace(" ", "")
      tdue = totalrow.split("$")[1].strip().replace("$", "").replace(",", "").replace(" ", "")
      tpaid = totalrow.split("$")[2].strip().replace("$", "").replace(",", "").replace(" ", "")
      thold = totalrow.split("$")[4].strip().replace("$", "").replace(",", "").replace(" ", "")
      try:
         tdue = pd.to_numeric(tdue, 'coerce')
         tpaid = pd.to_numeric(tpaid, 'coerce')
         thold = pd.to_numeric(thold, 'coerce')
      except:
         pass
   except IndexError:
      totalrow = 0
      tdue = 0
      tpaid = 0
      thold = 0
   return [totalrow, tdue, tpaid, tdue, thold]


def getTotalBalance(text: str):
   """Return total balance from case text
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   try:
      trowraw = re.findall(r'(Total.*\$.*)', str(text), re.MULTILINE)[0]
      totalrow = re.sub(r'[^0-9|\.|\s|\$]', "", trowraw)
      if len(totalrow.split("$")[-1]) > 5:
         totalrow = totalrow.split(" . ")[0]
      tbal = totalrow.split("$")[3].strip().replace("$", "").replace(",", "").replace(" ", "")
   except:
      tbal = ""
   return str(tbal)


def getPaymentToRestore(text: str):
   """
   Return (total balance - total d999) from case text -> str
   Does not mask misc balances!
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   totalrow = "".join(re.findall(r'(Total.*\$.+\$.+\$.+)', str(text), re.MULTILINE)) if bool(
      re.search(r'(Total.*\$.*)', str(text), re.MULTILINE)) else "0"
   try:
      tbalance = totalrow.split("$")[3].strip().replace("$", "").replace(",", "").replace(" ", "").strip()
      try:
         tbal = pd.Series([tbalance]).astype(float)
      except ValueError:
         tbal = 0.0
   except (IndexError, TypeError):
      tbal = 0.0
   try:
      d999raw = re.search(r'(ACTIVE.*?D999\$.*)', str(text), re.MULTILINE).group() if bool(
         re.search(r'(ACTIVE.*?D999\$.*)', str(text), re.MULTILINE)) else "0"
      d999 = pd.Series([d999raw]).astype(float)
   except (IndexError, TypeError):
      d999 = 0.0
   t_out = pd.Series(tbal - d999).astype(float).values[0]
   return str(t_out)


def getBalanceByCode(text: str, code: str):
   """
   Return balance by code from case text -> str
   
   Args:
      text (str): Description
      code (str): Description
   
   Returns:
      TYPE: Description
   """
   actives = re.findall(r'(ACTIVE.*\$.*)', str(text))
   fees = pd.Series(actives, dtype=str)
   fees_noalpha = fees.map(lambda x: re.sub(r'[^0-9|\.|\s|\$]', "", x))
   srows = fees.map(lambda x: x.strip().split(" "))
   drows = fees_noalpha.map(lambda x: x.replace(",", "").split("$"))
   coderows = srows.map(lambda x: str(x[5]).strip() if len(x) > 5 else "")
   balancerows = drows.map(lambda x: str(x[-1]).strip() if len(x) > 5 else "")
   codemap = pd.DataFrame({
      'Code': coderows,
      'Balance': balancerows
   })
   matches = codemap[codemap.Code == code].Balance
   return str(matches.sum())


def getAmtDueByCode(text: str, code: str):
   """
   Return total amt due from case text -> str
   
   Args:
      text (str): Description
      code (str): Description
   
   Returns:
      TYPE: Description
   """
   actives = re.findall(r'(ACTIVE.*\$.*)', str(text))
   fees = pd.Series(actives, dtype=str)
   fees_noalpha = fees.map(lambda x: re.sub(r'[^0-9|\.|\s|\$]', "", x))
   srows = fees.map(lambda x: x.strip().split(" "))
   drows = fees_noalpha.map(lambda x: x.replace(",", "").split("$"))
   coderows = srows.map(lambda x: str(x[5]).strip() if len(x) > 5 else "")
   payorrows = srows.map(lambda x: str(x[6]).strip() if len(x) > 6 else "")
   amtduerows = drows.map(lambda x: str(x[1]).strip() if len(x) > 1 else "")

   codemap = pd.DataFrame({
      'Code': coderows,
      'Payor': payorrows,
      'AmtDue': amtduerows
   })

   codemap.AmtDue = codemap.AmtDue.map(lambda x: pd.to_numeric(x, 'coerce'))

   due = codemap.AmtDue[codemap.Code == code]
   return str(due)


def getAmtPaidByCode(text: str, code: str):
   """
   Return total amt paid from case text -> str
   
   Args:
      text (str): Description
      code (str): Description
   
   Returns:
      TYPE: Description
   """
   actives = re.findall(r'(ACTIVE.*\$.*)', str(text))
   fees = pd.Series(actives, dtype=str)
   fees_noalpha = fees.map(lambda x: re.sub(r'[^0-9|\.|\s|\$]', "", x))
   srows = fees.map(lambda x: x.strip().split(" "))
   drows = fees_noalpha.map(lambda x: x.replace(",", "").split("$"))
   coderows = srows.map(lambda x: str(x[5]).strip() if len(x) > 5 else "")
   payorrows = srows.map(lambda x: str(x[6]).strip() if len(x) > 6 else "")
   amtpaidrows = drows.map(lambda x: str(x[2]).strip() if len(x) > 2 else "")

   codemap = pd.DataFrame({
      'Code': coderows,
      'Payor': payorrows,
      'AmtPaid': amtpaidrows
   })

   codemap.AmtPaid = codemap.AmtPaid.map(lambda x: pd.to_numeric(x, 'coerce'))

   paid = codemap.AmtPaid[codemap.Code == code]
   return str(paid)


def getCharges(text: str):
   """
   Returns charges summary from case text -> List: [convictions, dcharges, fcharges, cerv_convictions, pardon_convictions, perm_convictions, conviction_ct, charge_ct, cerv_ct, pardon_ct, perm_ct, conv_cerv_ct, conv_pardon_ct, conv_perm_ct, charge_codes, conv_codes, allcharge, charges]
   
   Args:
      text (str): Description
   
   Returns:
      TYPE: Description
   """
   cnum = getCaseNumber(text)
   rc = re.findall(r'(\d{3}\s{1}.{1,1000}?.{3}-.{3}-.{3}.{10,75})', text, re.MULTILINE)
   unclean = pd.DataFrame({'Raw': rc})
   unclean['FailTimeTest'] = unclean['Raw'].map(lambda x: bool(re.search(r'([0-9]{1}\:{1}[0-9]{2})', x)))
   unclean['FailNumTest'] = unclean['Raw'].map(
      lambda x: False if bool(re.search(r'([0-9]{3}\s{1}.{4}\s{1})', x)) else True)
   unclean['Fail'] = unclean.index.map(
      lambda x: unclean['FailTimeTest'][x] == True or unclean['FailNumTest'][x] == True)
   passed = pd.Series(unclean[unclean['Fail'] == False]['Raw'].dropna().explode().tolist())
   passed = passed.explode()
   passed = passed.dropna()
   passed = pd.Series(passed.tolist())
   passed = passed.map(lambda x: re.sub(r'(\s+[0-1]{1}$)', '', x))
   passed = passed.map(lambda x: re.sub(r'([©|\w]{1}[a-z]+)', '', x))
   passed = passed.map(lambda x: re.sub(r'(0\.00.+$)', '', x))
   passed = passed.map(lambda x: re.sub(r'(\#)', '', x))
   passed = passed.explode()
   c = passed.dropna().tolist()
   cind = range(0, len(c))
   charges = pd.DataFrame({'Charges': c, 'parentheses': '', 'decimals': ''}, index=cind)
   charges['Charges'] = charges['Charges'].map(lambda x: re.sub(r'(\$|\:|©.+)', '', x, re.MULTILINE))
   charges['Charges'] = charges['Charges'].map(lambda x: re.sub(r'(\s0\.\s+$)', '', x, re.MULTILINE))
   charges['CaseNumber'] = charges.index.map(lambda x: cnum)
   split_charges = charges['Charges'].map(lambda x: x.split(" "))
   charges['Num'] = split_charges.map(lambda x: x[0].strip())
   charges['Code'] = split_charges.map(lambda x: x[1].strip()[0:4])
   charges['Felony'] = charges['Charges'].map(lambda x: True if bool(re.search(r'FELONY', x)) else False)
   charges['Conviction'] = charges['Charges'].map(lambda x: True if bool(re.search(r'GUILTY|CONVICTED', x)) else False)
   charges['VRRexception'] = charges['Charges'].map(lambda x: True if bool(re.search(r'(A ATT|ATTEMPT|S SOLICIT|CONSP)', x)) else False)
   charges['CERVCode'] = charges['Code'].map(lambda x: True if bool(re.search(
      r'(OSUA|EGUA|MAN1|MAN2|MANS|ASS1|ASS2|KID1|KID2|HUT1|HUT2|BUR1|BUR2|TOP1|TOP2|TPCS|TPCD|TPC1|TET2|TOD2|ROB1|ROB2|ROB3|FOR1|FOR2|FR2D|MIOB|TRAK|TRAG|VDRU|VDRY|TRAO|TRFT|TRMA|TROP|CHAB|WABC|ACHA|ACAL)',
      x)) else False)
   charges['PardonCode'] = charges['Code'].map(lambda x: True if bool(re.search(
      r'(RAP1|RAP2|SOD1|SOD2|STSA|SXA1|SXA2|ECHI|SX12|CSSC|FTCS|MURD|MRDI|MURR|FMUR|PMIO|POBM|MIPR|POMA|INCE)', x)) else False)
   charges['PermanentCode'] = charges['Code'].map(lambda x: True if bool(re.search(r'(CM\d\d|CMUR)', x)) else False)
   charges['CERV'] = charges.index.map(
      lambda x: charges['CERVCode'][x] == True and charges['VRRexception'][x] == False and charges['Felony'][
         x] == True)
   charges['Pardon'] = charges.index.map(
      lambda x: charges['PardonCode'][x] == True and charges['VRRexception'][x] == False and charges['Felony'][
         x] == True)
   charges['Permanent'] = charges.index.map(
      lambda x: charges['PermanentCode'][x] == True and charges['VRRexception'][x] == False and charges['Felony'][
         x] == True)
   charges['Disposition'] = charges['Charges'].map(lambda x: True if bool(re.search(r'\d{2}/\d{2}/\d{4}', x)) else False)
   charges['CourtActionDate'] = charges['Charges'].map(
      lambda x: re.search(r'(\d{2}/\d{2}/\d{4})', x).group() if bool(re.search(r'(\d{2}/\d{2}/\d{4})', x)) else "")
   charges['CourtAction'] = charges['Charges'].map(lambda x: re.search(
      r'(BOUND|GUILTY PLEA|WAIVED|DISMISSED|TIME LAPSED|NOL PROSS|CONVICTED|INDICTED|DISMISSED|FORFEITURE|TRANSFER|REMANDED|ACQUITTED|WITHDRAWN|PETITION|PRETRIAL|COND\. FORF\.)',
      x).group() if bool(re.search(
      r'(BOUND|GUILTY PLEA|WAIVED|DISMISSED|TIME LAPSED|NOL PROSS|CONVICTED|INDICTED|DISMISSED|FORFEITURE|TRANSFER|REMANDED|ACQUITTED|WITHDRAWN|PETITION|PRETRIAL|COND\. FORF\.)',
      x)) else "")
   try:
      charges['Cite'] = charges['Charges'].map(
         lambda x: re.search(r'([^a-z]{1,2}?.{1}-[^\s]{3}-[^\s]{3})', x).group())
   except (AttributeError, IndexError):
      pass
      try:
         charges['Cite'] = charges['Charges'].map(
            lambda x: re.search(r'([0-9]{1,2}.{1}-.{3}-.{3})', x).group())  # TEST
      except (AttributeError, IndexError):
         charges['Cite'] = ""
   charges['Cite'] = charges['Cite'].astype(str)
   try:
      charges['decimals'] = charges['Charges'].map(lambda x: re.search(r'(\.[0-9])', x).group())
      charges['Cite'] = charges['Cite'] + charges['decimals']
   except (AttributeError, IndexError):
      charges['Cite'] = charges['Cite']
   try:
      charges['parentheses'] = charges['Charges'].map(lambda x: re.search(r'(\([A-Z]\))', x).group())
      charges['Cite'] = charges['Cite'] + charges['parentheses']
      charges['Cite'] = charges['Cite'].map(
         lambda x: x[1:-1] if bool(x[0] == "R" or x[0] == "Y" or x[0] == "C") else x)
   except (AttributeError, IndexError):
      pass
   charges['TypeDescription'] = charges['Charges'].map(
      lambda x: re.search(r'(BOND|FELONY|MISDEMEANOR|OTHER|TRAFFIC|VIOLATION)', x).group() if bool(
         re.search(r'(BOND|FELONY|MISDEMEANOR|OTHER|TRAFFIC|VIOLATION)', x)) else "")
   charges['Category'] = charges['Charges'].map(lambda x: re.search(
      r'(ALCOHOL|BOND|CONSERVATION|DOCKET|DRUG|GOVERNMENT|HEALTH|MUNICIPAL|OTHER|PERSONAL|PROPERTY|SEX|TRAFFIC)',
      x).group() if bool(re.search(
      r'(ALCOHOL|BOND|CONSERVATION|DOCKET|DRUG|GOVERNMENT|HEALTH|MUNICIPAL|OTHER|PERSONAL|PROPERTY|SEX|TRAFFIC)',
      x)) else "")
   charges['Charges'] = charges['Charges'].map(
      lambda x: x.replace("SentencesSentence", "").replace("Sentence", "").strip())
   charges.drop(columns=['PardonCode', 'PermanentCode', 'CERVCode', 'VRRexception', 'parentheses', 'decimals'],
             inplace=True)
   ch_Series = charges['Charges']
   noNumCode = ch_Series.str.slice(8)
   noNumCode = noNumCode.str.strip()
   noDatesEither = noNumCode.str.replace("\d{2}/\d{2}/\d{4}", '', regex=True)
   noWeirdColons = noDatesEither.str.replace("\:.+", "", regex=True)
   descSplit = noWeirdColons.str.split(".{3}-.{3}-.{3}", regex=True)
   descOne = descSplit.map(lambda x: x[0])
   descTwo = descSplit.map(lambda x: x[1])

   descs = pd.DataFrame({
      'One': descOne,
      'Two': descTwo
   })

   descs['TestOne'] = descs['One'].str.replace("TRAFFIC", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("FELONY", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("PROPERTY", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("MISDEMEANOR", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("PERSONAL", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("FELONY", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("DRUG", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("GUILTY PLEA", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("DISMISSED", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("NOL PROSS", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("CONVICTED", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.replace("WAIVED TO GJ", "").astype(str)
   descs['TestOne'] = descs['TestOne'].str.strip()

   descs['TestTwo'] = descs['Two'].str.replace("TRAFFIC", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("FELONY", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("PROPERTY", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("MISDEMEANOR", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("PERSONAL", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("FELONY", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.replace("DRUG", "").astype(str)
   descs['TestTwo'] = descs['TestTwo'].str.strip()

   descs['Winner'] = descs['TestOne'].str.len() - descs['TestTwo'].str.len()

   descs['DoneWon'] = descs['One'].astype(str)
   descs['DoneWon'][descs['Winner'] < 0] = descs['Two'][descs['Winner'] < 0]
   descs['DoneWon'] = descs['DoneWon'].str.replace("(©.*)", "", regex=True)
   descs['DoneWon'] = descs['DoneWon'].str.replace(":", "")
   descs['DoneWon'] = descs['DoneWon'].str.strip()

   charges['Description'] = descs['DoneWon']

   charges['Category'] = charges['Category'].astype("category")
   charges['TypeDescription'] = charges['TypeDescription'].astype("category")
   charges['Code'] = charges['Code'].astype("category")
   charges['CourtAction'] = charges['CourtAction'].astype("category")


   # counts
   conviction_ct = charges[charges.Conviction == True].shape[0]
   charge_ct = charges.shape[0]
   cerv_ct = charges[charges.CERV == True].shape[0]
   pardon_ct = charges[charges.Pardon == True].shape[0]
   perm_ct = charges[charges.Permanent == True].shape[0]
   conv_cerv_ct = charges[charges.CERV == True][charges.Conviction == True].shape[0]
   conv_pardon_ct = charges[charges.Pardon == True][charges.Conviction == True].shape[0]
   conv_perm_ct = charges[charges.Permanent == True][charges.Conviction == True].shape[0]

   # summary strings
   convictions = "; ".join(charges[charges.Conviction == True]['Charges'].tolist())
   conv_codes = " ".join(charges[charges.Conviction == True]['Code'].tolist())
   charge_codes = " ".join(charges[charges.Disposition == True]['Code'].tolist())
   dcharges = "; ".join(charges[charges.Disposition == True]['Charges'].tolist())
   fcharges = "; ".join(charges[charges.Disposition == False]['Charges'].tolist())
   cerv_convictions = "; ".join(charges[charges.CERV == True][charges.Conviction == True]['Charges'].tolist())
   pardon_convictions = "; ".join(charges[charges.Pardon == True][charges.Conviction == True]['Charges'].tolist())
   perm_convictions = "; ".join(charges[charges.Permanent == True][charges.Conviction == True]['Charges'].tolist())

   allcharge = "; ".join(charges['Charges'])
   if charges.shape[0] == 0:
      charges = np.nan

   return [convictions, dcharges, fcharges, cerv_convictions, pardon_convictions, perm_convictions, conviction_ct,
         charge_ct, cerv_ct, pardon_ct, perm_ct, conv_cerv_ct, conv_pardon_ct, conv_perm_ct, charge_codes,
         conv_codes, allcharge, charges]

def getCaseYear(text):
   """
   Return case year 
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   cnum = getCaseNumber(text)
   return float(cnum[6:10])

def getCounty(text):
   """
   Return county
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   cnum = getCaseNumber(text)
   return int(cnum[0:2])

def getLastName(text):
   """
   Return last name
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   name = getName(text)
   return name.split(" ")[0].strip()

def getFirstName(text):
   """
   Return first name
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   name = getName(text)
   if len(name.split(" ")) > 1:
      return name.split(" ")[1].strip()
   else:
      return name

def getMiddleName(text):
   """
   Return middle name or initial
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   name = getName(text)
   if len(name.split(" ")) > 2:
      return name.split(" ")[2].strip()
   else:
      return ""

def getConvictions(text):
   """
   Return convictions as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[0]

def getDispositionCharges(text):
   """
   Return disposition charges as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[1]

def getFilingCharges(text):
   """
   Return filing charges as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[2]

def getCERVConvictions(text):
   """
   Return CERV convictions as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[3]

def getPardonDQConvictions(text):
   """
   Return pardon-to-vote charges as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[4]

def getPermanentDQConvictions(text):
   """
   Return permanent no vote charges as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[5]

def getConvictionCount(text):
   """
   Return convictions count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[6]

def getChargeCount(text):
   """
   Return charges count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[7]

def getCERVChargeCount(text):
   """
   Return CERV charges count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[8]

def getPardonDQCount(text):
   """
   Return pardon-to-vote charges count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[9]

def getPermanentDQChargeCount(text):
   """
   Return permanent no vote charges count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[10]

def getCERVConvictionCount(text):
   """
   Return CERV convictions count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[11]

def getPardonDQConvictionCount(text):
   """
   Return pardon-to-vote convictions count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[12]

def getPermanentDQConvictionCount(text):
   """
   Return permanent no vote convictions count from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[13]

def getChargeCodes(text):
   """
   Return charge codes as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[14]

def getConvictionCodes(text):
   """
   Return convictions codes as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[15]

def getChargesString(text):
   """
   Return charges as string from case text
   
   Args:
      text (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   return getCharges(text)[16]


def getRawCharges(text):
   b = re.findall(r'(\d{3}\s{1}[A-Z0-9]{4}.{1,200}?.{3}-.{3}-.{3}.{10,75})', text, re.MULTILINE)
   b = [re.sub(r'[A-Z][a-z][a-z\s]+.+','',x) for x in b]
   b = [re.sub(r'\:\s\:.+','',x) for x in b]
   b = [re.sub(r'^\d{3}\n.+','',x) for x in b]
   b = ['' if re.search(r'\d{1,2}\:{1,2}\s\D{2}',x) else x for x in b]
   return b



# LOGS

def echo_conf(input_path, make, output_path, overwrite, no_write, dedupe, no_prompt, compress):
   """
   Logs configuration details to console
   
   Args:
      input_path (TYPE): Description
      make (TYPE): Description
      output_path (TYPE): Description
      overwrite (TYPE): Description
      no_write (TYPE): Description
      dedupe (TYPE): Description
      no_prompt (TYPE): Description
      compress (TYPE): Description
   
   Returns:
      TYPE: Description
   """
   d = click.style("* Successfully configured!\n", fg='bright_green')
   f = click.style(
      f"""{"ARCHIVE is enabled. Alacorder will write full text case archive to output path instead of data tables. " if make == "archive" else ''}{"NO-WRITE is enabled. Alacorder will NOT export outputs. " if no_write else ''}{"OVERWRITE is enabled. Alacorder will overwrite existing files at output path! " if overwrite else ''}{"REMOVE DUPLICATES is enabled. At time of export, all duplicate cases will be removed from output. " if dedupe and make == "archive" else ''}{"NO_PROMPT is enabled. All user confirmation prompts will be suppressed as if set to default by user." if no_prompt else ''}{"COMPRESS is enabled. Alacorder will try to compress output file." if compress == True else ''}""".strip(),
      italic=True, fg='white')
   return d + f

upick_table = ('''
For compressed archive, enter:
   [A] Full text archive
To export a data table, enter:
   [B]  Case Details
   [C]  Fee Sheets
   [D]  Charges (all)
   [E]  Charges (disposition only)
   [F]  Charges (filing only)

Enter selection to continue. [A-F]
''')

upick_table_only = ('''
To export a data table, enter:
   [B]  Case Details
   [C]  Fee Sheets
   [D]  Charges (all)
   [E]  Charges (disposition only)
   [F]  Charges (filing only)

Enter selection to continue. [B-F]
''')

def pick_table():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(upick_table)

def pick_table_only():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(upick_table_only)

ujust_table = ('''
EXPORT DATA TABLE: To export data table from case inputs, enter full output path. Use .xls or .xlsx to export all tables, or, if using another format (.csv, .json, .dta), select a table after entering output file path.

Enter path.
''')

ujust_archive = ('''
EXPORT ARCHIVE: Compressed archives can store thousands of cases' data using a fraction of the original PDF storage. To export full text archive, enter full output path. Supported file extensions are archive.pkl.xz, archive.json.zip, archive.csv.zip, and archive.parquet.

Enter path.
''')


def just_table():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(ujust_table)

def just_archive():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(ujust_archive)

uboth = ('''

EXPORT FULL TEXT ARCHIVE: To process case inputs into a full text archive (recommended), enter archive path below with file extension .pkl.xz or .json.zip.

EXPORT DATA TABLE: To export data table from case inputs, enter full output path. Use .xls or .xlsx to export all tables, or, if using another format (.csv, .json, .dta), select a table after entering output file path.

Enter output path.
''')


def both():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(uboth)


utitle = click.style("\nALACORDER beta 77",bold=True,italic=True) + """

Alacorder retrieves case detail PDFs from Alacourt.com and processes them into text archives and data tables suitable for research purposes.

   ACCEPTED      /pdfs/path/   PDF directory           
   INPUTS:       .pkl.xz       Compressed pickle archive
              .json.zip     Compressed JSON archive
              .csv.zip      Compressed CSV archive
              .parquet      Apache Parquet


Enter input path.
"""


def title():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return utitle

usmalltitle = click.style("\nALACORDER beta 77",bold=True,italic=True) + """

Alacorder retrieves case detail PDFs from Alacourt.com and processes them into text archives and data tables suitable for research purposes.

"""

def smalltitle():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return title

utext_p = ('''

Enter path to output text file (must be .txt). 
''')


def text_p():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return click.style(utext_p, bold=True)


def title():
   """Summary
   
   Returns:
      TYPE: Description
   """
   return utitle

def complete(conf, *outputs):
   """
   Logs completion
   
   Args:
      conf (TYPE): Description
      *outputs: Description
   """

   elapsed = math.floor(time.time() - conf.TIME)
   if conf['LOG'] != False and conf['MAKE'] != "archive":
      click.secho(f"Task completed in {elapsed} seconds.", bold=True, fg='green')

def logdebug(msg, debug=False, *conf):
   """Summary
   
   Args:
      conf (TYPE): Description
      *msg: Description
   """
   if debug:
      click.secho(f"debug log {time.time()}")
      click.secho(msg)


def log(msg, fg="", bold=False, italic=False, *conf):
   if isinstance(conf, pd.core.series.Series):
      try:
         if conf['LOG']:
            click.secho(msg, fg=fg, bold=bold, italic=italic)
      except:
         pass
   else:
      click.secho(msg, fg=fg, bold=bold, italic=italic)
