import plac
from pathlib import Path
import csv
import re
import pandas as pd
from sqlalchemy import create_engine, inspect, event
import json

csv_dir = Path("C:\\GlobalCube\\System\\CARLO\\Export")
target_dsn = { 'user': "sa", 'pass': "Mffu3011#", 'server': "GC-SERVER1\\GLOBALCUBE", 'database': "GC" }
temp_schema = "temp"
target_schema = "carlo"

def get_dtype (db_type):
    if db_type == "DATETIME":
        return "datetime64"
    if db_type == "DECIMAL(28, 8)" or db_type == "DECIMAL(18, 0)" or db_type == "NUMERIC(18, 0)":
        return "float64"
    return "object"

def conn_string (dsn):
    return f"mssql+pyodbc://{dsn['user']}:{dsn['pass']}@{dsn['server']}/{dsn['database']}?driver=SQL+Server+Native+Client+11.0"

def conn_params (dsn):
    return f"-S {dsn['server']} -d {dsn['database']} -U {dsn['user']} -P {dsn['pass']}"

def columns_from_csv (source_csv, target_insp_cols):
    target_col_dict = dict([(col['name'], get_dtype(str(col['type']))) for col in target_insp_cols if str(col['type']) != "DATETIME"])
    target_dates = [col['name'] for col in target_insp_cols if str(col['type']) == "DATETIME"]    
    df = pd.read_csv(source_csv, sep=";", encoding="ansi", decimal=",", dtype=target_col_dict, parse_dates=target_dates, nrows=10)
    source_columns_list = list(df.columns)
    for i,col in enumerate([col for col in source_columns_list if col[-2:] == ".1"]):
        source_columns_list[i] = col[:-2] + "2"
    return source_columns_list

def transform_template(target_insp, source_csv, table, target_schema):
    target_insp_cols = target_insp.get_columns(table, schema=target_schema)
    target_columns_list = [col['name'] for col in target_insp_cols]
    source_columns_list = columns_from_csv(source_csv, target_insp_cols)

    target_columns = set(target_columns_list)    
    source_columns = set(source_columns_list)            
    #intersect = source_columns.intersection(target_columns)
    #print("Auf beiden Seiten: " + ";".join(intersect))
    diff1 = source_columns.difference(target_columns)
    if len(diff1) > 0:
        print("Nur in Quelle: " + ";".join(diff1))
    diff2 = target_columns.difference(source_columns)
    if len(diff2) > 0:
        print("Nur in Ziel:   " + ";".join(diff2))

    transform = []
    for i, col in enumerate(target_columns_list):
        if col in source_columns_list:
            pos = source_columns_list.index(col)
        else:
            pos = -1
        transform.append((pos, get_dtype(str(target_insp_cols[i]['type']))))
    return transform

def transform_line (line, transform):
    pattern = re.compile(r"\d{4}-\d\d-\d\d \d\d:\d\d")
    result = []
    for pos, f in transform:
        e = ""
        if pos > -1:
            e = line[pos]
        
        if f == "float64":
            e = e.replace(",", ".")
        if f == "datetime64":
            if not pattern.match(e):
                e = ""
            #e += ":00.000"
        result.append(e)
    return result

def transform_file(source_csv, transform):
    stage_csv = Path("\\".join(source_csv.parts[:-1] + list("stage") + source_csv.parts[-1:]))

    if stage_csv.exists() and stage_csv.stat()['st_ctime'] > source_csv.stat()['st_ctime']:
        print(f"Stage-CSV '{stage_csv.name}' ist bereits aktuell.")
        return False

    with csv.reader(open(source_csv, "r", encoding="ansi", newline=""), delimiter=";") as source_file:
        with csv.writer(open(stage_csv, "w", encoding="utf8", newline=""), delimiter="\t") as target_file:
            next(source_file) # ignore header
            for cols in source_file:
                target_file.writerow(transform_line(cols, transform))


def csv_tables (csv_dir, target_tables_ci):
    p = re.compile("_\d+$")
    result = []
    if not csv_dir.is_dir():
        print(f"Verzeichnis {csv_dir} existiert nicht!")
        return result   

    for source_csv in csv_dir.glob("*.csv"):
        if source_csv.is_dir():
            continue
        
        table = source_csv.name[:-4].lower()
        if not table in target_tables_ci:
            table = p.sub("", table)
            if not table in target_tables_ci:
                print(f"Ziel-Tabelle '{table}' existiert nicht!")
                continue
        result.append((table, source_csv))
    return result


def target_tables (target_dsn, target_schema):
    engine = create_engine(conn_string(target_dsn))
    target_insp = inspect(engine)
    target_tables = target_insp.get_table_names(schema=target_schema)
    return (target_insp, list(map(str.lower, target_tables)))


def batch (csv_dir, action):
    target_insp, target_tables_ci = target_tables(target_dsn, target_schema)

    for table in csv_tables(csv_dir, target_tables_ci):
        source_csv = f"{csv_dir}\\{table}.csv"
        print(f"=={table}==")
        stage_csv = f"{csv_dir}\\stage\\{table}.csv"
        print(f"sqlcmd.exe {conn_params(target_dsn)} -p -Q \"TRUNCATE TABLE [{target_schema}].[{table}]\" ")
        tf_template = transform_template(target_insp, source_csv, table, target_schema)
        print(f"%PYTHON%\\python.exe csv_import.py transform \"{source_csv}\" \"{json.dumps(tf_template)}\" ")
        print(f"bcp.exe [{target_schema}].[{table}] in \"{stage_csv}\" {conn_params(target_dsn)} -c -C 65001 -e \"{stage_csv}.log\" ")
        print("")


@plac.pos('csv_dir', "", type=Path)
@plac.opt('action', "", choices=['overwrite', 'update', 'transform'])
@plac.opt('template', "")
def main(csv_dir, action="overwrite", template="[]"):
    if action == "transform":
        transform_file(csv_dir, template)
    else:
        batch(csv_dir, action)


if __name__ == '__main__':
    #plac.call(main)
    main(csv_dir)