"""
The MIT License (MIT)

Copyright (c) 2015 seahboonsiew

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import csv
import sys
import dateutil.parser
from pyspark.sql.types import (StringType, DoubleType, TimestampType, NullType,
                               IntegerType, StructType, StructField)

py_version = sys.version_info[0]


def csvToDataFrame(sqlCtx, rdd, columns=None, sep=",", parseDate=True):
    """Converts CSV plain text RDD into SparkSQL DataFrame (former SchemaRDD)
    using PySpark. If columns not given, assumes first row is the header.
    If separator not given, assumes comma separated
    """
    if py_version < 3:
        def toRow(line):
            return toRowSep(line.encode('utf-8'), sep)
    else:
        def toRow(line):
            return toRowSep(line, sep)

    rdd_array = rdd.map(toRow)
    rdd_sql = rdd_array

    if columns is None:
        columns = rdd_array.first()
        rdd_sql = rdd_array.zipWithIndex().filter(
            lambda r_i: r_i[1] > 0).keys()
    column_types = evaluateType(rdd_sql, parseDate)

    def toSqlRow(row):
        return toSqlRowWithType(row, column_types)

    schema = makeSchema(zip(columns, column_types))

    return sqlCtx.createDataFrame(rdd_sql.map(toSqlRow), schema=schema)


def makeSchema(columns):
    struct_field_map = {'string': StringType(),
                        'date': TimestampType(),
                        'double': DoubleType(),
                        'int': IntegerType(),
                        'none': NullType()}
    fields = [StructField(k, struct_field_map[v], True) for k, v in columns]

    return StructType(fields)


def toRowSep(line, d):
    """Parses one row using csv reader"""
    for r in csv.reader([line], delimiter=d):
        return r


def toSqlRowWithType(row, col_types):
    """Convert to sql.Row"""
    d = row
    for col, data in enumerate(row):
        typed = col_types[col]
        if isNone(data):
            d[col] = None
        elif typed == 'string':
            d[col] = data
        elif typed == 'int':
            d[col] = int(round(float(data)))
        elif typed == 'double':
            d[col] = float(data)
        elif typed == 'date':
            d[col] = toDate(data)
    return d


# Type converter
def isNone(d):
    return (d is None or d == 'None' or
            d == '?' or
            d == '' or
            d == 'NULL' or
            d == 'null')


def toDate(d):
    return dateutil.parser.parse(d)


def getRowType(row):
    """Infers types for each row"""
    d = row
    for col, data in enumerate(row):
        try:
            if isNone(data):
                d[col] = 'none'
            else:
                num = float(data)
                if num.is_integer():
                    d[col] = 'int'
                else:
                    d[col] = 'double'
        except:
            try:
                toDate(data)
                d[col] = 'date'
            except:
                d[col] = 'string'
    return d


def getRowTypeNoDate(row):
    """Infers types for each row"""
    d = row
    for col, data in enumerate(row):
        try:
            if isNone(data):
                d[col] = 'none'
            else:
                num = float(data)
                if num.is_integer():
                    d[col] = 'int'
                else:
                    d[col] = 'double'
        except:
            d[col] = 'string'
    return d


def reduceTypes(a, b):
    """Reduces column types among rows to find common denominator"""
    type_order = {'string': 0, 'date': 1, 'double': 2, 'int': 3, 'none': 4}
    reduce_map = {'int': {0: 'string', 1: 'string', 2: 'double'},
                  'double': {0: 'string', 1: 'string'},
                  'date': {0: 'string'}}
    d = a
    for col, a_type in enumerate(a):
        # a_type = a[col]
        b_type = b[col]
        if a_type == 'none':
            d[col] = b_type
        elif b_type == 'none':
            d[col] = a_type
        else:
            order_a = type_order[a_type]
            order_b = type_order[b_type]
            if order_a == order_b:
                d[col] = a_type
            elif order_a > order_b:
                d[col] = reduce_map[a_type][order_b]
            elif order_a < order_b:
                d[col] = reduce_map[b_type][order_a]
    return d


def evaluateType(rdd_sql, parseDate):
    if parseDate:
        return rdd_sql.map(getRowType).reduce(reduceTypes)
    else:
        return rdd_sql.map(getRowTypeNoDate).reduce(reduceTypes)