# -*- coding: utf-8 -*-

__author__ = 'chunk'

from ..common import *

from .dependencies import *
from . import *

# from ..mdata import MSR, CV, ILSVRC, ILSVRC_S

from ..mjpeg import *
from ..msteg import *
from ..msteg.steganography import LSB, F3, F4, F5

from ..mfeat import IntraBlockDiff

import sys

from pyspark import SparkConf, SparkContext

from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD

from pyspark.mllib.regression import LabeledPoint
from numpy import array
import json
import pickle

import tempfile

import numpy as np
from scipy import stats
from hashlib import md5

np.random.seed(sum(map(ord, "whoami")))
package_dir = os.path.dirname(os.path.abspath(__file__))

def rddparse_data_CV(raw_row):

    """

    input: (u'key0',u'cf_feat:hog:[0.056273,...]--%--cf_pic:data:\ufffd\ufffd\...--%--cf_tag:hog:True')

    return: ([0.056273,...],1)

    """
    data = raw_row[1].split('--%--')
    feat = json.loads(data[0].split(':')[-1])
    tag = 1 if data[-1].split(':')[-1] == 'True' else 0
    return (feat, tag)


def rddparse_data_ILS(raw_row):
    """
    input: (u'key0',u'cf_feat:hog:[0.056273,...]--%--cf_pic:data:\ufffd\ufffd\...--%--cf_tag:hog:True')

    return: ([0.056273,...],1)

    In fact we can also use mapValues.
    """
    key = raw_row[0]
    # if key == '04650c488a2b163ca8a1f52da6022f03.jpg':
    # with open('/tmp/hhhh','wb') as f:

    # f.write(raw_row[1].decode('unicode-escape')).encode('latin-1')

    items = raw_row[1].decode('unicode-escape').encode('latin-1').split('--%--')
    data = items[0].split('cf_pic:data:')[-1]
    return (key, data)

def rddparse_all_ILS(raw_row):
    key = raw_row[0]
    items = raw_row[1].decode('unicode-escape').encode('latin-1').split('--%--')
    data = [items[0].split('cf_pic:data:')[-1]] + [json.loads(item.split(':')[-1]) for item in items[1:]]

    return (key, data)

def rddinfo_ILS(img, info_rate=None, tag_chosen=None, tag_class=None):

    """

    Tempfile is our friend. (?)
    """
    info_rate = info_rate if info_rate != None else 0.0

    tag_chosen = tag_chosen if tag_chosen != None else stats.bernoulli.rvs(0.8)
    tag_class = tag_class if tag_class != None else 0

    try:
        tmpf = tempfile.NamedTemporaryFile(suffix='.jpg', mode='w+b')

        tmpf.write(img)

        tmpf.seek(0)
        im = Jpeg(tmpf.name, key=sample_key)
        info = [

            im.image_width,
            im.image_height,
            im.image_width * im.image_height,
            im.getCapacity(),
            im.getQuality(),
            info_rate,
            tag_chosen,

            tag_class
        ]

        return info
    except Exception as e:
        print e
        raise

    finally:

        tmpf.close()


def rddembed_ILS(row):
    """
    input:
        e.g. row =('row1',[1,3400,'hello'])

    return:

        newrow = ('row2',[34,5400,'embeded'])
    """
    items = row[1]

    capacity, rate, chosen = int(items[4]), float(items[6]), int(items[7])
    if chosen == 0:
        return None
    try:
        tmpf_src = tempfile.NamedTemporaryFile(suffix='.jpg', mode='w+b')
        tmpf_src.write(items[0])
        tmpf_src.seek(0)
        tmpf_dst = tempfile.NamedTemporaryFile(suffix='.jpg', mode='w+b')

        steger = F5.F5(sample_key, 1)

        if rate == None:
            embed_rate = steger.embed_raw_data(tmpf_src.name, os.path.join(package_dir, '../res/toembed'),

                                               tmpf_dst.name)

        else:
            assert (rate >= 0 and rate < 1)
            # print capacity
            hidden = np.random.bytes(int(int(capacity) * rate) / 8)

            embed_rate = steger.embed_raw_data(tmpf_src.name, hidden, tmpf_dst.name, frommem=True)

        tmpf_dst.seek(0)
        raw = tmpf_dst.read()
        index = md5(raw).hexdigest()

        return (index + '.jpg', [raw] + rddinfo_ILS(raw, embed_rate, 0, 1))

    except Exception as e:

        print e
        raise
    finally:
        tmpf_src.close()
        tmpf_dst.close()


def _get_feat(image, feattype='ibd', **kwargs):

    if feattype == 'ibd':
        feater = IntraBlockDiff.FeatIntraBlockDiff()

    else:

        raise Exception("Unknown feature type!")

    desc = feater.feat(image)

    return desc


def rddfeat_ILS(row, feattype='ibd', **kwargs):
    items = row[1]
    capacity, rate, chosen = int(items[4]), float(items[6]), int(items[7])
    try:
        tmpf_src = tempfile.NamedTemporaryFile(suffix='.jpg', mode='w+b')
        tmpf_src.write(items[0])

        tmpf_src.seek(0)

        desc = json.dumps(_get_feat(tmpf_src.name, feattype=feattype).tolist())

        return (row[0], row[1].append(desc))

    except Exception as e:
        print e
        raise

    finally:

        tmpf_src.close()


def format_out(row, cols):
    """
    input:
        e.g. row =('row1',[1,3400,'hello'])
            cols = [['cf_info', 'id'], ['cf_info', 'size'], ['cf_tag', 'desc']]
    return:
        [('row1',['row1', 'cf_info', 'id', '1']),('row1',['row1', 'cf_info', 'size', '3400']),('row1',['row1', 'cf_tag', 'desc', 'hello'])]
    """
    puts = []
    key = row[0]
    for data, col in zip(row[1], cols):
        puts.append((key, [key] + col + [str(data)]))
    return puts

class Sparker(object):

    def __init__(self, host='HPC-server', appname='NewPySparkApp', **kwargs):
        load_env()

        self.host = host
        self.appname = appname

        self.master = kwargs.get('master', 'spark://%s:7077' % self.host)
        self.conf = SparkConf()
        self.conf.setSparkHome(self.host) \
            .setMaster(self.master) \
            .setAppName(self.appname)

        # self.conf.set("spark.akka.frameSize","10685760")
        # self.conf.set("spark.driver.extraClassPath", extraClassPath) \
        # .set("spark.executor.extraClassPath", extraClassPath) \
        # .set("SPARK_CLASSPATH", extraClassPath) \
        # .set("spark.driver.memory", "1G") \

        # .set("spark.yarn.jar", sparkJar)

        self.sc = SparkContext(conf=self.conf)

        self.model = None


    def read_hbase(self, table_name, func=None, collect=False):
        """

        ref - http://happybase.readthedocs.org/en/latest/user.html#retrieving-data

        Filter format:
            columns=['cf1:col1', 'cf1:col2']
            or
            columns=['cf1']

        """

        hconf = {"hbase.zookeeper.quorum": self.host,
                 "hbase.mapreduce.inputtable": table_name,
                 }

        hbase_rdd = self.sc.newAPIHadoopRDD(inputFormatClass=hparams["inputFormatClass"],
                                            keyClass=hparams["readKeyClass"],
                                            valueClass=hparams["readValueClass"],
                                            keyConverter=hparams["readKeyConverter"],
                                            valueConverter=hparams["readValueConverter"],
                                            conf=hconf)

        parser = func if func != None else rddparse_data_CV

        hbase_rdd = hbase_rdd.map(lambda x: parser(x))

        if collect:
            return hbase_rdd.collect()
        else:
            return hbase_rdd

    def write_hbase(self, table_name, data, fromrdd=False, columns=None):
        """

        Data Format: (Deprecated)
            e.g. [["row8", "f1", "", "caocao cao"], ["row9", "f1", "c1", "asdfg hhhh"]]

        Data(from dictionary):
            e.g. data ={'row1':[1,3400,'hello'], 'row2':[34,5000,'here in mine']},
                cols = ['cf_info:id', 'cf_info:size', 'cf_tag:desc']
        Data(from Rdd):
            e.g. data =[('row1',[1,3400,'hello']), ('row2',[34,5000,'here in mine'])],
                cols = ['cf_info:id', 'cf_info:size', 'cf_tag:desc']
        """
        hconf = {"hbase.zookeeper.quorum": self.host,
                 "hbase.mapreduce.inputtable": table_name,
                 "hbase.mapred.outputtable": table_name,
                 "mapreduce.outputformat.class": hparams["outputFormatClass"],
                 "mapreduce.job.output.key.class": hparams["writeKeyClass"],
                 "mapreduce.job.output.value.class": hparams["writeValueClass"],
                 }
        cols = [col.split(':') for col in columns]
        if not fromrdd:
            rdd_data = self.sc.parallelize(data)

        else:

            rdd_data = data

        rdd_data.flatMap(lambda x: format_out(x, cols)).saveAsNewAPIHadoopDataset(
            conf=hconf,
            keyConverter=hparams["writeKeyConverter"],
            valueConverter=hparams["writeValueConverter"])

    def train_svm(self, rdd_labeled):
        svm = SVMWithSGD.train(rdd_labeled)
        self.model = svm

        return svm

    def train_svm(self, X, Y):

        # data = []
        # for feat, tag in zip(X, Y):
        # data.append(LabeledPoint(tag, feat))

        # svm = SVMWithSGD.train(self.sc.parallelize(data))

        hdd_data = self.sc.parallelize(zip(X, Y), 20).map(lambda x: LabeledPoint(x[1], x[0]))

        svm = SVMWithSGD.train(hdd_data)

        self.model = svm

        # with open('res/svm_spark.model', 'wb') as modelfile:
        # model = pickle.dump(svm, modelfile)

        return svm

    def predict_svm(self, x, model=None):
        if model is None:
            if self.model != None:
                model = self.model
            else:
                # with open('res/svm_spark.model', 'rb') as modelfile:
                # model = pickle.load(modelfile)
                raise Exception("No model available!")

        return model.predict(x)

    def test_svm(self, X, Y, model=None):
        pass