#!/usr/bin/env python

from heapq import heappush, heappop
from multiprocessing import Queue, Process, Value, cpu_count
from sklearn import neighbors
from sklearn import svm
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.externals import joblib
from tempfile import TemporaryFile, NamedTemporaryFile
from timeit import default_timer

import argparse
import logging
import math
import numpy as np
import os
import random
import sklearn
import sys
from toolwrapper import ToolWrapper
from mosestokenizer import MosesTokenizer

#Allows to load modules while inside or outside the package 

#Allows to load modules while inside or outside the package  
try:
    from .features import feature_extract, FEATURES_VERSION
    from .prob_dict import ProbabilisticDictionary
    from .util import no_escaping, check_positive, check_positive_or_zero, logging_setup
    from .training import shuffle,precision_recall, repr_right, write_metadata, train_fluency_filter
except (SystemError, ImportError):
    from features import feature_extract, FEATURES_VERSION
    from prob_dict import ProbabilisticDictionary
    from util import no_escaping, check_positive, check_positive_or_zero, logging_setup
    from training import shuffle,precision_recall, repr_right, write_metadata, train_fluency_filter 

__author__ = "Sergio Ortiz-Rojas"
# Please, don't delete the previous descriptions. Just add new version description at the end.

__version__ = "Version 0.1 # December 2017 # Initial version # Sergio Ortiz-Rojas"
__version__ = "Version 0.2 # 09/01/2018 # Adding argument for injecting wrong examples from a file # Jorge Ferrández-Tordera"
__version__ = "Version 0.3 # 18/01/2019 # Integrated training of LM and refactor to avoid code duplicity # Víctor M. Sánchez-Cartagena"
    
# Argument parsing
def initialization():
    parser = argparse.ArgumentParser(prog=os.path.basename(sys.argv[0]), formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=__doc__)

    parser.add_argument('input',  nargs='?', type=argparse.FileType('r'), default=sys.stdin,  help="Tab-separated bilingual input file")

    groupM = parser.add_argument_group("Mandatory")
    groupM.add_argument('-m', '--metadata', type=argparse.FileType('w'), required=True, help="Training metadata (YAML file)")
    groupM.add_argument('-c', '--classifier', type=argparse.FileType('wb'), required=True, help="Classifier data file")
    groupM.add_argument('-s', '--source_lang',  required=True, help="Source language")
    groupM.add_argument('-t', '--target_lang', required=True, help="Target language")
    groupM.add_argument('-d', '--source_dictionary',  type=argparse.FileType('r'), required=True, help="LR gzipped probabilistic dictionary")
    groupM.add_argument('-D', '--target_dictionary', type=argparse.FileType('r'), required=True, help="RL gzipped probabilistic dictionary")

    groupO = parser.add_argument_group('Options')
    groupO.add_argument('-S', '--source_tokeniser_path', help="Source language tokeniser absolute path")
    groupO.add_argument('-T', '--target_tokeniser_path', help="Target language tokeniser absolute path")
    groupO.add_argument('--normalize_by_length', action='store_true', help="Normalize by length in qmax dict feature")
    groupO.add_argument('--treat_oovs', action='store_true', help="Special treatment for OOVs in qmax dict feature")
    groupO.add_argument('--qmax_limit', type=check_positive_or_zero, default=20, help="Number of max target words to be taken into account, sorted by length")
    groupO.add_argument('--disable_features_quest', action='store_false', help="Disable less important features")
    groupO.add_argument('-g', '--good_examples',  type=check_positive_or_zero, default=50000, help="Number of good examples")
    groupO.add_argument('-w', '--wrong_examples', type=check_positive_or_zero, default=50000, help="Number of wrong examples")
    groupO.add_argument('--good_test_examples',  type=check_positive_or_zero, default=10000, help="Number of good test examples")
    groupO.add_argument('--wrong_test_examples', type=check_positive_or_zero, default=10000, help="Number of wrong test examples")
    groupO.add_argument('--classifier_type', choices=['svm', 'nn', 'nn1', 'adaboost', 'random_forest'], default="random_forest", help="Classifier type")
    groupO.add_argument('--dump_features', type=argparse.FileType('w'), default=None, help="Dump training features to file")
    groupO.add_argument('-b', '--block_size', type=check_positive, default=10000, help="Sentence pairs per block")
    groupO.add_argument('-p', '--processes', type=check_positive, default=max(1, cpu_count()-1), help="Number of process to use")
    groupO.add_argument('--wrong_examples_file', type=argparse.FileType('r'), default=None, help="File with wrong examples extracted to replace the synthetic examples from method used by default")
    groupO.add_argument('--features_version', type=check_positive, default=FEATURES_VERSION , help="Version of the features")

    #For LM filtering
    groupO.add_argument('--noisy_examples_file_sl', type=str, help="File with noisy text in the SL. These are used to estimate the perplexity of noisy text.")
    groupO.add_argument('--noisy_examples_file_tl', type=str, help="File with noisy text in the TL. These are used to estimate the perplexity of noisy text.")
    groupO.add_argument('--lm_dev_size', type=check_positive_or_zero, default=2000, help="Number of sentences to be removed from clean text before training LMs. These are used to estimate the perplexity of clean text.")
    groupO.add_argument('--lm_file_sl', type=str, help="SL language model output file.")
    groupO.add_argument('--lm_file_tl', type=str, help="TL language model output file.")
    groupO.add_argument('--lm_training_file_sl', type=str, help="SL text from which the SL LM is trained. If this parameter is not specified, SL LM is trained from the SL side of the input file, after removing --lm_dev_size sentences.")
    groupO.add_argument('--lm_training_file_tl', type=str, help="TL text from which the TL LM is trained. If this parameter is not specified, TL LM is trained from the TL side of the input file, after removing --lm_dev_size sentences.")
    groupO.add_argument('--lm_clean_examples_file_sl', type=str, help="File with clean text in the SL. Used to estimate the perplexity of clean text. This option must be used together with --lm_training_file_sl and both files must not have common sentences. This option replaces --lm_dev_size.")
    groupO.add_argument('--lm_clean_examples_file_tl', type=str, help="File with clean text in the TL. Used to estimate the perplexity of clean text. This option must be used together with --lm_training_file_tl and both files must not have common sentences. This option replaces --lm_dev_size.")
    
    
    groupL = parser.add_argument_group('Logging')
    groupL.add_argument('-q', '--quiet', action='store_true', help='Silent logging mode')
    groupL.add_argument('--debug', action='store_true', help='Debug logging mode')
    groupL.add_argument('--logfile', type=argparse.FileType('a'), default=sys.stderr, help="Store log to a file")

    args = parser.parse_args()
    # Logging
    logging_setup(args)
    return args

# Training function: receives two file descriptors, input and test, and a
# type classifiers and trains a classifier storing it in classifier_output
# and returns some quality estimates.
def train_classifier(input_features, test_features, classifier_type, classifier_output):
    feats=[]
    labels=[]

    # Load features and labels and format them as numpy array
    for line in input_features:
        parts=line.rstrip("\n").split("\t")
        feats.append( [float(v) for v in parts[:-1] ] )
        labels.append(int(parts[-1]))
        
    dataset = dict()
    dataset['data']   = np.array(feats)
    dataset['target'] = np.array(labels)
    
    # Train classifier
    if classifier_type == "svm":
        clf = svm.SVC(gamma=0.001, C=100., probability=True)
    elif classifier_type == "nn":
        clf = neighbors.KNeighborsClassifier(n_neighbors=5, n_jobs=-1)
    elif classifier_type == "nn1":
        clf = neighbors.KNeighborsClassifier(n_neighbors=1, n_jobs=-1)
    elif classifier_type == "adaboost":
        clf = AdaBoostClassifier(n_estimators=100)
    elif classifier_type == "random_forest":
        clf = RandomForestClassifier(bootstrap=True, class_weight=None,
                                     criterion='gini',
                                     max_depth=2, 
                                     max_features='auto', 
                                     max_leaf_nodes=None,
                                     min_impurity_decrease=0.0, 
                                     min_impurity_split=None,
                                     min_samples_leaf=1, 
                                     min_samples_split=2,
                                     min_weight_fraction_leaf=0.0, 
                                     n_estimators=200, n_jobs=-1,
                                     oob_score=False, 
                                     random_state=0, 
                                     verbose=0, 
                                     warm_start=False)
    else:
        logging.error("Unknown classifier: "+ classifier_type)
        sys.exit(1)

    clf.fit(dataset['data'], dataset['target'])

    joblib.dump(clf, classifier_output)

    feats = []
    labels = []

    for line in test_features:
        parts = line.rstrip("\n").split("\t")
        feats.append([float (v) for v in parts[:-1]])
        labels.append(int(parts[-1]))

    dataset = np.array(feats)
    prediction = clf.predict_proba(dataset)
    
    pos = 0
    good = []
    wrong = []
    for pred in prediction:
        if labels[pos] == 1:
           good.append(pred[1])
        else:
           wrong.append(pred[1])
        pos += 1

    hgood  = np.histogram(good,  bins = np.arange(0, 1.1, 0.1))
    hwrong = np.histogram(wrong, bins = np.arange(0, 1.1, 0.1))

    return hgood[0].tolist(), hwrong[0].tolist()



# Writes all features of the input TUs into a temporary file
def reduce_process(output_queue, output_file):
    h = []
    last_block = 0
    while True:
        logging.debug("Reduce: heap status {}".format(h.__str__()))
        while len(h) > 0 and h[0][0] == last_block:
            nblock, filein_name = heappop(h)
            last_block += 1

            with open(filein_name, 'r') as filein:
                for i in filein:
                    output_file.write(i)
                filein.close()

            os.unlink(filein_name)

        job = output_queue.get()
        if job:
            nblock, filein_name = job
            heappush(h, (nblock, filein_name))
        else:
            logging.debug("Exiting reduce loop")
            break

    if len(h) > 0:
        logging.debug("Still elements in heap")

    while len(h) > 0 and h[0][0] == last_block:
        nblock, filein_name = heapq.heappop(h)
        last_block += 1

        with open(filein_name, 'r') as filein:
            for i in filein:
                output_file.write(i)
            filein.close()
    
        os.unlink(filein_name)

    if len(h) != 0:
        logging.error("The queue is not empty and it should!")
        sys.exit(0)

    output_file.close()

# Calculates all the features needed for the training
def worker_process(i, jobs_queue, output_queue, args):
    if args.source_tokeniser_path:
        source_tokeniser = ToolWrapper(args.source_tokeniser_path.split(' '))
    else:
        source_tokeniser = MosesTokenizer(args.source_lang)
    if args.target_tokeniser_path:
        target_tokeniser = ToolWrapper(args.target_tokeniser_path.split(' '))
    else:
        target_tokeniser = MosesTokenizer(args.target_lang)
    while True:
        job = jobs_queue.get()
        if job:
            logging.debug("Job {}".format(job.__repr__()))
            nblock, filein_name, label = job

            with open(filein_name, 'r') as filein, NamedTemporaryFile(mode="w", delete=False) as fileout:
                logging.debug("Filtering: creating temporary file {}".format(fileout.name))
                for i in filein:
                    srcsen,trgsen = i.split("\t")[:2]
                    trgsen = trgsen.strip()
#                    print(str(srcsen) + " --- " + str(trgsen))
                    features = feature_extract(srcsen, trgsen, source_tokeniser, target_tokeniser, args)
                    
                    for j in features:
                        fileout.write("{}".format(j))
                        fileout.write("\t")
                    fileout.write("{}".format(label))
                    fileout.write("\n")
                ojob = (nblock, fileout.name)
                fileout.close()
                filein.close()
                output_queue.put(ojob)
            os.unlink(filein_name)
        else:
            logging.debug("Exiting worker")
            source_tokeniser.close()
            target_tokeniser.close()
            break

# Divides the input among processors to speed up the throughput
def map_process(input, block_size, jobs_queue, label, first_block=0):
    logging.info("Start mapping")
    nblock = first_block
    nline = 0
    mytemp = None
    for line in input:
        if (nline % block_size) == 0:
            if mytemp:
                job = (nblock, mytemp.name, label)
                mytemp.close()
                jobs_queue.put(job)
                nblock += 1
            mytemp = NamedTemporaryFile(mode="w", delete=False)
            logging.debug("Mapping: creating temporary file {}".format(mytemp.name))
        parts = line.split("\t")[0:2]
        if len(parts) >= 2:
            mytemp.write(line)
            nline += 1

    input.close()

    if nline > 0:
        job = (nblock, mytemp.name, label)
        mytemp.close()
        jobs_queue.put(job)

    return nblock

# Main loop of the program
def perform_training(args):
    time_start = default_timer()
    logging.info("Starting process")
    logging.info("Running {0} workers at {1} rows per block".format(args.processes, args.block_size))

    process_count = max(1, args.processes)
    maxsize = 1000 * process_count

    output_queue = Queue(maxsize = maxsize)
    worker_count = process_count

    #Read input to a named temporary file
    #We may need to read it multiple times and that would be problematic if it is sys.stdin
    input = NamedTemporaryFile(mode="w",delete=False)
    for line in args.input:
        input.write(line)
    input.close()
    
    stats=None
    with open(input.name) as input_f:
        args.input=input_f
        stats=train_fluency_filter(args)
        input_f.seek(0)

        # Shuffle and get length ratio
        total_size, length_ratio, good_sentences, wrong_sentences = shuffle(args.input, args.good_examples + args.good_test_examples, args.wrong_examples + args.wrong_test_examples, args.wrong_examples_file)
    os.remove(input.name)
    
    args.length_ratio = length_ratio

    # Load dictionaries
    args.dict_sl_tl = ProbabilisticDictionary(args.source_dictionary)
    args.dict_tl_sl = ProbabilisticDictionary(args.target_dictionary)


    features_file = TemporaryFile('w+')
    # Start reducer
    reduce = Process(target = reduce_process,
                     args   = (output_queue, features_file))
    reduce.start()

    # Start workers
    jobs_queue = Queue(maxsize = maxsize)
    workers = []
    for i in range(worker_count):
        worker = Process(target = worker_process,
                         args   = (i, jobs_queue, output_queue, args))
        worker.daemon = True # dies with the parent process
        worker.start()
        workers.append(worker)


    # Mapper process (foreground - parent)
    last_block = map_process(good_sentences, args.block_size, jobs_queue, 1, 0)
    good_sentences.close()

    map_process(wrong_sentences, args.block_size, jobs_queue, 0, last_block+1)
    wrong_sentences.close()


    # Worker termination
    for _ in workers:
        jobs_queue.put(None)

    logging.info("End mapping")

    for w in workers:
        w.join()

    # Reducer termination
    output_queue.put(None)
    reduce.join()

    features_file.seek(0)

    if args.dump_features:
        logging.info("Dumping features to " + os.path.abspath(args.dump_features.name))
        for i in features_file:
            args.dump_features.write(i)
        args.dump_features.close()
        features_file.seek(0)

    logging.info("Start training")

    hgood = []
    hwrong = []
    with TemporaryFile("w+") as features_train, TemporaryFile("w+") as features_test:
        nline = 0
        for line in features_file:
            if nline < args.good_examples:
                features_train.write(line)
            elif nline < args.good_examples + args.good_test_examples:
                features_test.write(line)
            elif nline < args.good_examples + args.good_test_examples + args.wrong_examples:
                features_train.write(line)
            else:
                features_test.write(line)
            nline += 1
        
        features_train.flush()
        features_test.flush()
        
        features_train.seek(0)
        features_test.seek(0)
        hgood, hwrong = train_classifier(features_train, features_test, args.classifier_type, args.classifier)
        features_train.close()
        features_test.close()

    logging.info("End training")

    write_metadata(args, length_ratio, hgood, hwrong, stats)
    args.metadata.close()

    # Stats
    logging.info("Finished")
    elapsed_time = default_timer() - time_start
    logging.info("Elapsed time {:.2f} s".format(elapsed_time))

# Main function: setup logging and calling the main loop
def main(args):
    # Parameter parsing
#    args = initialization()

    # Filtering
    perform_training(args)

if __name__ == '__main__':
    args = initialization()
    main(args)
