# Path: dsenser/constants.py
# ENCODING = "utf-8"
# 
# DFLT_BROWN_PATH = os.path.join(DATA_DIR, "brown_cluster_1000.txt")
# 
# DFLT_ECONN_PATH = os.path.join(ECONN_DIR, "ExpConn.txt")
# 
# DFLT_INQUIRER_PATH = os.path.join(DATA_DIR, "inquirer_word")
# 
# DFLT_LCSI_PATH = os.path.join(DATA_DIR, "LCSInfomerge.txt")
# 
# DFLT_MPQA_PATH = os.path.join(DATA_DIR, "MPQA_Subjectivity_Lexicon.txt")
# 
# DFLT_W2V_PATH = os.path.join(DATA_DIR, "GoogleNews-vectors-negative300.bin")


from dsenser.constants import ENCODING, DFLT_BROWN_PATH, DFLT_ECONN_PATH, \
    DFLT_INQUIRER_PATH, DFLT_LCSI_PATH, DFLT_MPQA_PATH, DFLT_W2V_PATH
from dsenser.scorer.conn_head_mapper import ConnHeadMapper
from collections import defaultdict
from nltk.stem.porter import PorterStemmer
    from gensim.models.word2vec import Word2Vec
import codecs
import gc
import re
import sys
           unloads the resource

        """
        if self.resource is not None:
            print("Unloading resource '{:s}'...".format(repr(self.resource)),
                  file=sys.stderr)
            del self.resource
            self.resource = None
            gc.collect()


##################################################################
# Resources
LCSI = load_LCSI(DFLT_LCSI_PATH)
BROWN_CLUSTERS = LoadOnDemand(load_BROWN, DFLT_BROWN_PATH)
CONNS = load_conns(DFLT_ECONN_PATH)
CONNTOK2CONN = defaultdict(list)

itok = None
for iconn in CONNS:
    for i, ipart in enumerate(iconn):
        itok = ipart[0]
        CONNTOK2CONN[itok].append((i, iconn))

for iconns in CONNTOK2CONN.itervalues():
    iconns.sort(key=lambda el: el[0])

CONNTOKS = set(CONNTOK2CONN.keys())
INQUIRER, STEMMED_INQUIRER = load_INQUIRER(DFLT_INQUIRER_PATH)
MPQA = load_MPQA(DFLT_MPQA_PATH)

# Path: dsenser/constants.py
# ENCODING = "utf-8"
# 
# DFLT_BROWN_PATH = os.path.join(DATA_DIR, "brown_cluster_1000.txt")
# 
# DFLT_ECONN_PATH = os.path.join(ECONN_DIR, "ExpConn.txt")
# 
# DFLT_INQUIRER_PATH = os.path.join(DATA_DIR, "inquirer_word")
# 
# DFLT_LCSI_PATH = os.path.join(DATA_DIR, "LCSInfomerge.txt")
# 
# DFLT_MPQA_PATH = os.path.join(DATA_DIR, "MPQA_Subjectivity_Lexicon.txt")
# 
# DFLT_W2V_PATH = os.path.join(DATA_DIR, "GoogleNews-vectors-negative300.bin")


# Path: dsenser/resources.py
from dsenser.constants import ENCODING, DFLT_BROWN_PATH, DFLT_ECONN_PATH, \
    DFLT_INQUIRER_PATH, DFLT_LCSI_PATH, DFLT_MPQA_PATH, DFLT_W2V_PATH
from dsenser.scorer.conn_head_mapper import ConnHeadMapper
from collections import defaultdict
from nltk.stem.porter import PorterStemmer
    from gensim.models.word2vec import Word2Vec
import codecs
import gc
import re
import sys

           unloads the resource

        """
        if self.resource is not None:
            print("Unloading resource '{:s}'...".format(repr(self.resource)),
                  file=sys.stderr)
            del self.resource
            self.resource = None
            gc.collect()


##################################################################
# Resources
LCSI = load_LCSI(DFLT_LCSI_PATH)
BROWN_CLUSTERS = LoadOnDemand(load_BROWN, DFLT_BROWN_PATH)
CONNS = load_conns(DFLT_ECONN_PATH)
CONNTOK2CONN = defaultdict(list)

itok = None
for iconn in CONNS:
    for i, ipart in enumerate(iconn):
        itok = ipart[0]
        CONNTOK2CONN[itok].append((i, iconn))

for iconns in CONNTOK2CONN.itervalues():
    iconns.sort(key=lambda el: el[0])

CONNTOKS = set(CONNTOK2CONN.keys())
INQUIRER, STEMMED_INQUIRER = load_INQUIRER(DFLT_INQUIRER_PATH)
MPQA = load_MPQA(DFLT_MPQA_PATH)

W2V = LoadOnDemand(load_W2V, DFLT_W2V_PATH)