import configparser

import os

import pathlib

import numpy as np

import pandas as pd

from translate.storage.tmx import tmxfile

from algo.translation_scores import calculate_bleu_score, calculate_meteor_score

configParser = configparser.RawConfigParser()

configFilePath = "config.txt"

configParser.read(configFilePath)

source = configParser.get('data-preparing-config', 'SOURCE')

target = configParser.get('data-preparing-config', 'TARGET')

data_folder = configParser.get('data-preparing-config', 'DATA_FOLDER')

target_name = configParser.get('data-preparing-config', 'TARGET')

result_folder = configParser.get('result-config', 'RESULT_FOLDER')

result_file = configParser.get('okapi-config', 'RESULT_FILE')

test_src = pd.read_csv(os.path.join(data_folder, target_name, "src_volume_3.tsv"), sep="\t")

test_target = pd.read_csv(os.path.join(data_folder, target_name, "target_volume_3.tsv"), sep="\t")

test = pd.concat([test_src, test_target], axis=1)

test = test.reset_index(drop=True)

print(test.head())

test_source_sentences = test["source"].astype(str).tolist()

test_target_sentences = test["target"].astype(str).tolist()

source_sentences = list()

target_sentences = list()

retrived_target_sentences = list()

bleu_scores = list()

meteor_scores = list()

tmx_file_path = "result/ES-ES/unapproved.tmx"

with open(tmx_file_path, 'rb') as fin:

tmx_file = tmxfile(fin, source, target)

i = 0

for node in tmx_file.unit_iter():

i = i + 1

source_sentence = node.getsource().strip()

retrieved_target_sentence = node.gettarget().strip()

index = test_source_sentences.index(source_sentence) if source_sentence in test_source_sentences else -1

if index > -1:

target_sentence = test_target_sentences[index]

bleu_score = calculate_bleu_score(target_sentence, retrieved_target_sentence)

meteor_score = calculate_meteor_score(target_sentence, retrieved_target_sentence)

source_sentences.append(source_sentence)

target_sentences.append(target_sentence)

retrived_target_sentences.append(retrieved_target_sentence)

bleu_scores.append(bleu_score)

meteor_scores.append(meteor_score)

if i % 10 == 0:

print(i)

fin.close()

print(target_sentences)

# print(len(source_sentences))

# print(len(target_sentences))

# print(len(retrived_target_sentences))

# print(len(bleu_scores))

#

# print("started saving")

#

# with open('some.csv', 'w') as f:

# writer = csv.writer(f, delimiter='\t')

# writer.writerows(zip(source_sentences, target_sentences))

okapi_results = pd.DataFrame(

np.column_stack([source_sentences, target_sentences, retrived_target_sentences, bleu_scores, meteor_scores]),

columns=['source', 'target', 'retrieved_target_sentences', 'bleu_scores', 'meteor_scores'])

pathlib.Path(os.path.join(result_folder, target_name)).mkdir(parents=True, exist_ok=True)

okapi_results.to_csv(os.path.join(result_folder, target_name, result_file), sep='\t', index=False)

#

# okapi_results = pd.DataFrame(source_sentences, columns=['source'])

#

# pathlib.Path(os.path.join(result_folder, target_name)).mkdir(parents=True, exist_ok=True)

# okapi_results.to_csv(os.path.join(result_folder, target_name, result_file), sep='\t', index=False)

#

# okapi_results_temp = pd.read_csv(os.path.join(result_folder, target_name, result_file), sep="\t")

# #

# print("adding more columns")

# #

# okapi_results_temp["target"] = pd.Series(target_sentences)

# # okapi_results['retrieved_target_sentences'] = retrived_target_sentences

# # okapi_results['bleu_scores'] = bleu_scores

#

# # okapi_results_temp.to_csv(os.path.join(result_folder, target_name, result_file), sep='\t', index=False)

print("done")