53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
# -*- coding: utf-8 -*-
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from transformers import MarianTokenizer, MarianMTModel
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import pickle
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import sys
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import string
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from googletrans import Translator
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import morfeusz2
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import time
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trans = Translator()
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morf = morfeusz2.Morfeusz()
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pl_letters = ['ą','ę','ł','ź','ć','ś','ń','ó','ż']
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def pl_trans():
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for line in sys.stdin:
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new_line = line.rstrip()
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p_line =new_line.capitalize()
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print(p_line)
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def process_words(voc):
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lines = []
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exclude = ['.','?','!',',','/','-','+','=',')','(','%','0','1','2','3','4','5','6','7','8','9','[',']',':',';',"'",'"']
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confusion_words = ['on', 'one', 'no', 'my', 'knot', 'but', 'chart', 'prom', 'pup', 'much', 'lot', 'pan', 'herb', 'dude', 'to', 'wanna', 'unia', 'we']
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file_to_process = {}
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for idx,line in enumerate(sys.stdin):
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line_to_process = line.rstrip().split()
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processed_line = ''
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new_line = []
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for word in line_to_process:
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en_word = ''
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p_word = ''.join(w for w in word if w not in exclude)
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analysis = morf.analyse(p_word)
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for i,j,l in analysis:
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lema = l[0]
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if str(p_word) in voc.keys() and p_word.lower() not in confusion_words and p_word not in exclude:
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en_word = voc[p_word]
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elif str(lema) in voc.keys() and lema.lower() not in confusion_words and lema not in exclude:
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en_word = voc[str(lema)]
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else:
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if p_word.lower() in confusion_words and check_letters(word.lower(),pl_letters):
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en_word = word
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else:
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en_word = trans.translate(word,dest='en',src='pl').text
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processed_line = processed_line + en_word + ' '
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print(processed_line)
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def check_letters(text,pl):
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for ch in text:
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if ch in pl:
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return False
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return True
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#voc = pickle.load(open('pl_en.pickle', 'rb'))
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#process_words(voc)
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pl_trans() |