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351 lines
16 KiB
351 lines
16 KiB
import argparse
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import json
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import logging
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import os
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import shutil
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import time
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from xml.etree import ElementTree
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from conllu import TokenList
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import conllu
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import classla
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import copy
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from lxml import etree
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from src.create_tei import construct_sentence_from_list, \
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construct_paragraph_from_list, TeiDocument, build_tei_etrees, build_links, build_complete_tei, convert_bibl
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logging.basicConfig(level=logging.INFO)
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def add_token(svala_i, source_i, target_i, el, source, target, edges, svala_data, sentence_string_id):
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source_id = "s" + svala_i
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target_id = "t" + svala_i
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edge_id = "e-" + source_id + "-" + target_id
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labels = svala_data['edges'][edge_id]['labels']
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sentence_string_id_split = sentence_string_id.split('.')
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source_token_id = f'{sentence_string_id_split[0]}s.{".".join(sentence_string_id_split[1:])}.{source_i}'
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target_token_id = f'{sentence_string_id_split[0]}t.{".".join(sentence_string_id_split[1:])}.{source_i}'
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token_tag = 'w' if el.tag.startswith('w') else 'pc'
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lemma = el.attrib['lemma'] if token_tag == 'w' else el.text
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source.append({'token': el.text, 'tag': token_tag, 'ana': el.attrib['ana'], 'lemma': lemma, 'id': source_token_id, 'space_after': False})
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target.append({'token': el.text, 'tag': token_tag, 'ana': el.attrib['ana'], 'lemma': lemma, 'id': target_token_id, 'space_after': False})
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edges.append({'source_ids': [source_token_id], 'target_ids': [target_token_id], 'labels': labels})
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def add_error_token(el, out_list, sentence_string_id, out_list_i, out_list_ids, is_source):
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sentence_string_id_split = sentence_string_id.split('.')
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source_token_id = f'{sentence_string_id_split[0]}s.{".".join(sentence_string_id_split[1:])}.{out_list_i}' if is_source \
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else f'{sentence_string_id_split[0]}t.{".".join(sentence_string_id_split[1:])}.{out_list_i}'
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token_tag = 'w' if el.tag.startswith('w') else 'pc'
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lemma = el.attrib['lemma'] if token_tag == 'w' else el.text
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out_list.append({'token': el.text, 'tag': token_tag, 'ana': el.attrib['ana'], 'lemma': lemma, 'id': source_token_id, 'space_after': False})
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out_list_ids.append(source_token_id)
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def add_errors(svala_i, source_i, target_i, error, source, target, edges, svala_data, sentence_string_id):
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source_edge_ids = []
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target_edge_ids = []
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source_ids = []
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target_ids = []
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# solar5.7
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for el in error:
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if el.tag.startswith('w') or el.tag.startswith('pc'):
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ind = str(svala_i)
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source_id = "s" + ind
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source_edge_ids.append(source_id)
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add_error_token(el, source, sentence_string_id, source_i, source_ids, True)
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source_i += 1
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svala_i += 1
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elif el.tag.startswith('c') and len(source) > 0:
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source[-1]['space_after'] = True
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elif el.tag.startswith('p'):
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for p_el in el:
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if p_el.tag.startswith('w') or p_el.tag.startswith('pc'):
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ind = str(svala_i)
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target_id = "t" + ind
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target_edge_ids.append(target_id)
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add_error_token(p_el, target, sentence_string_id, target_i, target_ids, False)
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target_i += 1
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svala_i += 1
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elif p_el.tag.startswith('c') and len(target) > 0:
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target[-1]['space_after'] = True
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elif el.tag.startswith('u2'):
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for el_l2 in el:
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if el_l2.tag.startswith('w') or el_l2.tag.startswith('pc'):
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ind = str(svala_i)
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source_id = "s" + ind
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source_edge_ids.append(source_id)
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add_error_token(el_l2, source, sentence_string_id, source_i, source_ids, True)
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source_i += 1
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svala_i += 1
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elif el_l2.tag.startswith('c') and len(source) > 0:
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source[-1]['space_after'] = True
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elif el_l2.tag.startswith('u3'):
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for el_l3 in el_l2:
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if el_l3.tag.startswith('w') or el_l3.tag.startswith('pc'):
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ind = str(svala_i)
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source_id = "s" + ind
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source_edge_ids.append(source_id)
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add_error_token(el_l3, source, sentence_string_id, source_i, source_ids, True)
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source_i += 1
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svala_i += 1
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elif el_l3.tag.startswith('c') and len(source) > 0:
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source[-1]['space_after'] = True
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elif el_l3.tag.startswith('u4'):
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for el_l4 in el_l3:
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if el_l4.tag.startswith('w') or el_l4.tag.startswith('pc'):
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ind = str(svala_i)
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source_id = "s" + ind
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source_edge_ids.append(source_id)
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add_error_token(el_l4, source, sentence_string_id, source_i, source_ids, True)
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source_i += 1
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svala_i += 1
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elif el_l4.tag.startswith('c') and len(source) > 0:
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source[-1]['space_after'] = True
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elif el_l4.tag.startswith('u5'):
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for el_l5 in el_l4:
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if el_l5.tag.startswith('w') or el_l5.tag.startswith('pc'):
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ind = str(svala_i)
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source_id = "s" + ind
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source_edge_ids.append(source_id)
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add_error_token(el_l5, source, sentence_string_id, source_i, source_ids, True)
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source_i += 1
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svala_i += 1
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elif el_l5.tag.startswith('c') and len(source) > 0:
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source[-1]['space_after'] = True
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# TODO NOT SURE IF THIS SHOULD BE COMMENTED! IF IT IS NOT THERE ARE ERRORS ON 2ND lvl of errors, where some words are duplicated
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# for p_el in el:
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# if p_el.tag.startswith('w') or p_el.tag.startswith('pc'):
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# ind = str(svala_i)
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#
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# target_id = "t" + ind
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# target_edge_ids.append(target_id)
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#
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# add_error_token(p_el, target, sentence_string_id, target_i, target_ids, False)
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#
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# target_i += 1
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# svala_i += 1
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# elif p_el.tag.startswith('c') and len(target) > 0:
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# target[-1]['space_after'] = True
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edge_ids = sorted(source_edge_ids) + sorted(target_edge_ids)
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edge_id = "e-" + "-".join(edge_ids)
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edges.append({'source_ids': source_ids, 'target_ids': target_ids, 'labels': svala_data['edges'][edge_id]['labels']})
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return svala_i, source_i, target_i
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def create_conllu(interest_list, sentence_string_id):
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conllu_result = TokenList([{"id": token_i + 1, "form": token['token'], "lemma": None, "upos": None, "xpos": None, "feats": None,
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"head": None, "deprel": None, "deps": None, "misc": "SpaceAfter=No"} if not token['space_after']
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else {"id": token_i + 1, "form": token['token'], "lemma": None, "upos": None, "xpos": None,
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"feats": None, "head": None, "deprel": None, "deps": None, "misc": None} for token_i, token in
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enumerate(interest_list)])
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# Delete last SpaceAfter
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misc = conllu_result[len(conllu_result) - 1]['misc'] if len(conllu_result) > 0 else None
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if misc is not None:
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misc_split = misc.split('|')
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if misc is not None and misc == 'SpaceAfter=No':
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conllu_result[len(conllu_result) - 1]['misc'] = None
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elif misc is not None and 'SpaceAfter=No' in misc_split:
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conllu_result[len(conllu_result) - 1]['misc'] = '|'.join([el for el in misc_split if el != 'SpaceAfter=No'])
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conllu_result.metadata = {"sent_id": sentence_string_id}
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return conllu_result.serialize()
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def process_file(et, args, nlp):
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if os.path.exists(args.results_folder):
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shutil.rmtree(args.results_folder)
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os.mkdir(args.results_folder)
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etree_source_documents = []
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etree_target_documents = []
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etree_source_divs = []
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etree_target_divs = []
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complete_source_conllu = ''
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complete_target_conllu = ''
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document_edges = []
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for div in et.iter('div'):
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bibl = div.find('bibl')
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file_name = bibl.get('n')
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file_name = file_name.replace('/', '_')
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svala_path = os.path.join(args.svala_folder, file_name)
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# skip files that are not svala annotated (to enable short examples)
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if not os.path.isdir(svala_path):
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continue
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svala_list = [[fname[:-13], fname] if 'problem' in fname else [fname[:-5], fname] for fname in os.listdir(svala_path)]
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svala_dict = {e[0]: e[1] for e in svala_list}
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etree_source_paragraphs = []
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etree_target_paragraphs = []
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paragraph_edges = []
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paragraphs = div.findall('p')
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for paragraph in paragraphs:
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sentences = paragraph.findall('s')
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svala_i = 1
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# read json
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svala_file = os.path.join(svala_path, svala_dict[paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id']])
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jf = open(svala_file)
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svala_data = json.load(jf)
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jf.close()
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etree_source_sentences = []
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etree_target_sentences = []
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sentence_edges = []
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for sentence_id, sentence in enumerate(sentences):
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source = []
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target = []
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edges = []
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sentence_id += 1
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source_i = 1
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target_i = 1
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sentence_string_id = paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'] + f'.{sentence_id}'
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for el in sentence:
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if el.tag.startswith('w'):
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add_token(str(svala_i), source_i, target_i, el, source, target, edges, svala_data, sentence_string_id)
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svala_i += 1
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source_i += 1
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target_i += 1
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elif el.tag.startswith('pc'):
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add_token(str(svala_i), source_i, target_i, el, source, target, edges, svala_data, sentence_string_id)
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svala_i += 1
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source_i += 1
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target_i += 1
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elif el.tag.startswith('u'):
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svala_i, source_i, target_i = add_errors(svala_i, source_i, target_i, el, source, target, edges, svala_data, sentence_string_id)
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elif el.tag.startswith('c'):
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if len(source) > 0:
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source[-1]['space_after'] = True
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if len(target) > 0:
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target[-1]['space_after'] = True
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sentence_edges.append(edges)
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if len(source) > 0:
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source_conllu = create_conllu(source, sentence_string_id)
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if len(target) > 0:
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target_conllu = create_conllu(target, sentence_string_id)
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if len(source) > 0:
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source_conllu_annotated = nlp(source_conllu).to_conll()
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if len(target) > 0:
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target_conllu_annotated = nlp(target_conllu).to_conll()
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if len(source) > 0:
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complete_source_conllu += source_conllu_annotated
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complete_target_conllu += target_conllu_annotated
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if len(source) > 0:
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source_conllu_parsed = conllu.parse(source_conllu_annotated)[0]
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if len(target) > 0:
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target_conllu_parsed = conllu.parse(target_conllu_annotated)[0]
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if len(source) > 0:
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etree_source_sentences.append(construct_sentence_from_list(str(sentence_id), source_conllu_parsed, True))
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if len(target) > 0:
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etree_target_sentences.append(construct_sentence_from_list(str(sentence_id), target_conllu_parsed, False))
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etree_source_paragraphs.append(construct_paragraph_from_list(paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[0], paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[1], etree_source_sentences, True))
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etree_target_paragraphs.append(construct_paragraph_from_list(paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[0], paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[1], etree_target_sentences, False))
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paragraph_edges.append(sentence_edges)
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etree_bibl = convert_bibl(bibl)
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etree_source_divs.append((etree_source_paragraphs, copy.deepcopy(etree_bibl)))
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etree_target_divs.append((etree_target_paragraphs, copy.deepcopy(etree_bibl)))
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document_edges.append(paragraph_edges)
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etree_source_documents.append(TeiDocument(paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[0] + 's', etree_source_divs))
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etree_target_documents.append(TeiDocument(paragraph.attrib['{http://www.w3.org/XML/1998/namespace}id'].split('.')[0] + 't', etree_target_divs))
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etree_source = build_tei_etrees(etree_source_documents)
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etree_target = build_tei_etrees(etree_target_documents)
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etree_links = build_links(document_edges)
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complete_etree = build_complete_tei(copy.deepcopy(etree_source), copy.deepcopy(etree_target), etree_links)
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with open(os.path.join(args.results_folder, f"source.conllu"), 'w') as sf:
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sf.write(complete_source_conllu)
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with open(os.path.join(args.results_folder, f"target.conllu"), 'w') as sf:
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sf.write(complete_target_conllu)
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with open(os.path.join(args.results_folder, f"source.xml"), 'w') as sf:
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sf.write(etree.tostring(etree_source[0], pretty_print=True, encoding='utf-8').decode())
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with open(os.path.join(args.results_folder, f"target.xml"), 'w') as tf:
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tf.write(etree.tostring(etree_target[0], pretty_print=True, encoding='utf-8').decode())
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with open(os.path.join(args.results_folder, f"links.xml"), 'w') as tf:
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tf.write(etree.tostring(etree_links, pretty_print=True, encoding='utf-8').decode())
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with open(os.path.join(args.results_folder, f"complete.xml"), 'w') as tf:
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tf.write(etree.tostring(complete_etree, pretty_print=True, encoding='utf-8').decode())
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with open(os.path.join(args.results_folder, f"links.json"), 'w') as jf:
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json.dump(document_edges, jf, ensure_ascii=False, indent=" ")
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def main(args):
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with open(args.solar_file, 'r') as fp:
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logging.info(args.solar_file)
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nlp = classla.Pipeline('sl', pos_use_lexicon=True, pos_lemma_pretag=False, tokenize_pretokenized="conllu", type='standard_jos')
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et = ElementTree.XML(fp.read())
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process_file(et, args, nlp)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description='Read already processed xmls, erase entries without examples and limit gigafida examples to 1 per entry.')
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parser.add_argument('--solar_file', default='data/Solar2.0/solar2.xml',
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help='input file in (gz or xml currently). If none, then just database is loaded')
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parser.add_argument('--svala_folder', default='data/solar.svala.error.small',
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help='input file in (gz or xml currently). If none, then just database is loaded')
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parser.add_argument('--results_folder', default='data/results/solar3.0',
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help='input file in (gz or xml currently). If none, then just database is loaded')
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args = parser.parse_args()
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start = time.time()
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main(args)
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logging.info("TIME: {}".format(time.time() - start))
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