diff --git a/code/classes_def_2.py b/code/classes_def_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..20aee54530ce61beb3f5c45d90df9f86d81bf654
--- /dev/null
+++ b/code/classes_def_2.py
@@ -0,0 +1,57 @@
+# Classes for discut22
+
+
+
+class Data:
+ def __init__(self, infos, stamp):
+ self.name = infos['name']
+ self.lang = infos['language']
+ self.path = f"../data/{self.name}"
+ self.exte = infos['exte']
+ self.stamp = stamp
+ self.conv = f"{self.path}/data_converted_{stamp}" # à intégrer
+ self.resu = f"{self.path}/results_{stamp}"
+ self.meta = infos['existing_metadata']
+
+
+class Process:
+ def __init__(self, infos):
+ self.main = infos["main"] # train test annotation
+
+ self.pre_process_to_do = infos['pre-processing']['to_do']
+ self.synt_tool = infos['pre-processing']['syntactic_tool']
+ self.synt_parse = infos['pre-processing']['syntactic_parsing']
+ self.toke = infos['pre-processing']['tokenization']
+ self.ssplit = infos['pre-processing']['sentence_split']
+ self.crea_meta = infos['pre-processing']['create_metadata']['to_do']
+ self.meta_line = infos['pre-processing']['create_metadata']['line']
+ self.meta_sent = infos['pre-processing']['create_metadata']['sent']
+
+ #self.ner_init = infos['pre-processing']['NER_format_initialisation'] # useless because done anyway
+
+ #if self.main == "train":
+ #if self.ner_init == True : # à faire en relatif !! split truc
+ # self.train_data = f"{self.data.path}/{self.data.name}_train.ner{self.data.file}"
+ # self.dev_data = f"{self.data.path}/{self.data.name}_dev.ner{self.data.file}"
+ #else :
+ # self.train_data = infos['discourse_segmenter']['training']['train_data_path']
+ # self.dev_data = infos['discourse_segmenter']['training']['validation_data_path']
+ self.toolkit = infos['discourse_segmenter']['training']['toolkit']
+ self.tr_config = infos['discourse_segmenter']['training']['config_file']
+ self.pretr_lm = infos['discourse_segmenter']['training']['pre_trained_lm']
+
+ self.model = infos['discourse_segmenter']['model'] # ezpz for Tony
+
+ #self.post_tab = infos['post-processing']['json_to_tab']
+
+ self.eval = infos['evaluation']
+ self.test_data = infos['gold_test_data_path']
+
+
+
+class Output:
+ def __init__(self, infos):
+ self.prod_tab = infos['file']['json_to_tab']
+ self.prod_bracket = infos['file']['tab_to_bracket']
+ self.prod_conll = infos['file']['conllu']
+ self.metadata = infos['file']['metadata']
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diff --git a/code/config_global_1.2.json b/code/config_global_1.2.json
new file mode 100644
index 0000000000000000000000000000000000000000..3e93ff20c17c3479d55b7e8cccc84685fe6cd115
--- /dev/null
+++ b/code/config_global_1.2.json
@@ -0,0 +1,48 @@
+{
+ "usecase_description": "Config file for usecase_1 : from a text, get the same text but with EDU bracket.",
+ "data_raw": {
+ "name": "edgar_poe_en",
+ "exte": ".txt",
+ "language": "en",
+ "existing_metadata": false
+ },
+ "steps":{
+ "main": "annotation",
+ "pre-processing": {
+ "to_do": true,
+ "syntactic_tool": "stanza",
+ "sentence_split": true,
+ "tokenization": true,
+ "syntactic_parsing": true,
+ "create_metadata": {
+ "to_do": true,
+ "line": "paragraph",
+ "sent": "sent"
+ }
+ },
+ "discourse_segmenter": {
+ "model": "/home/lriviere/andiamo/discut/Results_conllu/results_eng.rst.gum-eng_bert/model.tar.gz",
+ "training": {
+ "toolkit": null,
+ "pre_trained_lm": null,
+ "config_file": null,
+ "train_data_path": null,
+ "validation_data_path": null
+ }
+ },
+ "evaluation": false,
+ "gold_test_data_path": null
+ },
+ "output":{
+ "file":{
+ "json_to_tab": true,
+ "tab_to_bracket": true,
+ "conllu":true,
+ "metadata": true
+ },
+ "scores":false
+ }
+}
+
+
+
diff --git a/code/discut22_2.py b/code/discut22_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5b10fda0a6fa11b65eb6e221c9d38218fa516af
--- /dev/null
+++ b/code/discut22_2.py
@@ -0,0 +1,110 @@
+######################################
+###### DISCOURSE SEGMENTOR 2022 ######
+######################################
+""" This the main script
+ And the only one to run,
+ after completion of config.json
+ Discut22 uses allennlp toolkit. For that, it need NER intermediary format.
+
+
+
+
+
+ """
+import argparse
+from datetime import datetime
+import os
+import re
+import json
+from classes_def_2 import Data, Process, Output
+import utils_2.syntactic_parsing as synt_pars
+import utils.conv2ner as conv_to_ner
+
+
+
+
+
+def get_stamp():
+ now = datetime.now()
+ stamp = re.sub('[\s:]', '_', str(now))
+ return stamp
+
+def get_config_infos(config, stamp):
+ with open(config, 'r', encoding='utf-8') as f:
+ infos = json.load(f)
+ data = Data(infos['data_raw'], stamp)
+ steps = Process(infos['steps'])
+ prod = Output(infos['output'])
+ my_logs["config"] = infos
+ return data, steps, prod
+
+def create_folders(li):
+ for it in li:
+ if not os.path.isdir(it):
+ os.mkdir(it)
+
+def print_logs():
+ file_logs = f"{data.resu}/processes_logs.json"
+ print(my_logs)
+
+def pre_processing(data, steps):
+ data_in = f"{data.path}/{data.name}{data.exte}"
+ if steps.pre_process_to_do == True:
+ data_out = f"{data.path}/{data.name}.conll"
+ if steps.synt_tool == "stanza":
+ processors = []
+ metadata = {}
+ if steps.toke == True:
+ processors.extend(['tokenize', 'mwt'])
+ if steps.synt_parse == True:
+ processors.extend(['pos', 'lemma', 'depparse'])
+ #if steps.ssplit == True:
+ # processors.append('constituency')
+ if steps.crea_meta == True:
+ metadata['line'] = steps.meta_line
+ metadata['sent'] = steps.meta_sent
+ if data.meta == True:
+ metadata['meta'] = True
+ processors_str = ",".join(processors)
+ synt_pars.with_stanza(data.lang, data_in, data_out, processors_str, metadata)
+ else:
+ exit(f"Exited. Not valid syntactic tool: \"{steps.synt_tool}\". Options: \"stanza\". Change your config file.")
+ else:
+ data_out = data_in
+ my_logs['data_preprocessed'] = data_out
+ return data_out
+
+def data_to_ner_format(data_in):
+ """
+ This fonction build the NER format upon the Segmentor works.
+ INPUT: Tokenized text with whatever number of columns.
+ OUTPUT: Tokenized text with just 4 columns.
+ """
+ data_ner = f"{data_in}.ner"
+ conv_to_ner.main(data_in, data_ner, "conll")
+
+ #TODO add same for train/dev/test for config train
+
+ my_logs['data_ner'] = data_ner
+ return data_ner
+
+
+
+if __name__ == '__main__':
+ my_logs = {}
+ stamp = get_stamp()
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--config', help='Config file in JSON.')
+ parser.add_argument('--name',default=stamp , help='Run name.')
+ args = parser.parse_args()
+ config = args.config
+ stamp = args.name
+ my_logs["stamp"] = stamp
+
+ data, steps, prod = get_config_infos(config, stamp)
+ create_folders([data.conv, data.resu])
+
+ data_preprocessed = pre_processing(data, steps)
+ data_ner = data_to_ner_format(data_preprocessed)
+
+ #print_logs()
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