# Path: swe_pipeline.py
# def process_file(options, filename, tmp_dir, models, non_capitalized=None):
#     print("Processing %s..." % filename, file=sys.stderr)
# 
#     tokenized_filename = output_filename(tmp_dir, filename, "tok")
#     tagged_filename = output_filename(tmp_dir, filename, "tag")
#     ner_filename = output_filename(tmp_dir, filename, "ne")
# 
#     sentences = run_tokenization(options, filename,
#             non_capitalized=non_capitalized)
#     annotated_sentences = []
# 
#     with open(tokenized_filename, "w", encoding="utf-8") as tokenized, \
#             open(tagged_filename, "w", encoding="utf-8") as tagged, \
#             open(ner_filename, "w", encoding="utf-8") as ner:
# 
#         # Run only one pass over sentences for writing to both files
#         for sentence in sentences:
#             write_to_file(tokenized, sentence)
# 
#             if options.tagged or options.parsed or options.ner:
#                 lemmas, ud_tags_list, suc_tags_list, suc_ne_list = \
#                     run_tagging_and_lemmatization(options, sentence, models)
# 
#                 annotated_sentences.append(
#                     zip(sentence, lemmas, ud_tags_list, suc_tags_list)
#                 )
# 
#                 ud_tag_list = [
#                     ud_tags[:ud_tags.find("|")]
#                     for ud_tags in ud_tags_list
#                 ]
# 
#                 if lemmas and ud_tags_list:
#                     line_tokens = sentence, suc_tags_list, ud_tag_list, lemmas
#                 else:
#                     line_tokens = sentence, suc_tags_list
# 
#                 lines = ["\t".join(line) for line in zip(*line_tokens)]
# 
#                 write_to_file(tagged, lines)
# 
#                 if options.ner:
#                     ner_lines = [
#                         "\t".join(line)
#                         for line in zip(sentence, suc_ne_list)
#                     ]
# 
#                     write_to_file(ner, ner_lines)
# 
#     parsed_filename = ""
#     if options.parsed:
#         parsed_filename = parse(
#             options, filename, annotated_sentences, tmp_dir
#         )
# 
#     write_to_output([
#         (options.tokenized, tokenized_filename, options.output_dir),
#         (options.tagged, tagged_filename, options.output_dir),
#         (options.parsed, parsed_filename, options.output_dir),
#         (options.ner, ner_filename, options.output_dir),
#     ])
# 
#     print("done.", file=sys.stderr)


import os
import sys
import unittest
from swe_pipeline import process_file
from textwrap import dedent
from unittest.mock import patch, MagicMock, mock_open
                ["hej", "min", "namn", "vara"],
                [
                    "INTJ|_",
                    "DET|Definite=Def|Gender=Neut|Number=Sing|Poss=Yes",
                    "NOUN|Case=Nom|Definite=Ind|Gender=Neut|Number=Sing",
                    "AUX|Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act",
                ],
                ["IN", "PS|NEU|SIN|DEF", "NN|NEU|SIN|IND|NOM", "VB|PRS|AKT"],
                [],
            ),
            (
                ["hej", "min", "namn", "vara", "Slim", "Shady"],
                [
                    "INTJ|_",
                    "DET|Definite=Def|Gender=Neut|Number=Sing|Poss=Yes",
                    "NOUN|Case=Nom|Definite=Ind|Gender=Neut|Number=Sing",
                    "AUX|Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act",
                    "PROPN|Case=Nom",
                    "PROPN|Case=Nom",
                ],
                [
                    "IN", "PS|NEU|SIN|DEF", "NN|NEU|SIN|IND|NOM", "VB|PRS|AKT",
                    "PM|NOM", "PM|NOM"
                ],
                [],
            ),
        ]
        with open(os.devnull, 'w') as sys.stderr:
            open_mock = mock_open()
            with patch("swe_pipeline.open", open_mock, create=True):

# Path: swe_pipeline.py
# def process_file(options, filename, tmp_dir, models, non_capitalized=None):
#     print("Processing %s..." % filename, file=sys.stderr)
# 
#     tokenized_filename = output_filename(tmp_dir, filename, "tok")
#     tagged_filename = output_filename(tmp_dir, filename, "tag")
#     ner_filename = output_filename(tmp_dir, filename, "ne")
# 
#     sentences = run_tokenization(options, filename,
#             non_capitalized=non_capitalized)
#     annotated_sentences = []
# 
#     with open(tokenized_filename, "w", encoding="utf-8") as tokenized, \
#             open(tagged_filename, "w", encoding="utf-8") as tagged, \
#             open(ner_filename, "w", encoding="utf-8") as ner:
# 
#         # Run only one pass over sentences for writing to both files
#         for sentence in sentences:
#             write_to_file(tokenized, sentence)
# 
#             if options.tagged or options.parsed or options.ner:
#                 lemmas, ud_tags_list, suc_tags_list, suc_ne_list = \
#                     run_tagging_and_lemmatization(options, sentence, models)
# 
#                 annotated_sentences.append(
#                     zip(sentence, lemmas, ud_tags_list, suc_tags_list)
#                 )
# 
#                 ud_tag_list = [
#                     ud_tags[:ud_tags.find("|")]
#                     for ud_tags in ud_tags_list
#                 ]
# 
#                 if lemmas and ud_tags_list:
#                     line_tokens = sentence, suc_tags_list, ud_tag_list, lemmas
#                 else:
#                     line_tokens = sentence, suc_tags_list
# 
#                 lines = ["\t".join(line) for line in zip(*line_tokens)]
# 
#                 write_to_file(tagged, lines)
# 
#                 if options.ner:
#                     ner_lines = [
#                         "\t".join(line)
#                         for line in zip(sentence, suc_ne_list)
#                     ]
# 
#                     write_to_file(ner, ner_lines)
# 
#     parsed_filename = ""
#     if options.parsed:
#         parsed_filename = parse(
#             options, filename, annotated_sentences, tmp_dir
#         )
# 
#     write_to_output([
#         (options.tokenized, tokenized_filename, options.output_dir),
#         (options.tagged, tagged_filename, options.output_dir),
#         (options.parsed, parsed_filename, options.output_dir),
#         (options.ner, ner_filename, options.output_dir),
#     ])
# 
#     print("done.", file=sys.stderr)


# Path: tests/test_pipeline_process_file.py
import os
import sys
import unittest
from swe_pipeline import process_file
from textwrap import dedent
from unittest.mock import patch, MagicMock, mock_open

                ["hej", "min", "namn", "vara"],
                [
                    "INTJ|_",
                    "DET|Definite=Def|Gender=Neut|Number=Sing|Poss=Yes",
                    "NOUN|Case=Nom|Definite=Ind|Gender=Neut|Number=Sing",
                    "AUX|Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act",
                ],
                ["IN", "PS|NEU|SIN|DEF", "NN|NEU|SIN|IND|NOM", "VB|PRS|AKT"],
                [],
            ),
            (
                ["hej", "min", "namn", "vara", "Slim", "Shady"],
                [
                    "INTJ|_",
                    "DET|Definite=Def|Gender=Neut|Number=Sing|Poss=Yes",
                    "NOUN|Case=Nom|Definite=Ind|Gender=Neut|Number=Sing",
                    "AUX|Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act",
                    "PROPN|Case=Nom",
                    "PROPN|Case=Nom",
                ],
                [
                    "IN", "PS|NEU|SIN|DEF", "NN|NEU|SIN|IND|NOM", "VB|PRS|AKT",
                    "PM|NOM", "PM|NOM"
                ],
                [],
            ),
        ]
        with open(os.devnull, 'w') as sys.stderr:
            open_mock = mock_open()
            with patch("swe_pipeline.open", open_mock, create=True):

                process_file(options, "file.txt", "", models)