peppermenta commited on
Commit
0ad83bb
·
1 Parent(s): 78f3a66

Add new table benchmark

Browse files

Still needs some minor fixes

benchmarks/table/scoring.py ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ TEDS Code Adapter from https://github.com/ibm-aur-nlp/EDD
3
+ '''
4
+
5
+ from concurrent.futures import ThreadPoolExecutor, as_completed
6
+ from typing import List
7
+
8
+ from tqdm import tqdm
9
+ import distance
10
+ from apted import APTED, Config
11
+ from apted.helpers import Tree
12
+ from lxml import html
13
+ from collections import deque
14
+ import numpy as np
15
+
16
+ def wrap_table_html(table_html:str)->str:
17
+ return f'<html><body>{table_html}</body></html>'
18
+
19
+ class TableTree(Tree):
20
+ def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
21
+ self.tag = tag
22
+ self.colspan = colspan
23
+ self.rowspan = rowspan
24
+ self.content = content
25
+ self.children = list(children)
26
+
27
+ def bracket(self):
28
+ """Show tree using brackets notation"""
29
+ if self.tag == 'td':
30
+ result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \
31
+ (self.tag, self.colspan, self.rowspan, self.content)
32
+ else:
33
+ result = '"tag": %s' % self.tag
34
+ for child in self.children:
35
+ result += child.bracket()
36
+ return "{{{}}}".format(result)
37
+
38
+ class CustomConfig(Config):
39
+ @staticmethod
40
+ def maximum(*sequences):
41
+ """Get maximum possible value
42
+ """
43
+ return max(map(len, sequences))
44
+
45
+ def normalized_distance(self, *sequences):
46
+ """Get distance from 0 to 1
47
+ """
48
+ return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
49
+
50
+ def rename(self, node1, node2):
51
+ """Compares attributes of trees"""
52
+ if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
53
+ return 1.
54
+ if node1.tag == 'td':
55
+ if node1.content or node2.content:
56
+ return self.normalized_distance(node1.content, node2.content)
57
+ return 0.
58
+
59
+ def tokenize(node):
60
+ ''' Tokenizes table cells
61
+ '''
62
+ global __tokens__
63
+ __tokens__.append('<%s>' % node.tag)
64
+ if node.text is not None:
65
+ __tokens__ += list(node.text)
66
+ for n in node.getchildren():
67
+ tokenize(n)
68
+ if node.tag != 'unk':
69
+ __tokens__.append('</%s>' % node.tag)
70
+ if node.tag != 'td' and node.tail is not None:
71
+ __tokens__ += list(node.tail)
72
+
73
+ def tree_convert_html(node, convert_cell=False, parent=None):
74
+ ''' Converts HTML tree to the format required by apted
75
+ '''
76
+ global __tokens__
77
+ if node.tag == 'td':
78
+ if convert_cell:
79
+ __tokens__ = []
80
+ tokenize(node)
81
+ cell = __tokens__[1:-1].copy()
82
+ else:
83
+ cell = []
84
+ new_node = TableTree(node.tag,
85
+ int(node.attrib.get('colspan', '1')),
86
+ int(node.attrib.get('rowspan', '1')),
87
+ cell, *deque())
88
+ else:
89
+ new_node = TableTree(node.tag, None, None, None, *deque())
90
+ if parent is not None:
91
+ parent.children.append(new_node)
92
+ if node.tag != 'td':
93
+ for n in node.getchildren():
94
+ tree_convert_html(n, convert_cell, new_node)
95
+ if parent is None:
96
+ return new_node
97
+
98
+ def similarity_eval_html(pred, true, structure_only=False):
99
+ ''' Computes TEDS score between the prediction and the ground truth of a
100
+ given samples
101
+ '''
102
+ if pred.xpath('body/table') and true.xpath('body/table'):
103
+ pred = pred.xpath('body/table')[0]
104
+ true = true.xpath('body/table')[0]
105
+ n_nodes_pred = len(pred.xpath(".//*"))
106
+ n_nodes_true = len(true.xpath(".//*"))
107
+ tree_pred = tree_convert_html(pred, convert_cell=not structure_only)
108
+ tree_true = tree_convert_html(true, convert_cell=not structure_only)
109
+ n_nodes = max(n_nodes_pred, n_nodes_true)
110
+ distance = APTED(tree_pred, tree_true, CustomConfig()).compute_edit_distance()
111
+ return 1.0 - (float(distance) / n_nodes)
112
+ else:
113
+ return 0.0
114
+
115
+ def TEDS(prediction, ground_truth):
116
+ prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)
117
+ if prediction:
118
+ return similarity_eval_html(
119
+ html.fromstring(prediction),
120
+ html.fromstring(ground_truth)
121
+ )
122
+ else:
123
+ return 0.
124
+
125
+ def batched_TEDS(gts: List[str], preds: List[str], n_jobs:int=16):
126
+ with ThreadPoolExecutor(max_workers=n_jobs) as pool:
127
+ futures = [pool.submit(TEDS, pred, gt) for pred, gt in zip(preds, gts)]
128
+
129
+ teds_scores = []
130
+ for future in futures:
131
+ teds_scores.append(future.result())
132
+
133
+ return teds_scores
benchmarks/table/table.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import time
3
+ import datasets
4
+ from tqdm import tqdm
5
+ import tempfile
6
+ import click
7
+ from tabulate import tabulate
8
+ import json
9
+ from bs4 import BeautifulSoup
10
+
11
+ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS
12
+
13
+ from marker.config.parser import ConfigParser
14
+ from marker.converters.table import TableConverter
15
+ from marker.models import create_model_dict
16
+ from marker.output import save_output
17
+
18
+ from scoring import batched_TEDS
19
+
20
+
21
+ @click.command(help="Benchmark Table to HTML Conversion")
22
+ @click.argument("out_file", type=str)
23
+ @click.option("--dataset", type=str, default="tarun-menta/fintabnet-html-test", help="Dataset to use")
24
+ @click.option("--max", type=int, default=None, help="Max number of tables to process")
25
+ def main(out_file, dataset, max):
26
+ models = create_model_dict()
27
+ config_parser = ConfigParser({})
28
+ start = time.time()
29
+
30
+ converter = TableConverter(
31
+ config=config_parser.generate_config_dict(),
32
+ artifact_dict=models,
33
+ processor_list=config_parser.get_processors(),
34
+ renderer='marker.renderers.html.HTMLRenderer'
35
+ )
36
+
37
+ dataset = datasets.load_dataset(dataset, split='train')
38
+ dataset = dataset.shuffle(seed=0)
39
+
40
+ iterations = len(dataset)
41
+ if max is not None:
42
+ iterations = min(max, len(dataset))
43
+
44
+ results = []
45
+ for i in tqdm(range(iterations), desc='Converting Tables'):
46
+ row = dataset[i]
47
+ table_img = row['highres_table_img']
48
+ with tempfile.NamedTemporaryFile(suffix=".png", mode="wb+") as temp_img_file:
49
+ table_img.save(temp_img_file)
50
+ temp_img_file.seek(0)
51
+ filename = temp_img_file.name
52
+
53
+ marker_table_html = converter(filename).html
54
+ marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
55
+
56
+ #marker wraps the table in <tbody> which fintabnet data doesn't
57
+ marker_table_soup.find('tbody').unwrap()
58
+
59
+ #Fintabnet doesn't use th tags, need to be replaced for fair comparison
60
+ for th_tag in marker_table_soup.find_all('th'):
61
+ th_tag.name = 'td'
62
+
63
+ marker_table_html = str(marker_table_soup)
64
+
65
+ results.append({
66
+ "marker_table": marker_table_html,
67
+ "gt_table": row['orig_html']
68
+ })
69
+
70
+ scores = batched_TEDS([r['gt_table'] for r in results], [r['marker_table'] for r in results])
71
+ for result, score in zip(results, scores):
72
+ result.update({'score': score})
73
+
74
+ avg_score = sum([r["score"] for r in results]) / len(results)
75
+
76
+ total_time = time.time() - start
77
+ print(f"Total time: {time.time() - start}")
78
+ headers = ["Avg score", "Time per table", "Total tables"]
79
+ data = [f"{avg_score:.3f}", f"{total_time / iterations:.3f}", iterations]
80
+ table = tabulate([data], headers=headers, tablefmt="github")
81
+ print(table)
82
+ print("Avg score computed by comparing tabled predicted HTML with original HTML")
83
+
84
+ with open(out_file, "w+") as f:
85
+ json.dump(results, f, indent=2)
86
+
87
+ if __name__ == '__main__':
88
+ main()
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194
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750
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753
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1884
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1885
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5307
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194
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+ description = "APTED algorithm for the Tree Edit Distance"
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+ optional = false
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764
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1905
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1939
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1940
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1941
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1942
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1943
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1944
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1945
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1946
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1947
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1948
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1949
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1950
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1951
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1952
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1953
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1954
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1955
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1956
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1957
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1958
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1959
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1960
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1961
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1962
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1963
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1964
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1965
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1966
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1967
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1968
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
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1979
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1980
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1981
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1982
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1983
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1984
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1985
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1986
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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2009
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
+ {file = "lxml-5.3.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:8dc2c0395bea8254d8daebc76dcf8eb3a95ec2a46fa6fae5eaccee366bfe02ce"},
2029
+ {file = "lxml-5.3.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:6ba0d3dcac281aad8a0e5b14c7ed6f9fa89c8612b47939fc94f80b16e2e9bc83"},
2030
+ {file = "lxml-5.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:6e91cf736959057f7aac7adfc83481e03615a8e8dd5758aa1d95ea69e8931dba"},
2031
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:94d6c3782907b5e40e21cadf94b13b0842ac421192f26b84c45f13f3c9d5dc27"},
2032
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c300306673aa0f3ed5ed9372b21867690a17dba38c68c44b287437c362ce486b"},
2033
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:78d9b952e07aed35fe2e1a7ad26e929595412db48535921c5013edc8aa4a35ce"},
2034
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:01220dca0d066d1349bd6a1726856a78f7929f3878f7e2ee83c296c69495309e"},
2035
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:2d9b8d9177afaef80c53c0a9e30fa252ff3036fb1c6494d427c066a4ce6a282f"},
2036
+ {file = "lxml-5.3.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:20094fc3f21ea0a8669dc4c61ed7fa8263bd37d97d93b90f28fc613371e7a875"},
2037
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:ace2c2326a319a0bb8a8b0e5b570c764962e95818de9f259ce814ee666603f19"},
2038
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:92e67a0be1639c251d21e35fe74df6bcc40cba445c2cda7c4a967656733249e2"},
2039
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd5350b55f9fecddc51385463a4f67a5da829bc741e38cf689f38ec9023f54ab"},
2040
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:4c1fefd7e3d00921c44dc9ca80a775af49698bbfd92ea84498e56acffd4c5469"},
2041
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:71a8dd38fbd2f2319136d4ae855a7078c69c9a38ae06e0c17c73fd70fc6caad8"},
2042
+ {file = "lxml-5.3.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:97acf1e1fd66ab53dacd2c35b319d7e548380c2e9e8c54525c6e76d21b1ae3b1"},
2043
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:68934b242c51eb02907c5b81d138cb977b2129a0a75a8f8b60b01cb8586c7b21"},
2044
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b710bc2b8292966b23a6a0121f7a6c51d45d2347edcc75f016ac123b8054d3f2"},
2045
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18feb4b93302091b1541221196a2155aa296c363fd233814fa11e181adebc52f"},
2046
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:3eb44520c4724c2e1a57c0af33a379eee41792595023f367ba3952a2d96c2aab"},
2047
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:609251a0ca4770e5a8768ff902aa02bf636339c5a93f9349b48eb1f606f7f3e9"},
2048
+ {file = "lxml-5.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:516f491c834eb320d6c843156440fe7fc0d50b33e44387fcec5b02f0bc118a4c"},
2049
+ {file = "lxml-5.3.0.tar.gz", hash = "sha256:4e109ca30d1edec1ac60cdbe341905dc3b8f55b16855e03a54aaf59e51ec8c6f"},
2050
+ ]
2051
+
2052
+ [package.extras]
2053
+ cssselect = ["cssselect (>=0.7)"]
2054
+ html-clean = ["lxml-html-clean"]
2055
+ html5 = ["html5lib"]
2056
+ htmlsoup = ["BeautifulSoup4"]
2057
+ source = ["Cython (>=3.0.11)"]
2058
+
2059
  [[package]]
2060
  name = "markdown-it-py"
2061
  version = "3.0.0"
 
3241
  {file = "psutil-6.1.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:8df0178ba8a9e5bc84fed9cfa61d54601b371fbec5c8eebad27575f1e105c0d4"},
3242
  {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:1924e659d6c19c647e763e78670a05dbb7feaf44a0e9c94bf9e14dfc6ba50468"},
3243
  {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:018aeae2af92d943fdf1da6b58665124897cfc94faa2ca92098838f83e1b1bca"},
3244
+ {file = "psutil-6.1.1-cp27-none-win32.whl", hash = "sha256:6d4281f5bbca041e2292be3380ec56a9413b790579b8e593b1784499d0005dac"},
3245
+ {file = "psutil-6.1.1-cp27-none-win_amd64.whl", hash = "sha256:c777eb75bb33c47377c9af68f30e9f11bc78e0f07fbf907be4a5d70b2fe5f030"},
3246
  {file = "psutil-6.1.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:fc0ed7fe2231a444fc219b9c42d0376e0a9a1a72f16c5cfa0f68d19f1a0663e8"},
3247
  {file = "psutil-6.1.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:0bdd4eab935276290ad3cb718e9809412895ca6b5b334f5a9111ee6d9aff9377"},
3248
  {file = "psutil-6.1.1-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b6e06c20c05fe95a3d7302d74e7097756d4ba1247975ad6905441ae1b5b66003"},
 
5483
  [metadata]
5484
  lock-version = "2.0"
5485
  python-versions = "^3.10"
5486
+ content-hash = "14d7c7764a6152887a987d8e1790e1e271d137ab88f5952e3043a45806334b6e"
pyproject.toml CHANGED
@@ -52,6 +52,9 @@ uvicorn = "^0.32.0"
52
  python-multipart = "^0.0.16"
53
  pytest = "^8.3.3"
54
  pytest-mock = "^3.14.0"
 
 
 
55
 
56
  [tool.poetry.scripts]
57
  marker = "convert:main"
 
52
  python-multipart = "^0.0.16"
53
  pytest = "^8.3.3"
54
  pytest-mock = "^3.14.0"
55
+ apted = "1.0.3"
56
+ distance = "0.1.3"
57
+ lxml = "5.3.0"
58
 
59
  [tool.poetry.scripts]
60
  marker = "convert:main"