arubenruben
commited on
Commit
•
498a422
1
Parent(s):
7a9fdf2
Add Loading Script
Browse files- README.md +617 -0
- mathematics_dataset/mathematics_dataset/ds.zip +3 -0
- portuguese-math-dataset.py +276 -0
README.md
CHANGED
@@ -1,5 +1,622 @@
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---
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license: mit
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3 |
---
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4 |
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5 |
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1 |
---
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2 |
license: mit
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3 |
+
dataset_info:
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|
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---
|
621 |
|
622 |
|
mathematics_dataset/mathematics_dataset/ds.zip
ADDED
@@ -0,0 +1,3 @@
|
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|
|
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|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:a3e513877e85246627c135bcc6d0aa35ee84132cde51b3ad6f32b13517057cc4
|
3 |
+
size 98096
|
portuguese-math-dataset.py
ADDED
@@ -0,0 +1,276 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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+
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# Lint as: python3
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+
"""Mathematics database."""
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+
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+
import datasets
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+
import os
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+
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+
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+
logger = datasets.logging.get_logger(__name__)
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+
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+
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+
_CITATION = """
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+
@article{2019arXiv,
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author = {Saxton, Grefenstette, Hill, Kohli},
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+
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
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+
year = {2019},
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+
journal = {arXiv:1904.01557}
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+
}
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+
"""
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+
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+
_DESCRIPTION = """
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+
Mathematics database.
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+
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+
This dataset code generates mathematical question and answer pairs,
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+
from a range of question types at roughly school-level difficulty.
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+
This is designed to test the mathematical learning and algebraic
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+
reasoning skills of learning models.
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+
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+
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
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+
(Saxton, Grefenstette, Hill, Kohli).
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+
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+
Example usage:
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+
train_examples, val_examples = datasets.load_dataset(
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+
'math_dataset/arithmetic__mul',
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+
split=['train', 'test'],
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+
as_supervised=True)
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+
"""
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+
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+
_DATA_URL = os.path.join('mathematics_dataset', 'mathematics_dataset', 'ds.zip')
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+
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+
_TRAIN_CATEGORY = [
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+
"train-easy",
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+
"train-medium",
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+
"train-hard",
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+
]
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+
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+
_INTERPOLATE_CATEGORY = [
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+
"interpolate",
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+
]
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+
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+
_MODULES = [
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+
# extrapolate
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+
"measurement__conversion",
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+
# interpolate
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+
"algebra__linear_1d",
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+
"algebra__linear_1d_composed",
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+
"algebra__linear_2d",
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+
"algebra__linear_2d_composed",
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+
"algebra__polynomial_roots",
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+
"algebra__polynomial_roots_composed",
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+
"algebra__sequence_next_term",
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+
"algebra__sequence_nth_term",
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+
"arithmetic__add_or_sub",
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78 |
+
"arithmetic__add_or_sub_in_base",
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79 |
+
"arithmetic__add_sub_multiple",
|
80 |
+
"arithmetic__div",
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+
"arithmetic__mixed",
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82 |
+
"arithmetic__mul",
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+
"arithmetic__mul_div_multiple",
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84 |
+
"arithmetic__nearest_integer_root",
|
85 |
+
"arithmetic__simplify_surd",
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86 |
+
"calculus__differentiate",
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87 |
+
"calculus__differentiate_composed",
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88 |
+
"comparison__closest",
|
89 |
+
"comparison__closest_composed",
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+
"comparison__kth_biggest",
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91 |
+
"comparison__kth_biggest_composed",
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+
"comparison__pair",
|
93 |
+
"comparison__pair_composed",
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94 |
+
"comparison__sort",
|
95 |
+
"comparison__sort_composed",
|
96 |
+
"measurement__conversion",
|
97 |
+
"measurement__time",
|
98 |
+
"numbers__base_conversion",
|
99 |
+
"numbers__div_remainder",
|
100 |
+
"numbers__div_remainder_composed",
|
101 |
+
"numbers__gcd",
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102 |
+
"numbers__gcd_composed",
|
103 |
+
"numbers__is_factor",
|
104 |
+
"numbers__is_factor_composed",
|
105 |
+
"numbers__is_prime",
|
106 |
+
"numbers__is_prime_composed",
|
107 |
+
"numbers__lcm",
|
108 |
+
"numbers__lcm_composed",
|
109 |
+
"numbers__list_prime_factors",
|
110 |
+
"numbers__list_prime_factors_composed",
|
111 |
+
"numbers__place_value",
|
112 |
+
"numbers__place_value_composed",
|
113 |
+
"numbers__round_number",
|
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+
"numbers__round_number_composed",
|
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+
"polynomials__add",
|
116 |
+
"polynomials__coefficient_named",
|
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+
"polynomials__collect",
|
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+
"polynomials__compose",
|
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+
"polynomials__evaluate",
|
120 |
+
"polynomials__evaluate_composed",
|
121 |
+
"polynomials__expand",
|
122 |
+
"polynomials__simplify_power",
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+
"probability__swr_p_level_set",
|
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+
"probability__swr_p_sequence",
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+
# train-easy train-medium train-hard
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+
"algebra__linear_1d",
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+
"algebra__linear_1d_composed",
|
128 |
+
"algebra__linear_2d",
|
129 |
+
"algebra__linear_2d_composed",
|
130 |
+
"algebra__polynomial_roots",
|
131 |
+
"algebra__polynomial_roots_composed",
|
132 |
+
"algebra__sequence_next_term",
|
133 |
+
"algebra__sequence_nth_term",
|
134 |
+
"arithmetic__add_or_sub",
|
135 |
+
"arithmetic__add_or_sub_in_base",
|
136 |
+
"arithmetic__add_sub_multiple",
|
137 |
+
"arithmetic__div",
|
138 |
+
"arithmetic__mixed",
|
139 |
+
"arithmetic__mul",
|
140 |
+
"arithmetic__mul_div_multiple",
|
141 |
+
"arithmetic__nearest_integer_root",
|
142 |
+
"arithmetic__simplify_surd",
|
143 |
+
"calculus__differentiate",
|
144 |
+
"calculus__differentiate_composed",
|
145 |
+
"comparison__closest",
|
146 |
+
"comparison__closest_composed",
|
147 |
+
"comparison__kth_biggest",
|
148 |
+
"comparison__kth_biggest_composed",
|
149 |
+
"comparison__pair",
|
150 |
+
"comparison__pair_composed",
|
151 |
+
"comparison__sort",
|
152 |
+
"comparison__sort_composed",
|
153 |
+
"measurement__conversion",
|
154 |
+
"measurement__time",
|
155 |
+
"numbers__base_conversion",
|
156 |
+
"numbers__div_remainder",
|
157 |
+
"numbers__div_remainder_composed",
|
158 |
+
"numbers__gcd",
|
159 |
+
"numbers__gcd_composed",
|
160 |
+
"numbers__is_factor",
|
161 |
+
"numbers__is_factor_composed",
|
162 |
+
"numbers__is_prime",
|
163 |
+
"numbers__is_prime_composed",
|
164 |
+
"numbers__lcm",
|
165 |
+
"numbers__lcm_composed",
|
166 |
+
"numbers__list_prime_factors",
|
167 |
+
"numbers__list_prime_factors_composed",
|
168 |
+
"numbers__place_value",
|
169 |
+
"numbers__place_value_composed",
|
170 |
+
"numbers__round_number",
|
171 |
+
"numbers__round_number_composed",
|
172 |
+
"polynomials__add",
|
173 |
+
"polynomials__coefficient_named",
|
174 |
+
"polynomials__collect",
|
175 |
+
"polynomials__compose",
|
176 |
+
"polynomials__evaluate",
|
177 |
+
"polynomials__evaluate_composed",
|
178 |
+
"polynomials__expand",
|
179 |
+
"polynomials__simplify_power",
|
180 |
+
"probability__swr_p_level_set",
|
181 |
+
"probability__swr_p_sequence",
|
182 |
+
]
|
183 |
+
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184 |
+
_QUESTION = "question"
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185 |
+
_ANSWER = "answer"
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186 |
+
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187 |
+
_DATASET_VERSION = "mathematics_dataset-v1.0"
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188 |
+
|
189 |
+
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190 |
+
def _generate_builder_configs():
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+
"""Generate configs with different subsets of mathematics dataset."""
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192 |
+
configs = []
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193 |
+
for module in sorted(set(_MODULES)):
|
194 |
+
configs.append(
|
195 |
+
datasets.BuilderConfig(
|
196 |
+
name=module,
|
197 |
+
version=datasets.Version("1.0.0"),
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198 |
+
description=_DESCRIPTION,
|
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+
)
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200 |
+
)
|
201 |
+
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+
return configs
|
203 |
+
|
204 |
+
|
205 |
+
class PortugueseMathDataset(datasets.GeneratorBasedBuilder):
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+
"""Math Dataset."""
|
207 |
+
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+
BUILDER_CONFIGS = _generate_builder_configs()
|
209 |
+
|
210 |
+
def _info(self):
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+
return datasets.DatasetInfo(
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+
description=_DESCRIPTION,
|
213 |
+
features=datasets.Features(
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214 |
+
{
|
215 |
+
_QUESTION: datasets.Value("string"),
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216 |
+
_ANSWER: datasets.Value("string"),
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217 |
+
}
|
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+
),
|
219 |
+
supervised_keys=(_QUESTION, _ANSWER),
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220 |
+
homepage="https://github.com/deepmind/mathematics_dataset",
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221 |
+
citation=_CITATION,
|
222 |
+
)
|
223 |
+
|
224 |
+
def _get_filepaths_from_categories(self, config, categories):
|
225 |
+
filepaths = []
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226 |
+
for category in categories:
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227 |
+
data_file = "/".join([_DATASET_VERSION, category, config])
|
228 |
+
filepaths.append(data_file)
|
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+
return set(filepaths)
|
230 |
+
|
231 |
+
def _split_generators(self, dl_manager):
|
232 |
+
"""Returns SplitGenerators."""
|
233 |
+
|
234 |
+
archive = dl_manager.download(_DATA_URL)
|
235 |
+
config = self.config.name + ".txt"
|
236 |
+
|
237 |
+
return [
|
238 |
+
datasets.SplitGenerator(
|
239 |
+
name=datasets.Split.TRAIN,
|
240 |
+
gen_kwargs={
|
241 |
+
"files": dl_manager.iter_archive(archive),
|
242 |
+
"config": config,
|
243 |
+
"categories": _TRAIN_CATEGORY,
|
244 |
+
},
|
245 |
+
),
|
246 |
+
datasets.SplitGenerator(
|
247 |
+
name=datasets.Split.TEST,
|
248 |
+
gen_kwargs={
|
249 |
+
"files": dl_manager.iter_archive(archive),
|
250 |
+
"config": config,
|
251 |
+
"categories": _INTERPOLATE_CATEGORY,
|
252 |
+
},
|
253 |
+
),
|
254 |
+
]
|
255 |
+
|
256 |
+
def _generate_examples(self, files, config, categories):
|
257 |
+
"""Yields examples based on directory, module file.."""
|
258 |
+
|
259 |
+
idx = 0
|
260 |
+
filepaths = self._get_filepaths_from_categories(config, categories)
|
261 |
+
for path, f in files:
|
262 |
+
if not filepaths:
|
263 |
+
break
|
264 |
+
elif path in filepaths:
|
265 |
+
for question in f:
|
266 |
+
if not question:
|
267 |
+
continue
|
268 |
+
else:
|
269 |
+
for answer in f:
|
270 |
+
if not answer:
|
271 |
+
continue
|
272 |
+
else:
|
273 |
+
yield idx, {_QUESTION: question, _ANSWER: answer}
|
274 |
+
idx += 1
|
275 |
+
break
|
276 |
+
filepaths.remove(path)
|