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import json
import os
from pathlib import Path
import re
import sys
from urllib.request import urlretrieve
import fasttext
import tqdm
LANG_THRESHOLD = 0.1
FASTTEXT_MODEL_URL = (
"https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin"
)
JSON_SCHEMA_KEYWORDS = {
"$anchor",
"$comment",
"$defs",
"$dynamicAnchor",
"$dynamicRef",
"$id",
"$recursiveAnchor",
"$recursiveRef",
"$ref",
"$schema",
"$vocabulary",
"additionalItems",
"additionalProperties",
"allOf",
"anyOf",
"const",
"contains",
"contentEncoding",
"contentMediaType",
"contentSchema",
"definitions",
"dependencies",
"dependentRequired",
"dependentSchemas",
"description",
"disallow",
"divisibleBy",
"else",
"enum",
"exclusiveMaximum",
"exclusiveMinimum",
"extends",
"format",
"id",
"if",
"items",
"maxContains",
"maximum",
"maxItems",
"maxLength",
"maxProperties",
"minContains",
"minimum",
"minItems",
"minLength",
"minProperties",
"multipleOf",
"not",
"oneOf",
"pattern",
"patternProperties",
"prefixItems",
"properties",
"propertyNames",
"required",
"then",
"title",
"type",
"unevaluatedItems",
"unevaluatedProperties",
"uniqueItems",
}
IGNORE_KEYWORDS = {
"$id",
"$schema",
"$vocabulary",
"format",
"pattern",
"type",
}
# Adapted from https://stackoverflow.com/a/37697078/123695
def identifier_split(id_str):
return id_str
return " ".join(
re.sub("([A-Z][a-z]+)", r"_\1", re.sub("([A-Z]+)", r"_\1", id_str)).split("_")
)
def collect_text(schema):
"""Generate a string of text from a schema, ignoring keywords"""
text = ""
if isinstance(schema, dict):
for k, v in schema.items():
# Ignore some keywords completely
if k in IGNORE_KEYWORDS:
continue
# If the key is not a keyword, include it
if k not in JSON_SCHEMA_KEYWORDS:
text += " " + identifier_split(k)
text += collect_text(v)
elif isinstance(schema, list):
text += " ".join(collect_text(v) for v in schema)
elif isinstance(schema, str):
# Include any found string values
text += " " + schema
return text.replace("\n", " ")
def get_languages(text):
return {l.split("_")[-1]: p for (l, p) in zip(*model.predict(text, k=5))}
if __name__ == "__main__":
# Download the language model if needed
if not os.path.isfile("lid.176.bin"):
urlretrieve(FASTTEXT_MODEL_URL, "lid.176.bin")
model = fasttext.load_model("lid.176.bin")
files = list(Path("valid_data").rglob("*.json"))
for f in tqdm.tqdm(files):
if not f.is_file():
continue
schema = json.load(f.open(encoding="utf-8"))
schema_str = collect_text(schema)
langs = get_languages(schema_str)
top_lang, prob = max(langs.items(), key=lambda x: x[1])
if prob < LANG_THRESHOLD:
top_lang = None
obj = {
"repository": "/".join(f.parts[1:3]),
"commit": f.parts[3],
"path": str(Path(*f.parts[4:])),
"language": top_lang,
"languages": langs,
}
json.dump(obj, sys.stdout)
sys.stdout.write("\n")
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