chore: import upstream snapshot with attribution

extra/example_data/textcat_example_data/textcatjsonl_to_trainjson.py

@@ -0,0 +1,55 @@
+from pathlib import Path
+import plac
+import spacy
+from spacy.training import docs_to_json
+import srsly
+import sys
+
+
+@plac.annotations(
+    model=("Model name. Defaults to 'en'.", "option", "m", str),
+    input_file=("Input file (jsonl)", "positional", None, Path),
+    output_dir=("Output directory", "positional", None, Path),
+    n_texts=("Number of texts to convert", "option", "t", int),
+)
+def convert(model="en", input_file=None, output_dir=None, n_texts=0):
+    # Load model with tokenizer + sentencizer only
+    nlp = spacy.load(model)
+    nlp.select_pipes(disable=nlp.pipe_names)
+    sentencizer = nlp.create_pipe("sentencizer")
+    nlp.add_pipe(sentencizer, first=True)
+
+    texts = []
+    cats = []
+    count = 0
+
+    if not input_file.exists():
+        print("Input file not found:", input_file)
+        sys.exit(1)
+    else:
+        with open(input_file) as fileh:
+            for line in fileh:
+                data = srsly.json_loads(line)
+                texts.append(data["text"])
+                cats.append(data["cats"])
+
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        if not output_dir.exists():
+            output_dir.mkdir()
+    else:
+        output_dir = Path(".")
+
+    docs = []
+    for i, doc in enumerate(nlp.pipe(texts)):
+        doc.cats = cats[i]
+        docs.append(doc)
+        if n_texts > 0 and count == n_texts:
+            break
+        count += 1
+
+    srsly.write_json(output_dir / input_file.with_suffix(".json"), [docs_to_json(docs)])
+
+
+if __name__ == "__main__":
+    plac.call(convert)