Vocab study — noun & adjective flashcards with CEFR level toggles
Add SRS-driven noun and adjective flashcards modeled on the existing verb flashcard flow: - SharedModels/Lexeme — catalog of non-verb vocab, frequency-ranked, with gender for nouns and optional example sentences. Seeded from a bundled vocab_lexemes.json built by Scripts/vocab/build_lexemes.py, which joins frequency.csv + es-en.data from a pinned doozan/spanish_data commit (CC-BY-SA: hermitdave/FrequencyWords + Wiktionary). 1,449 nouns and 600 adjectives, each with Wiktionary-sourced gender and (where available) an example sentence with English translation. - LexemeReviewCard + LexemeReviewStore — cloud-synced SM-2 SRS, keyed by partOfSpeech + lexemeId + drillMode so future drill modes can coexist. - LexemeSessionQueue + LexemePool — parallel to VocabSessionQueue; fresh cards sort by frequency rank. - LexemeStudyGroup — cloud-synced resumable session per (partOfSpeech, drillMode). - NounFlashcardPracticeView + AdjectiveFlashcardPracticeView — same flow as VocabFlashcardPracticeView: English prompt → tap to reveal Spanish → Again/Hard/Good/Easy. Nouns reveal with their article (la taza, el problema) so gender is taught alongside meaning, not as a separate quiz. Example sentence shown when present. CEFR-style level toggles: - LexemeLevel enum (A1/A2/B1/B2/C1+) derived from frequencyRank with standard Spanish-frequency-dictionary cutoffs (250/500/1000/2000). - UserProgress.selectedLexemeLevels — cloud-synced multi-select, defaults to A1+A2 on first launch. - SettingsView gains a "Vocabulary Levels" section with five toggles; the existing "Levels" section is renamed "Verb Levels" for clarity. - Due SRS cards always surface regardless of toggles. Disabling a level only stops new cards from that band entering the pool. PracticeView gets "Nouns" and "Adjectives" rows under "Books". DataLoader: new lexemeDataVersion gate that re-seeds the Lexeme table from vocab_lexemes.json independent of book seeding. project.yml lists the new JSON resource and the existing book_olly-vol2.json (which the previous build was silently excluding because xcodegen rewrote the project from project.yml). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""Build Conjuga/vocab_lexemes.json from doozan/spanish_data.
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Joins doozan's frequency.csv (CC-BY-SA 3.0, OpenSubtitles via FrequencyWords)
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with es-en.data (CC-BY-SA, Wiktionary) into a single bundled JSON catalog of
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the highest-frequency Spanish nouns and adjectives — each row carries the
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lemma, English gloss, gender (for nouns), frequency rank, and an example
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sentence with translation when Wiktionary has one.
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The app's DataLoader.seedLexemesFromCatalog reads this file at startup to
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populate the Lexeme table that powers Noun / Adjective flashcard study.
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Usage:
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python3 build_lexemes.py [--max-nouns N] [--max-adjectives N]
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[--output PATH] [--cache-dir PATH]
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Pinned doozan commit: aeac698949e7b27112056ee8d72f70f853cd1ef9 (2026-05-01)
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import sys
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import urllib.request
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from pathlib import Path
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DOOZAN_COMMIT = "aeac698949e7b27112056ee8d72f70f853cd1ef9"
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BASE_URL = f"https://raw.githubusercontent.com/doozan/spanish_data/{DOOZAN_COMMIT}"
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FILES = {
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"frequency.csv": f"{BASE_URL}/frequency.csv",
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"es-en.data": f"{BASE_URL}/es-en.data",
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}
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# Both frequency.csv and es-en.data use short POS codes (`n`, `adj`); we keep
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# the same codes for the join. The output JSON uses the longer names the
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# app's Lexeme model expects.
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JOIN_POS = {"n", "adj"}
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OUTPUT_POS = {"n": "noun", "adj": "adjective"}
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def fetch(name: str, url: str, cache_dir: Path) -> Path:
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"""Download once; reuse local cache on subsequent runs."""
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cache_dir.mkdir(parents=True, exist_ok=True)
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out = cache_dir / name
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if out.exists() and out.stat().st_size > 0:
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return out
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print(f" downloading {name} ({url}) ...", file=sys.stderr)
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with urllib.request.urlopen(url) as resp, open(out, "wb") as fh:
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fh.write(resp.read())
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return out
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def load_frequency(path: Path, *, keep_pos: set[str]) -> list[dict]:
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"""Read frequency.csv → list of {lemma, pos, rank} for the POSes we care
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about. Rank is the row index (1-based), which matches frequency-descending
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order in the source file."""
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rows: list[dict] = []
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with open(path, encoding="utf-8") as fh:
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reader = csv.DictReader(fh)
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for i, row in enumerate(reader):
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pos = (row.get("pos") or "").strip()
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if pos not in keep_pos:
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continue
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flags = (row.get("flags") or "").strip()
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if "DUPLICATE" in flags or "NOUSAGE" in flags:
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continue
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lemma = (row.get("spanish") or "").strip()
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if not lemma:
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continue
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rows.append({"lemma": lemma, "pos": pos, "rank": i + 1})
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return rows
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def load_es_en(path: Path) -> dict[tuple[str, str], dict]:
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"""Parse es-en.data → {(lemma, pos): {gender, english, exampleES, exampleEN}}.
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A single `_____`-delimited block can hold multiple `pos:` sub-entries
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for the same lemma (e.g. `rojo` is both an adjective ("red") and a
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masculine noun ("a red one"); `mano` has two noun senses with different
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genders). We commit each sub-entry when we see the next `pos:` line, so
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`(lemma, pos)` pairs don't get clobbered by later same-block sub-entries.
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First-sense-wins on duplicate keys, which aligns with Wiktionary listing
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the most-common meaning first.
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"""
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entries: dict[tuple[str, str], dict] = {}
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lemma = pos = gender = english = ex_es = ex_en = None
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next_is_lemma = False
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def commit_subentry() -> None:
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nonlocal pos, gender, english, ex_es, ex_en
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if lemma and pos and english:
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key = (lemma, pos)
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if key not in entries:
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entries[key] = {
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"gender": gender,
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"english": english,
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"exampleES": ex_es,
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"exampleEN": ex_en,
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}
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pos = gender = english = ex_es = ex_en = None
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def reset_entry() -> None:
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nonlocal lemma
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commit_subentry()
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lemma = None
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with open(path, encoding="utf-8") as fh:
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for raw in fh:
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line = raw.rstrip("\n")
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stripped = line.lstrip()
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if stripped == "_____":
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reset_entry()
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next_is_lemma = True
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continue
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if next_is_lemma:
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lemma = stripped
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next_is_lemma = False
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continue
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if stripped.startswith("pos: "):
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# Starting a new sub-entry for the current lemma; commit the
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# previous sub-entry's state before resetting.
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commit_subentry()
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pos = stripped[5:].strip()
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elif stripped.startswith("g: "):
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gender = stripped[3:].strip()
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elif stripped.startswith("gloss: "):
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if english is None:
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english = stripped[7:].strip()
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elif stripped.startswith("ex: "):
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if ex_es is None:
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ex_es = stripped[4:].strip()
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elif stripped.startswith("eng: "):
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if ex_en is None:
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ex_en = stripped[5:].strip()
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reset_entry()
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return entries
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def normalize_gender(g: str | None) -> str | None:
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"""Reduce Wiktionary gender codes to {m, f, m/f, None}.
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`mp` (masculine plural) / `fp` (feminine plural) are inherently-plural
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nouns (gafas, pantalones); they don't fit the singular el/la drill cleanly
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in v1, so we drop them here and the entry is filtered out upstream.
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"""
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if not g:
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return None
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g = g.strip()
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if g in ("m", "f"):
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return g
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if g in ("mf", "m/f", "m, f", "f, m"):
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return "m/f"
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return None
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def build(args) -> None:
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cache = Path(args.cache_dir).expanduser()
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paths = {name: fetch(name, url, cache) for name, url in FILES.items()}
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print(
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f"Reading frequency.csv (top {args.max_nouns} nouns, "
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f"top {args.max_adjectives} adjectives) ...",
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file=sys.stderr,
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)
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rows = load_frequency(paths["frequency.csv"], keep_pos=JOIN_POS)
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nouns = [r for r in rows if r["pos"] == "n"][: args.max_nouns]
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adjs = [r for r in rows if r["pos"] == "adj"][: args.max_adjectives]
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print(f" candidates: {len(nouns)} nouns, {len(adjs)} adjectives", file=sys.stderr)
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print("Parsing es-en.data ...", file=sys.stderr)
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es_en = load_es_en(paths["es-en.data"])
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print(f" {len(es_en)} (lemma, pos) entries", file=sys.stderr)
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out: list[dict] = []
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skipped_no_entry = 0
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skipped_no_english = 0
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skipped_no_gender = 0
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for source_rows in (nouns, adjs):
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for r in source_rows:
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short_pos = r["pos"]
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output_pos = OUTPUT_POS[short_pos]
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entry = es_en.get((r["lemma"], short_pos))
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if not entry:
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skipped_no_entry += 1
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continue
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english = entry.get("english")
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if not english:
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skipped_no_english += 1
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continue
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gender = normalize_gender(entry.get("gender")) if short_pos == "n" else None
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if short_pos == "n" and gender is None:
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# Drill needs gender; if Wiktionary doesn't have it, skip.
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skipped_no_gender += 1
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continue
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out.append({
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"baseForm": r["lemma"],
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"english": english,
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"partOfSpeech": output_pos,
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"gender": gender,
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"frequencyRank": r["rank"],
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"exampleES": entry.get("exampleES"),
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"exampleEN": entry.get("exampleEN"),
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})
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out.sort(key=lambda e: e["frequencyRank"])
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out_path = Path(args.output).expanduser()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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with open(out_path, "w", encoding="utf-8") as fh:
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json.dump(out, fh, ensure_ascii=False, separators=(",", ":"))
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fh.write("\n")
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noun_count = sum(1 for e in out if e["partOfSpeech"] == "noun")
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adj_count = sum(1 for e in out if e["partOfSpeech"] == "adjective")
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print(
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f"Wrote {out_path} — {noun_count} nouns, {adj_count} adjectives "
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f"({len(out)} total, {out_path.stat().st_size:,} bytes)",
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file=sys.stderr,
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)
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print(
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f" skipped: no es-en entry={skipped_no_entry}, "
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f"no english={skipped_no_english}, "
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f"no gender={skipped_no_gender}",
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file=sys.stderr,
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)
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def main() -> None:
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here = Path(__file__).resolve().parent
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument("--max-nouns", type=int, default=1500)
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parser.add_argument("--max-adjectives", type=int, default=600)
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parser.add_argument(
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"--output",
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default=str(here / ".." / ".." / "Conjuga" / "vocab_lexemes.json"),
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)
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parser.add_argument(
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"--cache-dir",
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default=str(here / ".cache" / DOOZAN_COMMIT[:8]),
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)
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build(parser.parse_args())
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if __name__ == "__main__":
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main()
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