"""Mix definition: million words per source for the 10M / 100M / 1000M versions."""

SIZES = {"10M": 0, "100M": 1, "1000M": 2}

# key: (genre, (10M, 100M, 1000M) quota in million words)
MIX = {
    "tinystories":     ("narrative",  (1.5, 8, 40)),
    "simplestories":   ("narrative",  (2.0, 8, 60)),
    "cosmo_stories":   ("narrative",  (0, 5, 50)),
    "tinydialogues":   ("dialogue",   (1.5, 7, 29)),
    "soda":            ("dialogue",   (1, 9, 75)),
    "ultrachat":       ("dialogue",   (0, 4, 61)),
    "smoltalk":        ("dialogue",   (0, 1, 25)),
    "tinyhelen":       ("expository", (1.5, 10, 50)),
    "simplewiki":      ("expository", (1, 10, 30)),
    "enwiki_intro":    ("expository", (0, 0, 50)),
    "cosmo_v2":        ("expository", (1, 12, 180)),
    "tiny_textbooks":  ("expository", (0, 4, 40)),  # uses the synthetic `textbook` column
    "cosmo_v01":       ("expository", (0.5, 3, 30)),
    "fineweb_edu":     ("expository", (0, 9, 180)),
    "gutenberg_books": ("books",      (0, 8, 70)),
    "poetry":          ("poetry",     (0, 1, 10)),
    "cnn":             ("news",       (0, 1, 20)),
}

# Complexity tiers per size: reference vocab size, max Flesch-Kincaid grade,
# document length range (words), max average words per dialogue turn.
TIERS = {
    "10M":   dict(vocab=8000,  fk=5,  fk_exp=9,  doc_max=600,  turn=25),
    "100M":  dict(vocab=15000, fk=8,  fk_exp=10, doc_max=1200, turn=40),
    "1000M": dict(vocab=30000, fk=12, fk_exp=12, doc_max=2000, turn=80),
}
# fk_exp: expository text (Cosmopedia textbooks) has median grade ~11 even for school
# audiences, so fk=5 passed 0.2% of it. Vocabulary coverage still guards simplicity.
# Assistant chats may average this many times the per-turn word cap of a named dialogue.
CHAT_TURN_MULT = 4

# --- run safety nets (see build.py) ---
# Relaxation ladder: (extra Flesch-Kincaid grades, change to vocabulary coverage).
LADDER = [(0, 0.0), (1, -0.02), (2, -0.04), (3, -0.06)]
PROBE_DOCS = 1000   # docs sampled before a source starts, to pick the starting ladder step
WINDOW = 5000       # docs per yield check while a source runs
MAX_SCAN = 40       # hard cap: raw words scanned per source = MAX_SCAN x its quota
# When a source falls short, same-genre substitutes make up the difference in order.
# A substitute may add at most max(EXTRA_FRAC x its quota, EXTRA_FLOOR x total target)
# and never end above SHARE_CAP x total target.
EXTRA_FRAC, EXTRA_FLOOR, SHARE_CAP = 0.5, 0.05, 0.35
FALLBACKS = {
    "tinystories":     ["simplestories", "cosmo_stories"],
    "simplestories":   ["tinystories", "cosmo_stories"],
    "cosmo_stories":   ["simplestories", "tinystories"],
    "tinydialogues":   ["soda", "smoltalk"],
    "soda":            ["tinydialogues", "ultrachat", "smoltalk"],
    "ultrachat":       ["smoltalk", "soda"],
    "smoltalk":        ["ultrachat", "soda"],
    "tinyhelen":       ["tiny_textbooks", "cosmo_v2", "fineweb_edu"],
    "simplewiki":      ["tiny_textbooks", "tinyhelen", "cosmo_v2"],
    "enwiki_intro":    ["cosmo_v2", "tiny_textbooks", "fineweb_edu"],
    "cosmo_v2":        ["tiny_textbooks", "tinyhelen", "fineweb_edu"],
    "cosmo_v01":       ["tiny_textbooks", "cosmo_v2", "tinyhelen"],
    "fineweb_edu":     ["cosmo_v2", "tiny_textbooks", "tinyhelen"],
    "gutenberg_books": ["tinyhelen", "fineweb_edu"],
    "poetry":          [],
    "cnn":             ["fineweb_edu", "cosmo_v2"],
}

# min share of (lowercase) tokens inside the reference vocab. The original 95% @ top-5k
# passed only 3% of SimpleStories and 35% of TinyStories, so it is 90% with larger vocabs.
VOCAB_COVERAGE = 0.90
# English-only by construction and made of one-word toddler turns, which fastText misreads
LID_SKIP = {"tinydialogues"}
DOC_MIN = 80