import os
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parent.parent
TEMPLATES_DIR = PROJECT_ROOT / "templates"
STORAGE_DIR = PROJECT_ROOT / "storage"
FRONTEND_DIST = PROJECT_ROOT / "frontend" / "dist"
DB_PATH = STORAGE_DIR / "creator.db"
PROJECTS_DIR = PROJECT_ROOT / "projects"
UNI_DIR = PROJECT_ROOT / "uni"


def _load_env(path: Path) -> None:
    """Mini .env loader (no dependency): KEY=VALUE lines. The FILE wins over inherited
    env: a --reload master keeps its startup environment forever, so "existing env wins"
    silently pinned stale values across .env edits (measured: file said 24, workers
    inherited 15 for hours). Trade-off: ad-hoc shell overrides lose against the file."""
    try:
        text = path.read_text(encoding="utf-8")
    except OSError:
        return
    for line in text.splitlines():
        line = line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        key, _, value = line.partition("=")
        key, value = key.strip(), value.strip().strip('"').strip("'")
        if key:
            os.environ[key] = value


_load_env(PROJECT_ROOT / ".env")

MAX_CONCURRENT_GENERATIONS = 10

# Readability gate: deterministic checker (small German complexity model,
# scale 1–7). Sections that are too hard go into the read-exam revision.
# If transformers/torch or the model are missing → gate silently off.
READABILITY_ACTIVE = True
READABILITY_MODEL = "MiriUll/distilbert-german-text-complexity"
# Anchors on the 1–7 scale (TextComplexityDE): plain language ~1.2; Wikipedia average
# ~3.22; from MOS > 4 a sentence counts as "truly complex" (the paper's simplification cutoff).
READABILITY_MAX = 3.5          # section too hard when the sentence average is above this
READABILITY_HARD = 4.0         # an individual sentence is "hard" from here on
READABILITY_HARD_SHARE = 0.30  # … OR when this share of sentences is hard

# Kanban clustering: semantic embeddings drive the online title clustering and the
# candidate pairs of the pair check. If transformers/torch are missing or the model
# won't load → embedding silently off (all pairs go to the judge, clusters stay singletons).
EMBEDDING_AKTIV = True
EMBEDDING_MODELL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"  # CPU, multilingual, ~470 MB
# Stronger (larger) CPU alternative if needed: "BAAI/bge-m3".
# Consolidation = two-stage: (1) the embedding builds COARSE similarity blocks (high recall),
# (2) an LLM judge groups EACH block into the real blocks (merge paraphrases, split
# over-merges). Pure threshold blocking creates a giant component (everything chained) →
# hence "capped blocking": greedily merge by cosine, but cap the block size. This keeps the
# LLM lists short and stable (evidenced: embedding block + LLM judge ≈ 95% precision).
EMBEDDING_BLOCK_FLOOR = 0.5   # minimum cosine for two candidates to share ONE block
EMBEDDING_BLOCK_CAP = 25      # max. titles per block (keep the LLM list short/stable)
# Subblock dedup: purely deterministic (no LLM). Subblocks are short statements IN THE SAME
# block context — from this cosine on two are the same statement (checked on aak: ≥0.88 are
# without exception true duplicates). Conservative 0.90 so different aspects (∈NP ≠ NP-hard) stay separate.
EMBEDDING_SUB_DUP = 0.90
# Variant folding BEFORE the subblock consensus count: finders rephrase the same concept each
# round, so exact-norm counting starves real concepts (measured Markdown run: 623/965 mentions
# discarded, „Zeichenkodierung" 73/74). 0.90 folds true paraphrases at ~0 false folds (0.85/0.88
# fold distinct aspects like ** vs ***). Antonym pairs measure 0.91–0.95 → negation guard required.
SUB_VARIANT_COS = 0.90
# Seed coverage check is LEXICAL first (token containment) — seeds are short fragment NAMES,
# subs are statements: true covers measure 0.27–0.38 while a wrong hit measured 0.76. The
# embedding stage only backs up the lexical one (catches „Line Breaks (Soft)" 0.888).
SEED_COVER_COS = 0.80

# Umbrella grouping (block granularity level 2, step "Blocks-Gruppierung", AFTER the filter):
# collapse sibling DEFINITIONS that are components of ONE umbrella concept (TM model:
# Konfiguration/Start-/Folge-/Stopkonfiguration/Berechnung/Alphabet/δ; KNF: Literale/Klauseln/
# Variablen) into ONE block whose description ENUMERATES the children — so the later subblock step
# re-derives them from the source under the umbrella scope (demotion is re-derivation, not transfer).
# If the flag is off or no embedding model → step is silently skipped (like Dedup).
BLOCKS_GRUPPIERUNG_AKTIV = True
# Lower floor than consolidation (0.5, paraphrase-tuned) for higher sibling recall; the LLM judge is
# the precision gate. Smaller cap since a lower floor pulls in more nodes → keep the judge lists short.
EMBEDDING_SIBLING_FLOOR = 0.35   # heterogeneous facets of one model co-cluster weakly → low floor (recall)
EMBEDDING_SIBLING_CAP = 18       # a rich model (TM) can have many constituent parts
# Reconcile pass: two independently-judged clusters can emit the SAME parent concept under different
# titles (e.g. two „Turingmaschine"-umbrellas). Merge umbrella pairs whose title+description cosine is
# ≥ this (conservative → only true same-parent duplicates, never two distinct umbrellas).
GROUP_RECONCILE_FLOOR = 0.75
# Over-merge backstop ONLY (no-structure floor). Research (meronymy ≠ similarity): parts of ONE model are
# legitimately DISSIMILAR (TM: Alphabet/Konfiguration/δ ~0.22), while distinct same-type concepts (P/NP/…)
# are SIMILAR (~0.85) — so member-vs-member cosine is the WRONG instrument for over-merge (empirically
# inverted: TM 0.218 < the P/NP bundle 0.227). The real precision floor is the ATOMICITY type-guard
# (_GROUP_STANDALONE: a member that is a named algorithm/problem/theorem/complexity-class dissolves the
# umbrella). This floor is demoted to a near-zero backstop that only rejects a literally structureless
# chain (random-pair baseline), set BELOW the legitimate heterogeneous minimum so it never kills a real model.
GROUP_MIN_COS_FLOOR = 0.15
# Fragment-demote backstop, same logic as GROUP_MIN_COS_FLOOR: fragment↔parent cosine is a BAD
# fragment detector (measured, Markdown run: wrong demotes Blockzitate→Codeblöcke 0.353 and
# Zeichenkodierung→Überschriften 0.640 sit ABOVE any usable floor, while true NP proof-gadget
# demotes αu-Variablen→Cook/Levin 0.172 sit low). So this only vetoes judge/panel demotes with
# NO containment match whose pair is literally structureless (Emoji→Tabelle 0.136).
FRAGMENT_MIN_COS = 0.15

# Caps for concurrent CLI agent processes (env-overridable). Two nested limits, both always active:
# a per-topic cap and a global cap across all topics. Defaults 10/10 = previous behavior (global
# dominates). Locally raise the global cap to actually parallelize across topics (per-topic stays 10).
# Own lane for interactive calls (chat, elements) so they don't hang behind running writers.
MAX_CONCURRENT_AGENTS = int(os.getenv("MAX_CONCURRENT_AGENTS", "16"))                      # global, all topics
MAX_CONCURRENT_AGENTS_PER_TOPIC = int(os.getenv("MAX_CONCURRENT_AGENTS_PER_TOPIC", "12"))  # per topic
MAX_CONCURRENT_INTERACTIVE = 8

# Grace window of the consensus races (blocks, guide, OnePager): after the first
# valid result the remaining agents may still become done for this many seconds
# (kill only once the minimum is already in).
CONSENSUS_GRACE = 300

# Research race: longer grace window. Research drives the whole block count;
# with slow providers (e.g. MiniMax) ALL 5 agents should become done, not just
# the quorum of 3. The per-agent timeout (TIMEOUTS["research"]=1800s) caps real hangs.
RESEARCH_GRACE = 900

# Cap of the clarification and check loops: maximum rounds until everything must be
# decided. In the last round the mapping agent MUST decide every entry;
# check loops leave any remaining objections standing after that.
CONSENSUS_MAX_ROUNDS = 3

# Crawler triage (content/noise) — deterministic rule filter instead of an LLM.
# Match: substring (lowercase) against URL AND file name. Order: keep > noise > min_chars > keep.
# Just add special rules here.
CRAWL_KEEP_PATTERNS = ["learn-unit", "learn-course"]            # always content
CRAWL_NOISE_PATTERNS = [                                         # clearly off-topic → out
    "clubs", "events", "podcasts", "resources", "-u-",
    "academy", "pricing", "/plans", "career", "newsletter", "impressum", "login",
]
CRAWL_MIN_CHARS = 400                                            # too little text → out

# LLM topic relevance gate (after the rule filter): per content page yes/no against the spec.
# Separates the subject area (e.g. backend vs frontend), which the global CRAWL_* rules can't.
QUELLE_RELEVANZ_CHUNK = 12       # pages per rater package (small, since a snippet ships per page)
QUELLE_RELEVANZ_SNIPPET = 800    # body characters per page in the prompt (URL is the primary signal)

# Inline evidence for judge agents: corpus excerpts go INTO the prompt instead of letting
# every judge re-search the source folder (measured: ~10 tool turns/judge, 82 % of the
# run's tokens were cache reads from those loops).
EVIDENCE_BUDGET_CHARS = 48_000   # max excerpt characters per judge prompt
EVIDENCE_CTX_LINES = 15          # context lines around a cited source position (facts check)

# Timeouts per agent step: (base seconds, seconds per block/section).
# Applies equally to all providers — whoever is too slow gets restarted or overtaken.
TIMEOUTS = {
    "research":     (900, 0),    # p95 measured 125 s (web mode); uni/link sections need headroom
    "research_mapping": (600, 3),  # n = pre-merged entries
    "selection_mapping":   (600, 2),  # n = remaining entries (block inventory)
    "ergaenzung":    (600, 0),   # subject-field extension for projects (web research)
    "plan":          (300, 5),
    "plan_judge":    (600, 5),    # judge reads up to 5 outlines, n = sections
    "content":        (450, 30),   # facts find/erg/fix — p95 measured 241 s (was 600+90n)
    "content_check":  (300, 10),   # content exam per block in the package
    "subblock":       (400, 15),  # finder round — p95 measured 124 s (was 900+45n)
    "subblock_check": (300, 15),  # judge decides contested subblocks in the chunk
    "level":             (300, 10),  # classify subblocks per chunk
    "level_check":       (300, 10),  # judge decides contested levels in the chunk
    "relevance":          (300, 10),  # subblocks relevant/peripheral per chunk
    "relevance_check":    (300, 10),  # judge decides contested relevance in the chunk
    "question_pattern":      (300, 15),  # question patterns per block (subblocks × types)
    "question_pattern_check": (300, 10),  # critic cleans up the pattern table per block
    "writer":        (450, 60),   # per section — split keeps sections ≤30 subs
    "lese_check":    (300, 10),   # per section in the package
    # guide board (per card = one block)
    "lernziele":     (300, 5),    # backward-design objectives per block
    "fakten_gate":   (600, 5),    # CoVe claim check per block
    "coverage":      (300, 5),    # objective↔section mapping per block
}

# Purpose per format — flows into the outline judge (what the guide should achieve).
# German strings: these are inserted verbatim into the judge prompt → kept German on purpose.
FORMAT_PURPOSE = {
    "Guide": "einen fokussierten Guide — alles Relevante ohne Randthemen",
    "FullGuide": "einen Komplett-Guide — das ganze Thema inkl. Randthemen",
    "Rest": "einen Ergänzungs-Guide — nur die Randthemen",
}

# Provider stacks: completely independent, any one can be removed at any time.
# Roles: "quick" = bulk work (research, classification),
#        "fast"  = interaction + voting (chat, exam, clarification, elements),
#        "judge" = mapping/judge/check agents — cold (low temperature,
#                  no thinking) for stable verdicts; Claude/local map to "fast",
#        "guide" = large generation (proposals, writer).
DEFAULT_PROVIDER = "claude"
PROVIDERS = {
    "claude": {
        "cli": "claude",
        "guide": "claude-opus-4-8[1m]",
        "fast": "claude-sonnet-4-6",
        "judge": "claude-sonnet-4-6",  # the CLI has no temperature setting
        "quick": "claude-sonnet-4-6",
        "env_key": None,  # auth via CLAUDE_CODE_OAUTH_TOKEN or ~/.claude
    },
    # "minimax-kalt/…" is NOT its own stack, just an opencode provider entry
    # (dev-ops/opencode.json) with low temperature; M3 there without thinking.
    "minimax": {
        "cli": "opencode",
        "guide": "minimax/MiniMax-M3",
        "fast": "minimax-kalt/MiniMax-M2.7-highspeed",
        "judge": "minimax-kalt/MiniMax-M3",
        "quick": "minimax/MiniMax-M2.7-highspeed",
        "env_key": "MINIMAX_API_KEY",
    },
    "lokal": {
        "cli": "opencode",
        "guide": "ollama/qwen3.6:27b",
        "fast": "ollama/qwen3.5:9b",
        "judge": "ollama/qwen3.5:9b",
        "quick": "ollama/qwen3.5:9b",
        "env_key": None,
        "check_url": "http://localhost:11434/api/tags",  # Ollama reachable?
    },
}

# Role routing: by DEFAULT the run's provider (the UI choice) handles ALL roles —
# the role only picks the model WITHIN that stack (PROVIDERS[stack][role]).
# Opt-in cross-provider mixing via env: ROLE_JUDGE=claude routes every judge call
# to the claude stack regardless of the UI choice ("provider:model" pins a model).
ROLE_ROUTING = {
    "quick": os.getenv("ROLE_QUICK", ""),
    "judge": os.getenv("ROLE_JUDGE", ""),
    "guide": os.getenv("ROLE_GUIDE", ""),
    "fast":  os.getenv("ROLE_FAST", ""),
}


def resolve_role(run_provider: str, role: str) -> tuple[str, str]:
    """→ (provider, model) for one agent call. Pure routing, no availability check —
    the caller (agents.run_agent) falls back to run_provider if the target is unavailable."""
    target = ROLE_ROUTING.get(role, "") or run_provider
    provider, _, model = target.partition(":")
    if provider not in PROVIDERS:
        provider, model = run_provider, ""
    if not model:
        model = PROVIDERS.get(provider, {}).get(role, "")
    return provider, model