"""Golden Harvest plot-report scraper — cross-vendor yield trials.

This is the FIRST source in the seed-mcp corpus with ``data_type:
"trial"`` rather than the per-variety identity records all other
scrapers emit. Each document is one head-to-head yield trial at a
specific state/year/site, comparing products across brands (NK,
DEKALB, Golden Harvest, sometimes Pioneer/Channel etc. listed as
competitor entries) — i.e. **third-party-feeling cross-vendor data
that Bayer doesn't publish itself**.

Source: ``goldenharvestseeds.com`` — same site as ``golden_harvest``
variety scraper. ``/sitemap-ghs-hybrids.xml`` (already walked for
the variety scraper) lists 8,237 plot reports across:

  Year   Corn    Soy   Silage   Total
  2023  1,832  1,614     173    3,619
  2024  1,432  1,277     137    2,846
  2025    973    703      96    1,772

Initial scrape: 2024 + 2025 (4,618 reports). 2023 is older data
that's still informative but lower priority. Defer 2023 to a later
backfill pass via ``--include-2023``.

URL shape:
  /<crop>/plot-report/<state-abbrev>/<year>/<plot-id>
  e.g. /corn/plot-report/al/2023/2374765

Per-report data (server-rendered HTML):
  - Cooperator name (h1 area)
  - State (full name, e.g. "Alabama")
  - Planted date / Harvested date
  - Population (seeds/acre), Row Width
  - One <table> with columns:
    Rank | Brand | Product | Traits | Yield (BU/Acre) | %MST |
    Test Weight | Gross Revenue | Entry #

Each row in the results table can be from any seed brand — the
trial is the test, not the catalog. Brand and product are the join
keys back to the per-variety corpus (lookup_variety can pull the
identity record if we have the same brand/product).

Output:
  corpus/gh_plot_reports/<source_key>.md      LLM-visible body
  corpus/gh_plot_reports/<source_key>.json    sidecar metadata

source_key convention: ``ghpr-<crop>-<state>-<year>-<plot_id>``
e.g. ``ghpr-corn-al-2023-2374765``.

CLI:
  python -m scrape.sources.gh_plot_reports --limit 5
  python -m scrape.sources.gh_plot_reports --crop corn --state ia --year 2024
  python -m scrape.sources.gh_plot_reports --include-2023 --force
"""

from __future__ import annotations

import argparse
import json
import logging
import os
import random
import re
import sys
import time
from dataclasses import dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

import requests
from bs4 import BeautifulSoup

SCRAPER_VERSION = "0.1.0"
USER_AGENT = "seed-mcp-scraper/0.1 (+https://drawbar.example/contact)"
BASE = "https://www.goldenharvestseeds.com"
SITEMAP_HYBRIDS = f"{BASE}/sitemap-ghs-hybrids.xml"

REPO_ROOT = Path(__file__).resolve().parents[2]
CORPUS_ROOT = Path(os.environ.get("CORPUS_ROOT") or REPO_ROOT / "corpus")
CORPUS_DIR = CORPUS_ROOT / "gh_plot_reports"

REQ_INTERVAL_SEC = 1.0

log = logging.getLogger("scrape.gh_plot_reports")

# State name normalization: URL gives a 2-letter abbrev; sidecar keeps
# both forms so search filters can use either.
STATE_NAMES = {
    "al": "Alabama", "ak": "Alaska", "az": "Arizona", "ar": "Arkansas",
    "ca": "California", "co": "Colorado", "ct": "Connecticut",
    "de": "Delaware", "fl": "Florida", "ga": "Georgia", "hi": "Hawaii",
    "id": "Idaho", "il": "Illinois", "in": "Indiana", "ia": "Iowa",
    "ks": "Kansas", "ky": "Kentucky", "la": "Louisiana", "me": "Maine",
    "md": "Maryland", "ma": "Massachusetts", "mi": "Michigan",
    "mn": "Minnesota", "ms": "Mississippi", "mo": "Missouri",
    "mt": "Montana", "ne": "Nebraska", "nv": "Nevada", "nh": "New Hampshire",
    "nj": "New Jersey", "nm": "New Mexico", "ny": "New York",
    "nc": "North Carolina", "nd": "North Dakota", "oh": "Ohio",
    "ok": "Oklahoma", "or": "Oregon", "pa": "Pennsylvania",
    "ri": "Rhode Island", "sc": "South Carolina", "sd": "South Dakota",
    "tn": "Tennessee", "tx": "Texas", "ut": "Utah", "vt": "Vermont",
    "va": "Virginia", "wa": "Washington", "wv": "West Virginia",
    "wi": "Wisconsin", "wy": "Wyoming",
}


# --------------------------------------------------------------------- HTTP


class RateLimitedSession:
    def __init__(self, interval: float = REQ_INTERVAL_SEC) -> None:
        self.s = requests.Session()
        self.s.headers["User-Agent"] = USER_AGENT
        self.interval = interval
        self._last = 0.0

    def _wait(self) -> None:
        delta = time.monotonic() - self._last
        if delta < self.interval:
            time.sleep(self.interval - delta)
        self._last = time.monotonic()

    def request(
        self,
        method: str,
        url: str,
        *,
        max_retries: int = 4,
        timeout: float = 30.0,
        **kw: Any,
    ) -> requests.Response:
        last_exc: Exception | None = None
        for attempt in range(max_retries):
            self._wait()
            try:
                resp = self.s.request(method, url, timeout=timeout, **kw)
            except requests.RequestException as exc:
                last_exc = exc
                backoff = min(30.0, (2 ** attempt) + random.random())
                log.warning("network error on %s %s: %s — retry in %.1fs",
                            method, url, exc, backoff)
                time.sleep(backoff)
                continue
            if resp.status_code == 429 or 500 <= resp.status_code < 600:
                ra = resp.headers.get("Retry-After")
                backoff = float(ra) if (ra and ra.isdigit()) else min(30.0, (2 ** attempt) + random.random())
                log.warning("HTTP %d on %s %s — retry in %.1fs",
                            resp.status_code, method, url, backoff)
                time.sleep(backoff)
                continue
            return resp
        if last_exc:
            raise last_exc
        return resp  # type: ignore[return-value]

    def get(self, url: str, **kw: Any) -> requests.Response:
        return self.request("GET", url, **kw)


# --------------------------------------------------------------------- model


@dataclass
class TrialResult:
    rank: int | None = None
    brand: str = ""
    product: str = ""
    traits: str = ""
    # Generic per-column metrics — keyed by the header from the table
    # (e.g. "Yield" / "%MST" / "Ton/Acre" / "Milk Per Acre" /
    # "Beef Per Ton"). Corn + soy use Yield/MST/Test Weight/Gross
    # Revenue; silage uses Ton/Acre + Milk + Beef columns. Storing as
    # an open dict keeps the scraper robust across crop types.
    metrics: dict[str, float | str | None] = field(default_factory=dict)
    entry_num: int | None = None

    # Convenience accessors — back-compat for the chunker that looks
    # up these specific keys.
    @property
    def yield_bu_ac(self) -> float | None:
        v = self.metrics.get("Yield")
        return v if isinstance(v, (int, float)) else None

    @property
    def mst_pct(self) -> float | None:
        v = self.metrics.get("%MST")
        return v if isinstance(v, (int, float)) else None

    @property
    def test_weight(self) -> float | None:
        v = self.metrics.get("Test Weight")
        return v if isinstance(v, (int, float)) else None

    @property
    def gross_revenue_dol_ac(self) -> float | None:
        v = self.metrics.get("Gross Revenue")
        return v if isinstance(v, (int, float)) else None

    @property
    def primary_metric(self) -> tuple[str, float | None]:
        """The first numeric metric — used as the canonical 'yield'
        for ranking in the chunk preamble. Corn/soy: Yield (BU/Ac).
        Silage: Ton/Acre."""
        for k in ("Yield", "Ton/Acre", "Tons/Acre"):
            v = self.metrics.get(k)
            if isinstance(v, (int, float)):
                return (k, v)
        # Fallback to first numeric metric
        for k, v in self.metrics.items():
            if isinstance(v, (int, float)):
                return (k, v)
        return ("", None)


@dataclass
class PlotReport:
    source_key: str
    source_url: str
    crop: str           # "corn" / "soybeans" / "silage"
    state_abbrev: str   # "al"
    state_name: str     # "Alabama"
    year: int
    plot_id: str

    cooperator: str | None = None
    planted_date: str | None = None    # ISO date
    harvested_date: str | None = None  # ISO date
    population: int | None = None
    row_width: int | None = None

    results: list[TrialResult] = field(default_factory=list)


# --------------------------------------------------------------------- discovery


_PLOT_URL_RE = re.compile(
    r".*?/(?P<crop>corn|soybean|silage)/plot-report/"
    r"(?P<state>[a-z]{2})/(?P<year>\d{4})/(?P<plot>\d+)"
)


def discover_plots(
    http: RateLimitedSession,
    *,
    crops: set[str],
    states: set[str] | None,
    years: set[int],
) -> list[tuple[str, str, str, int, str]]:
    """Walk the hybrids sitemap and return matching plot URLs as
    ``[(url, crop, state, year, plot_id), ...]`` tuples. ``crop`` is
    normalized to the schema's terms (soybean → soybeans)."""
    log.info("fetching sitemap %s", SITEMAP_HYBRIDS)
    r = http.get(SITEMAP_HYBRIDS)
    r.raise_for_status()
    entries = re.findall(r"<loc>([^<]+)</loc>", r.text)
    log.info("sitemap parsed: %d total locs", len(entries))

    out: list[tuple[str, str, str, int, str]] = []
    for url in entries:
        m = _PLOT_URL_RE.match(url)
        if not m:
            continue
        crop_url = m.group("crop")
        # Normalize "soybean" → "soybeans" to match the rest of the corpus.
        crop = "soybeans" if crop_url == "soybean" else crop_url
        state = m.group("state").lower()
        year = int(m.group("year"))
        plot = m.group("plot")
        if crops and crop not in crops:
            continue
        if states and state not in states:
            continue
        if years and year not in years:
            continue
        out.append((url, crop, state, year, plot))

    log.info("after filters: %d plot URLs", len(out))
    return out


# --------------------------------------------------------------------- helpers


def source_key_for(crop: str, state: str, year: int, plot_id: str) -> str:
    return f"ghpr-{crop}-{state}-{year}-{plot_id}"


def _parse_date_mdy(s: str) -> str | None:
    """``04/06/23`` → ``2023-04-06``. Two-digit years are assumed to
    be 20xx (sane for current-century trial data)."""
    s = (s or "").strip()
    m = re.match(r"^(\d{1,2})/(\d{1,2})/(\d{2,4})$", s)
    if not m:
        return None
    mo, dy, yr = m.group(1), m.group(2), m.group(3)
    if len(yr) == 2:
        yr = "20" + yr
    try:
        return f"{int(yr):04d}-{int(mo):02d}-{int(dy):02d}"
    except ValueError:
        return None


def _parse_int(s: str | None) -> int | None:
    if not s:
        return None
    s = re.sub(r"[,$]", "", str(s).strip())
    try:
        return int(s)
    except ValueError:
        return None


def _parse_float(s: str | None) -> float | None:
    if not s:
        return None
    s = re.sub(r"[,$]", "", str(s).strip())
    try:
        return float(s)
    except ValueError:
        return None


# --------------------------------------------------------------------- detail


def fetch_plot_detail(
    http: RateLimitedSession,
    url: str,
    crop: str,
    state: str,
    year: int,
    plot_id: str,
) -> PlotReport | None:
    """Fetch one plot-report page and parse it."""
    r = http.get(url)
    if r.status_code == 404:
        return None
    r.raise_for_status()
    soup = BeautifulSoup(r.text, "html.parser")

    prod = PlotReport(
        source_key=source_key_for(crop, state, year, plot_id),
        source_url=url,
        crop=crop,
        state_abbrev=state,
        state_name=STATE_NAMES.get(state, state.upper()),
        year=year,
        plot_id=plot_id,
    )

    # Pull metadata from the header area. The page renders cooperator
    # name + state + key fields as text following the h1.
    h1 = soup.find("h1")
    if h1:
        # Walk up to a parent that includes the metadata strip
        container = h1.parent
        while container is not None and not container.find("table"):
            parent = container.parent
            if parent is None:
                break
            container = parent
        if container:
            text = container.get_text(" | ", strip=True)
            # Cooperator is usually the segment right after the H1.
            # Pattern: "Corn Plot Results | <Name> | <State> | Planted: | ..."
            parts = [p.strip() for p in text.split("|") if p.strip()]
            # Drop the title segment
            if parts and parts[0].lower().startswith(("corn plot", "soybean plot", "silage plot")):
                parts = parts[1:]
            if parts:
                # First segment that doesn't match a state name is the cooperator
                cand = parts[0]
                if cand and cand != prod.state_name and not cand.endswith(":"):
                    prod.cooperator = cand

    # Walk the page text for known labeled fields.
    page_text = soup.get_text(" ", strip=True)
    m = re.search(r"Planted:\s*(\d{1,2}/\d{1,2}/\d{2,4})", page_text)
    if m:
        prod.planted_date = _parse_date_mdy(m.group(1))
    m = re.search(r"Harvested:\s*(\d{1,2}/\d{1,2}/\d{2,4})", page_text)
    if m:
        prod.harvested_date = _parse_date_mdy(m.group(1))
    m = re.search(r"Population:\s*([\d,]+)", page_text)
    if m:
        prod.population = _parse_int(m.group(1))
    m = re.search(r"Row Width:\s*(\d+)", page_text)
    if m:
        prod.row_width = _parse_int(m.group(1))

    # Parse the results table. The HTML uses ONE merged cell for
    # "Brand Product Traits" (despite the header containing all
    # three labels); subsequent cells are Yield, %MST, Test Weight,
    # Gross Revenue, Entry #. We split the merged cell using a
    # known-brand prefix match.
    table = soup.find("table")
    if not table:
        return prod
    rows = table.find_all("tr")
    if not rows:
        return prod

    header_cells = [c.get_text(" ", strip=True) for c in rows[0].find_all(["th", "td"])]

    def col_idx(*names: str) -> int | None:
        for n in names:
            for i, h in enumerate(header_cells):
                if n.lower() in h.lower():
                    return i
        return None

    # Position of the merged identity cell, by header containing "Brand".
    i_identity = col_idx("Brand")
    i_rank = col_idx("Rank")
    i_entry = col_idx("Entry")

    # Build a list of (header, index) for the OTHER columns (the
    # metric columns). Skips Rank, Brand-merge-cell, and Entry #.
    metric_columns: list[tuple[str, int]] = []
    skip_idx = {i_identity, i_rank, i_entry}
    for i, h in enumerate(header_cells):
        if i in skip_idx:
            continue
        h_clean = h.strip()
        if h_clean:
            metric_columns.append((h_clean, i))

    for row in rows[1:]:
        cells = [c.get_text(" ", strip=True) for c in row.find_all(["td", "th"])]
        if len(cells) < 2:
            continue
        def cell(i: int | None) -> str:
            return cells[i] if i is not None and 0 <= i < len(cells) else ""

        identity = cell(i_identity).strip()
        if any(k in identity.lower() for k in ("plot average", "trial average", "average")):
            continue

        brand, product, traits = _split_identity(identity)

        # Collect every metric column verbatim. Numeric where parseable,
        # else preserve the raw string (e.g. "ns" for not-significant).
        metrics: dict[str, float | str | None] = {}
        for h, idx in metric_columns:
            raw = cell(idx).strip()
            if not raw or raw == "-":
                metrics[h] = None
            else:
                f = _parse_float(raw)
                metrics[h] = f if f is not None else raw

        result = TrialResult(
            rank=_parse_int(cell(i_rank)),
            brand=brand,
            product=product,
            traits=traits,
            metrics=metrics,
            entry_num=_parse_int(cell(i_entry)),
        )
        has_data = result.brand or result.product or any(
            v is not None for v in metrics.values()
        )
        if has_data:
            prod.results.append(result)

    return prod


# Known seed brands that can appear in plot-report identity cells.
# Sorted longest-first so multi-word brands match before sub-strings.
_BRAND_NAMES = (
    "Golden Harvest", "WestBred", "AgriPro", "DEKALB", "Pioneer",
    "Channel", "Asgrow", "NK", "Becks", "Beck's", "Brevant",
    "Stine", "Renk", "Wyffels", "LG Seeds", "Croplan", "FS",
    "Local Choice", "Mycogen", "AgriGold", "Hoegemeyer",
)
_BRAND_RE = re.compile(
    r"^(?:" + "|".join(re.escape(b) for b in _BRAND_NAMES) + r")\b",
    re.I,
)


def _split_identity(identity: str) -> tuple[str, str, str]:
    """Split a plot-report identity cell into ``(brand, product, traits)``.

    The HTML emits one merged cell like "NK NK1748-3110 Agrisure ®"
    or "Golden Harvest G16Q82-DV DuracadeViptera™" or just
    "DEKALB DKC65-20". We:

    1. Match the brand against a known-brand list at the start.
    2. The token immediately after the brand is the product.
    3. Anything remaining is the trait stack (free text).
    """
    if not identity:
        return "", "", ""
    s = identity.strip()
    m = _BRAND_RE.match(s)
    if not m:
        # Unknown brand prefix — best-effort: first token is brand,
        # second is product, rest is traits.
        parts = s.split(maxsplit=2)
        if len(parts) == 1:
            return parts[0], "", ""
        if len(parts) == 2:
            return parts[0], parts[1], ""
        return parts[0], parts[1], parts[2]
    brand = m.group(0)
    rest = s[len(brand):].strip()
    parts = rest.split(maxsplit=1)
    product = parts[0] if parts else ""
    traits = parts[1].strip() if len(parts) > 1 else ""
    return brand, product, traits


# --------------------------------------------------------------------- render


def render_markdown(p: PlotReport) -> str:
    crop_label = {
        "corn": "Corn", "soybeans": "Soybean", "silage": "Silage",
    }.get(p.crop, p.crop.title())

    head: list[str] = [
        f"# {crop_label} yield trial — {p.state_name}, {p.year}",
        "",
        f"- **Source:** Golden Harvest plot report (cross-vendor head-to-head)",
        f"- **Crop:** {crop_label}",
        f"- **State:** {p.state_name} ({p.state_abbrev.upper()})",
        f"- **Year:** {p.year}",
        f"- **Plot ID:** {p.plot_id}",
    ]
    if p.cooperator:
        head.append(f"- **Cooperator:** {p.cooperator}")
    if p.planted_date:
        head.append(f"- **Planted:** {p.planted_date}")
    if p.harvested_date:
        head.append(f"- **Harvested:** {p.harvested_date}")
    if p.population:
        head.append(f"- **Population:** {p.population:,} seeds/acre")
    if p.row_width:
        head.append(f"- **Row width:** {p.row_width}\"")
    head.append(f"- **URL:** {p.source_url}")
    head.append("")
    head.append("---")
    head.append("")

    sections: list[str] = []
    if p.results:
        # Discover all metric columns present across results, in
        # first-seen order. This keeps corn (Yield/MST/...) and silage
        # (Ton/Acre/Milk/Beef) using their own header sets.
        metric_keys: list[str] = []
        seen_keys: set[str] = set()
        for r in p.results:
            for k in r.metrics.keys():
                if k not in seen_keys:
                    seen_keys.add(k)
                    metric_keys.append(k)

        sections.append("## Results (top-down by rank)")
        sections.append("")
        header_cells = ["Rank", "Brand", "Product", "Traits"] + metric_keys
        sections.append("| " + " | ".join(header_cells) + " |")
        sections.append("|" + "|".join(["---"] * len(header_cells)) + "|")
        for r in p.results:
            row = [
                str(r.rank) if r.rank is not None else "-",
                r.brand or "-",
                r.product or "-",
                r.traits or "-",
            ]
            for k in metric_keys:
                v = r.metrics.get(k)
                if v is None:
                    row.append("-")
                elif isinstance(v, (int, float)):
                    # Dollar columns rendered with $ prefix
                    if "Revenue" in k or "$" in k:
                        row.append(f"${v:.2f}")
                    else:
                        row.append(str(v))
                else:
                    row.append(str(v))
            sections.append("| " + " | ".join(row) + " |")
        sections.append("")

        # Compact text summary for embedder signal — uses the primary
        # metric (Yield for corn/soy, Ton/Acre for silage).
        top = p.results[: min(5, len(p.results))]
        if top:
            primary_label, _ = top[0].primary_metric
            if primary_label:
                summary = ", ".join(
                    f"{r.product or '?'} ({r.brand or '?'}) {r.primary_metric[1]}"
                    for r in top
                    if r.primary_metric[1] is not None
                )
                if summary:
                    sections.append(f"Top {len(top)} by {primary_label}: {summary}.")
                    sections.append("")

    return "\n".join(head) + "\n".join(sections)


# --------------------------------------------------------------------- write


def write_plot(prod: PlotReport, body_md: str) -> None:
    CORPUS_DIR.mkdir(parents=True, exist_ok=True)
    md_path = CORPUS_DIR / f"{prod.source_key}.md"
    json_path = CORPUS_DIR / f"{prod.source_key}.json"

    md_path.write_text(body_md, encoding="utf-8")
    sidecar = {
        "source": "gh_plot_reports",
        "source_key": prod.source_key,
        "data_type": "trial",
        "vendor": "Syngenta",     # Golden Harvest publishes the trial
        "brand": "Golden Harvest",
        "crop": prod.crop,
        "state": prod.state_name,
        "state_abbrev": prod.state_abbrev,
        "year": prod.year,
        "plot_id": prod.plot_id,
        "cooperator": prod.cooperator,
        "planted_date": prod.planted_date,
        "harvested_date": prod.harvested_date,
        "population_seeds_per_acre": prod.population,
        "row_width_in": prod.row_width,
        "results": [
            {
                "rank": r.rank,
                "brand": r.brand,
                "product": r.product,
                "traits": r.traits,
                # All per-column metrics verbatim. Corn/soy: Yield,
                # %MST, Test Weight, Gross Revenue. Silage: Ton/Acre,
                # Milk Per Acre, Milk Per Ton, Beef Per Acre, Beef Per
                # Ton. (Plus any other column the source publishes.)
                "metrics": r.metrics,
                "entry_num": r.entry_num,
            }
            for r in prod.results
        ],
        "n_results": len(prod.results),
        "source_urls": [prod.source_url],
        "fetched_at": datetime.now(timezone.utc).isoformat(),
        "scraper_version": SCRAPER_VERSION,
    }
    json_path.write_text(
        json.dumps(sidecar, indent=2, ensure_ascii=False) + "\n",
        encoding="utf-8",
    )


# --------------------------------------------------------------------- pipeline


def process_plot(
    http: RateLimitedSession,
    *,
    url: str,
    crop: str,
    state: str,
    year: int,
    plot_id: str,
    force: bool,
) -> tuple[str, PlotReport | None]:
    sk = source_key_for(crop, state, year, plot_id)
    md_path = CORPUS_DIR / f"{sk}.md"
    if md_path.exists() and not force:
        return "skipped", None
    try:
        prod = fetch_plot_detail(http, url, crop, state, year, plot_id)
    except Exception as exc:  # noqa: BLE001
        log.error("detail fetch failed for %s: %s", url, exc)
        return "failed", None
    if prod is None:
        return "missing", None
    body = render_markdown(prod)
    write_plot(prod, body)
    return "written", prod


def run(
    *,
    limit: int | None,
    force: bool,
    only_crop: str | None,
    only_state: str | None,
    only_year: int | None,
    include_2023: bool,
) -> int:
    CORPUS_DIR.mkdir(parents=True, exist_ok=True)
    http = RateLimitedSession()

    crops = {only_crop} if only_crop else {"corn", "soybeans", "silage"}
    states = {only_state} if only_state else None
    if only_year:
        years = {only_year}
    elif include_2023:
        years = {2023, 2024, 2025}
    else:
        years = {2024, 2025}

    targets = discover_plots(http, crops=crops, states=states, years=years)

    counts = {"written": 0, "skipped": 0, "missing": 0, "failed": 0}
    processed = 0
    for url, crop, state, year, plot_id in targets:
        if limit is not None and processed >= limit:
            break
        processed += 1
        status, prod = process_plot(
            http, url=url, crop=crop, state=state, year=year,
            plot_id=plot_id, force=force,
        )
        counts[status] = counts.get(status, 0) + 1
        if prod is not None and processed <= 5 or processed % 100 == 0:
            log.info(
                "[%d/%s] %s %s | results=%d coop=%s",
                processed, str(limit) if limit else len(targets),
                source_key_for(crop, state, year, plot_id), status,
                len(prod.results) if prod else 0,
                (prod.cooperator if prod else "-") or "-",
            )

    log.info(
        "done: processed=%d written=%d skipped=%d missing=%d failed=%d (of %d candidates)",
        processed, counts["written"], counts["skipped"],
        counts["missing"], counts["failed"], len(targets),
    )
    return 0 if counts["failed"] == 0 else 1


# --------------------------------------------------------------------- CLI


def _build_argparser() -> argparse.ArgumentParser:
    p = argparse.ArgumentParser(
        prog="scrape.sources.gh_plot_reports",
        description="Scrape Golden Harvest cross-vendor plot reports (yield trials).",
    )
    p.add_argument("--limit", type=int, default=None,
                   help="Stop after processing N plots (default: all).")
    p.add_argument("--force", action="store_true",
                   help="Re-fetch even if the markdown file already exists.")
    p.add_argument("--crop", default=None,
                   choices=("corn", "soybeans", "silage"),
                   help="Limit to one crop.")
    p.add_argument("--state", default=None,
                   help="Limit to one state (2-letter abbrev: ia, il, ne, ...).")
    p.add_argument("--year", type=int, default=None, choices=(2023, 2024, 2025),
                   help="Limit to one year.")
    p.add_argument("--include-2023", action="store_true",
                   help="Include 2023 plot reports (default: 2024-2025 only).")
    p.add_argument("--log-level", default=os.environ.get("LOG_LEVEL", "INFO"))
    return p


def main(argv: list[str] | None = None) -> int:
    args = _build_argparser().parse_args(argv)
    logging.basicConfig(
        level=args.log_level.upper(),
        format="%(asctime)s %(levelname)s %(name)s %(message)s",
        stream=sys.stderr,
    )
    return run(
        limit=args.limit,
        force=args.force,
        only_crop=args.crop,
        only_state=args.state.lower() if args.state else None,
        only_year=args.year,
        include_2023=args.include_2023,
    )


if __name__ == "__main__":
    sys.exit(main())