import pdfplumber
import re


def midpoint(w: dict) -> float:
    return (w["x0"] + w["x1"]) / 2.0

    
# ==========================================
KEYWORDS = ("HSN/SAC:", "Batch:", "Godown:")
TALLY_STOP_WORDS = ("Grand Total", "Closing Stock", "Opening Stock")
NUM_RE = re.compile(r"^-?[\d,]+(\.\d+)?$")
VALID_HEADERS = {"quantity", "qty", "rate", "value", "amount"}
SUPER_HEADERS = {"opening", "inwards", "outwards", "closing"}
CLUSTER_TOL = 3.0


def is_number(t: str) -> bool:
    return bool(NUM_RE.match(t.replace(" ", "")))


def line_text(words: list) -> str:
    return " ".join(w["text"] for w in words)


def has_financial(words: list, dividers: list) -> bool:
    if not dividers: return False
    for w in words:
        if midpoint(w) >= dividers[0]:
            if is_number(w["text"]):
                return True
    return False


def is_keyword_line(words: list) -> bool:
    t = line_text(words)
    return any(k in t for k in KEYWORDS)


def tally_is_stop_line(words: list) -> bool:
    t = line_text(words)
    return any(s in t for s in TALLY_STOP_WORDS)


def build_indent_map(blocks: list) -> dict:
    raw_x0s = [b["x0"] for b in blocks]
    if not raw_x0s:
        return {}
    unique = sorted(set(raw_x0s))
    clusters = []
    for x in unique:
        if clusters and x - clusters[-1] <= CLUSTER_TOL:
            continue
        clusters.append(x)
    indent_map = {}
    for x in raw_x0s:
        level = min(range(len(clusters)), key=lambda i: abs(clusters[i] - x))
        indent_map[x] = level
    return indent_map


def detect_columns(words: list, header_top: float):
    anchor_words = [w for w in words if abs(w["top"] - header_top) < 15 and w["text"].lower() in VALID_HEADERS]
    anchor_words.sort(key=lambda w: midpoint(w))
    super_words = [w for w in words if
                   header_top - 25 < w["top"] < header_top + 5 and w["text"].lower() in SUPER_HEADERS]
    # super_words = [w for w in words if header_top - 25 < w["top"] < header_top - 2 and w["text"].lower() not in VALID_HEADERS and w["text"].lower() != "particulars"]
    super_words.sort(key=lambda w: w["x0"])

    dynamic_cols = []
    for aw in anchor_words:
        prefix = ""
        for i, sw in enumerate(super_words):
            if midpoint(aw) >= sw["x0"] - 20:
                if i + 1 < len(super_words):
                    if midpoint(aw) < super_words[i + 1]["x0"] - 20:
                        prefix = sw["text"] + " "
                        break
                else:
                    prefix = sw["text"] + " "
                    break
        label = (prefix + aw["text"]).strip().title()
        dynamic_cols.append({"label": label, "center": midpoint(aw)})

    if not dynamic_cols:
        dynamic_cols = [
            {"label": "Quantity", "center": 300},
            {"label": "Rate", "center": 380},
            {"label": "Value", "center": 460}
        ]

    dividers = [dynamic_cols[0]["center"] - 30]
    for i in range(len(dynamic_cols) - 1):
        dividers.append((dynamic_cols[i]["center"] + dynamic_cols[i + 1]["center"]) / 2.0)
    return dynamic_cols, dividers


def parse_tally_pdf(pdf_path: str) -> list:
    full_tree = []
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            words = page.extract_words(keep_blank_chars=False)
            if not words: continue

            header_keywords = {"quantity", "qty", "rate", "value", "amount", "particulars"}
            header_candidates = [w["top"] for w in words if w["text"].lower() in header_keywords]
            header_top = min(header_candidates) if header_candidates else 80.0

            columns, dividers = detect_columns(words, header_top)
            lines = {}
            for w in words:
                if w["top"] < header_top + 2: continue
                y = round(w["top"] / 3) * 3
                lines.setdefault(y, []).append(w)

            blocks = []
            current = None

            def commit(blk):
                if blk: blocks.append(blk)

            for y in sorted(lines.keys()):
                lw = sorted(lines[y], key=lambda w: w["x0"])
                if not lw: continue
                if tally_is_stop_line(lw):
                    commit(current)
                    current = None
                    break

                fin = has_financial(lw, dividers)
                kw = is_keyword_line(lw)
                first_x0 = lw[0]["x0"]
                p = []
                col_bins = [[] for _ in columns]

                for w in lw:
                    cx = midpoint(w)
                    if cx < dividers[0]:
                        p.append(w["text"])
                    else:
                        placed = False
                        for i in range(len(dividers) - 1):
                            if cx < dividers[i + 1]:
                                col_bins[i].append(w["text"])
                                placed = True
                                break
                        if not placed:
                            col_bins[-1].append(w["text"])

                if fin or kw:
                    commit(current)
                    current = {"x0": first_x0, "particulars": p, "financials": col_bins}
                else:
                    current_is_anchor = (
                            current is not None and
                            (any(len(bin) > 0 for bin in current["financials"])
                             or is_keyword_line([{"text": t} for t in current["particulars"]]))
                    )
                    same_indent = (current is not None and abs(first_x0 - current["x0"]) <= CLUSTER_TOL)

                    if current_is_anchor and same_indent:
                        current["particulars"].extend([w["text"] for w in lw])
                    else:
                        commit(current)
                        current = {"x0": first_x0, "particulars": p, "financials": [[] for _ in columns]}

            commit(current)
            if not blocks: continue

            indent_map = build_indent_map(blocks)
            page_nodes = []
            hierarchy = []

            for blk in blocks:
                name = " ".join(blk["particulars"]).strip()
                if not name: continue
                level = indent_map.get(blk["x0"], 0)
                node = {"name": name, "level": level, "type": "group", "children": []}

                stock_data = {}
                has_stock = False
                for i, col in enumerate(columns):
                    val = " ".join(blk["financials"][i]).strip()
                    if val:
                        stock_data[col["label"]] = val
                        has_stock = True

                if has_stock: node["stock"] = stock_data

                if "HSN/SAC:" in name:
                    node["type"] = "hsn_detail"
                elif name.startswith("Batch:"):
                    node["type"] = "batch_detail"
                elif name.startswith("Godown:"):
                    node["type"] = "godown_detail"
                elif has_stock:
                    node["type"] = "item"

                while len(hierarchy) > level: hierarchy.pop()

                if not hierarchy:
                    page_nodes.append(node)
                else:
                    hierarchy[-1].setdefault("children", []).append(node)

                if node["type"] in ("group", "item"):
                    hierarchy.append(node)

            full_tree.extend(page_nodes)
    return full_tree