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"""
raq_extractor.py
----------------
Generic extractor for the RBI 'Report on Asset Quality' (RAQ) XBRL template.
The template has 25 sheets, each with a different table layout. Layouts are
described by IRIS/iFile markers (#LAYOUTSCSR#, #LAYOUTECSR#, #TABLE#, #CustPlc#,
#SERIAL#, #TYPDIM#) and the data rows are tagged in column B with an XBRL
element reference of the form `in-rbi-rep.xsd#in-rbi-rep_<Concept>`.
Rather than hard-coding 25 different parsers, we harvest every populated cell
into a single tidy "long" frame, attaching the best-effort row label (nearest
text to the left) and column header (nearest text above). This is robust to the
structural differences between sheets and is exactly the shape the data-quality
engine needs.
Public API
----------
extract_workbook(path) -> RAQExtract
    .meta        : dict   (return code, bank, period, dates, version ...)
    .long        : DataFrame  one row per populated data cell
    .by_sheet    : dict[str, DataFrame]  convenience per-sheet pivots
    .sheets      : list[str]  data sheets found
"""

from __future__ import annotations
import re
from dataclasses import dataclass, field
from datetime import datetime

import openpyxl
import pandas as pd

ELEMENT_PREFIX = "in-rbi-rep.xsd#"
MARKERS = {
    "#LAYOUTSCSR#", "#LAYOUTECSR#", "#LAYOUTSCER#", "#LAYOUTECER#",
    "#TABLE#", "#CustPlc#", "#SERIAL#", "#TYPDIM#", "#LAYOUTSCSR#",
}
# sheets that are config / taxonomy plumbing, not reportable data
NON_DATA_SHEETS = {
    "StartUpDataSheet", "MainSheet", "Navigation", "StartUp",
    "Data", "+FootnoteTexts", "+Elements", "+Lineitems",
}


def _is_marker(v) -> bool:
    return isinstance(v, str) and v.strip() in MARKERS


def _is_element(v) -> bool:
    return isinstance(v, str) and v.startswith(ELEMENT_PREFIX)


def _is_text(v) -> bool:
    return isinstance(v, str) and v.strip() != "" and not _is_marker(v) and not _is_element(v)


def _clean_label(v: str) -> str:
    s = re.sub(r"\s+", " ", str(v)).strip()
    return s


def _concept_from_element(v: str) -> str:
    # in-rbi-rep.xsd#in-rbi-rep_AssetsCurrentTermLoans -> AssetsCurrentTermLoans
    tail = v.split("#")[-1]
    return tail.split("in-rbi-rep_")[-1] if "in-rbi-rep_" in tail else tail


@dataclass
class RAQExtract:
    meta: dict = field(default_factory=dict)
    long: pd.DataFrame = field(default_factory=pd.DataFrame)
    by_sheet: dict = field(default_factory=dict)
    sheets: list = field(default_factory=list)


def _extract_meta(wb) -> dict:
    """Pull return-level metadata from the General Information sheet."""
    meta = {}
    if "General Information" not in wb.sheetnames:
        return meta
    ws = wb["General Information"]
    for row in ws.iter_rows(values_only=True):
        cells = [c for c in row if c is not None]
        # rows look like: <element/key> | <Label> | <Value>
        if len(cells) >= 2:
            key = _clean_label(cells[-2]) if isinstance(cells[-2], str) else None
            val = cells[-1]
            if key and isinstance(key, str):
                low = key.lower()
                if low in (
                    "return name", "return code", "reporting institution",
                    "bank code", "for the period ended", "reporting frequency",
                    "date of report", "status", "validation status",
                    "bank category", "return version", "start date",
                ):
                    meta[key] = val
    return meta


def _header_for_column(header_rows, col_idx) -> str:
    """Join all header-row texts found at this column (top-to-bottom)."""
    parts = []
    for hr in header_rows:
        v = hr.get(col_idx)
        if v and v not in parts:
            parts.append(v)
    return " | ".join(parts)


def _extract_sheet(ws) -> pd.DataFrame:
    """Harvest every populated data cell on a sheet into tidy rows."""
    records = []
    header_rows = []          # list of {col_idx: text} captured since last #TABLE# open
    current_row_label = None  # last text label seen at left of a data block
    table_open = False
    table_id = None

    rows = list(ws.iter_rows())
    for ri, row in enumerate(rows, start=1):
        values = [c.value for c in row]
        texts = {ci: _clean_label(v) for ci, v in enumerate(values, 1) if _is_text(v)}
        has_marker_table = any(isinstance(v, str) and v.strip() == "#TABLE#" for v in values)
        has_element = any(_is_element(v) for v in values)
        # capture a GUID block id (table identity) -> first cell that looks like a guid
        for v in values:
            if isinstance(v, str) and re.fullmatch(r"[0-9a-f]{8}-[0-9a-f]{4}-.*", v.strip()):
                table_id = v.strip()

        if has_marker_table:
            table_open = not table_open
            if table_open:
                header_rows = []  # reset header context at the start of a data block
            continue

        # Header-ish row: multiple texts, no element ref -> treat as column headers
        if not has_element and len(texts) >= 2 and not table_open:
            header_rows.append(texts)
            continue
        if not has_element and len(texts) >= 2 and table_open:
            header_rows.append(texts)
            continue

        if has_element:
            # row label = left-most text cell on this row (cols C/D), else carry last
            label = None
            for ci in sorted(texts):
                if ci >= 3:  # labels live from column C onward
                    label = texts[ci]
                    break
            if label:
                current_row_label = label
            element_ref = next((v for v in values if _is_element(v)), None)
            concept = _concept_from_element(element_ref) if element_ref else None
            # numeric / value cells
            for ci, v in enumerate(values, 1):
                if isinstance(v, bool):
                    continue
                is_num = isinstance(v, (int, float))
                is_val_text = isinstance(v, str) and _is_text(v) and ci >= 4 and texts.get(ci) != current_row_label
                if is_num or is_val_text:
                    records.append({
                        "row_label": current_row_label,
                        "concept": concept,
                        "col_idx": ci,
                        "col_header": _header_for_column(header_rows, ci),
                        "value": v,
                        "is_numeric": bool(is_num),
                        "cell": f"{openpyxl.utils.get_column_letter(ci)}{ri}",
                        "table_id": table_id,
                    })
        else:
            # single stray text -> may be a running row label for following block
            if len(texts) == 1:
                ci = next(iter(texts))
                if ci >= 3:
                    current_row_label = texts[ci]

    return pd.DataFrame.from_records(records)


def extract_workbook(path: str) -> RAQExtract:
    wb = openpyxl.load_workbook(path, data_only=True, read_only=False)
    meta = _extract_meta(wb)

    frames = []
    by_sheet = {}
    data_sheets = []
    for name in wb.sheetnames:
        if name in NON_DATA_SHEETS:
            continue
        ws = wb[name]
        df = _extract_sheet(ws)
        if df.empty:
            continue
        df.insert(0, "sheet", name)
        frames.append(df)
        by_sheet[name] = df
        data_sheets.append(name)

    long = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
    return RAQExtract(meta=meta, long=long, by_sheet=by_sheet, sheets=data_sheets)


if __name__ == "__main__":
    import sys
    p = sys.argv[1] if len(sys.argv) > 1 else "RAQ.XLSX"
    ex = extract_workbook(p)
    print("META:", ex.meta)
    print("DATA SHEETS:", len(ex.sheets))
    print("TOTAL DATA CELLS:", len(ex.long))
    print(ex.long.head(20).to_string())

	"""
	raq_extractor.py
	----------------
	Generic extractor for the RBI 'Report on Asset Quality' (RAQ) XBRL template.

	The template has 25 sheets, each with a different table layout. Layouts are
	described by IRIS/iFile markers (#LAYOUTSCSR#, #LAYOUTECSR#, #TABLE#, #CustPlc#,
	#SERIAL#, #TYPDIM#) and the data rows are tagged in column B with an XBRL
	element reference of the form `in-rbi-rep.xsd#in-rbi-rep_<Concept>`.

	Rather than hard-coding 25 different parsers, we harvest every populated cell
	into a single tidy "long" frame, attaching the best-effort row label (nearest
	text to the left) and column header (nearest text above). This is robust to the
	structural differences between sheets and is exactly the shape the data-quality
	engine needs.

	Public API
	----------
	extract_workbook(path) -> RAQExtract
	.meta : dict (return code, bank, period, dates, version ...)
	.long : DataFrame one row per populated data cell
	.by_sheet : dict[str, DataFrame] convenience per-sheet pivots
	.sheets : list[str] data sheets found
	"""

	from __future__ import annotations
	import re
	from dataclasses import dataclass, field
	from datetime import datetime

	import openpyxl
	import pandas as pd

	ELEMENT_PREFIX = "in-rbi-rep.xsd#"
	MARKERS = {
	"#LAYOUTSCSR#", "#LAYOUTECSR#", "#LAYOUTSCER#", "#LAYOUTECER#",
	"#TABLE#", "#CustPlc#", "#SERIAL#", "#TYPDIM#", "#LAYOUTSCSR#",
	}
	# sheets that are config / taxonomy plumbing, not reportable data
	NON_DATA_SHEETS = {
	"StartUpDataSheet", "MainSheet", "Navigation", "StartUp",
	"Data", "+FootnoteTexts", "+Elements", "+Lineitems",
	}


	def _is_marker(v) -> bool:
	return isinstance(v, str) and v.strip() in MARKERS


	def _is_element(v) -> bool:
	return isinstance(v, str) and v.startswith(ELEMENT_PREFIX)


	def _is_text(v) -> bool:
	return isinstance(v, str) and v.strip() != "" and not _is_marker(v) and not _is_element(v)


	def _clean_label(v: str) -> str:
	s = re.sub(r"\s+", " ", str(v)).strip()
	return s


	def _concept_from_element(v: str) -> str:
	# in-rbi-rep.xsd#in-rbi-rep_AssetsCurrentTermLoans -> AssetsCurrentTermLoans
	tail = v.split("#")[-1]
	return tail.split("in-rbi-rep_")[-1] if "in-rbi-rep_" in tail else tail


	@dataclass
	class RAQExtract:
	meta: dict = field(default_factory=dict)
	long: pd.DataFrame = field(default_factory=pd.DataFrame)
	by_sheet: dict = field(default_factory=dict)
	sheets: list = field(default_factory=list)


	def _extract_meta(wb) -> dict:
	"""Pull return-level metadata from the General Information sheet."""
	meta = {}
	if "General Information" not in wb.sheetnames:
	return meta
	ws = wb["General Information"]
	for row in ws.iter_rows(values_only=True):
	cells = [c for c in row if c is not None]
	# rows look like: <element/key> \| <Label> \| <Value>
	if len(cells) >= 2:
	key = _clean_label(cells[-2]) if isinstance(cells[-2], str) else None
	val = cells[-1]
	if key and isinstance(key, str):
	low = key.lower()
	if low in (
	"return name", "return code", "reporting institution",
	"bank code", "for the period ended", "reporting frequency",
	"date of report", "status", "validation status",
	"bank category", "return version", "start date",
	):
	meta[key] = val
	return meta


	def _header_for_column(header_rows, col_idx) -> str:
	"""Join all header-row texts found at this column (top-to-bottom)."""
	parts = []
	for hr in header_rows:
	v = hr.get(col_idx)
	if v and v not in parts:
	parts.append(v)
	return " \| ".join(parts)


	def _extract_sheet(ws) -> pd.DataFrame:
	"""Harvest every populated data cell on a sheet into tidy rows."""
	records = []
	header_rows = [] # list of {col_idx: text} captured since last #TABLE# open
	current_row_label = None # last text label seen at left of a data block
	table_open = False
	table_id = None

	rows = list(ws.iter_rows())
	for ri, row in enumerate(rows, start=1):
	values = [c.value for c in row]
	texts = {ci: _clean_label(v) for ci, v in enumerate(values, 1) if _is_text(v)}
	has_marker_table = any(isinstance(v, str) and v.strip() == "#TABLE#" for v in values)
	has_element = any(_is_element(v) for v in values)
	# capture a GUID block id (table identity) -> first cell that looks like a guid
	for v in values:
	if isinstance(v, str) and re.fullmatch(r"[0-9a-f]{8}-[0-9a-f]{4}-.*", v.strip()):
	table_id = v.strip()

	if has_marker_table:
	table_open = not table_open
	if table_open:
	header_rows = [] # reset header context at the start of a data block
	continue

	# Header-ish row: multiple texts, no element ref -> treat as column headers
	if not has_element and len(texts) >= 2 and not table_open:
	header_rows.append(texts)
	continue
	if not has_element and len(texts) >= 2 and table_open:
	header_rows.append(texts)
	continue

	if has_element:
	# row label = left-most text cell on this row (cols C/D), else carry last
	label = None
	for ci in sorted(texts):
	if ci >= 3: # labels live from column C onward
	label = texts[ci]
	break
	if label:
	current_row_label = label
	element_ref = next((v for v in values if _is_element(v)), None)
	concept = _concept_from_element(element_ref) if element_ref else None
	# numeric / value cells
	for ci, v in enumerate(values, 1):
	if isinstance(v, bool):
	continue
	is_num = isinstance(v, (int, float))
	is_val_text = isinstance(v, str) and _is_text(v) and ci >= 4 and texts.get(ci) != current_row_label
	if is_num or is_val_text:
	records.append({
	"row_label": current_row_label,
	"concept": concept,
	"col_idx": ci,
	"col_header": _header_for_column(header_rows, ci),
	"value": v,
	"is_numeric": bool(is_num),
	"cell": f"{openpyxl.utils.get_column_letter(ci)}{ri}",
	"table_id": table_id,
	})
	else:
	# single stray text -> may be a running row label for following block
	if len(texts) == 1:
	ci = next(iter(texts))
	if ci >= 3:
	current_row_label = texts[ci]

	return pd.DataFrame.from_records(records)


	def extract_workbook(path: str) -> RAQExtract:
	wb = openpyxl.load_workbook(path, data_only=True, read_only=False)
	meta = _extract_meta(wb)

	frames = []
	by_sheet = {}
	data_sheets = []
	for name in wb.sheetnames:
	if name in NON_DATA_SHEETS:
	continue
	ws = wb[name]
	df = _extract_sheet(ws)
	if df.empty:
	continue
	df.insert(0, "sheet", name)
	frames.append(df)
	by_sheet[name] = df
	data_sheets.append(name)

	long = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
	return RAQExtract(meta=meta, long=long, by_sheet=by_sheet, sheets=data_sheets)


	if __name__ == "__main__":
	import sys
	p = sys.argv[1] if len(sys.argv) > 1 else "RAQ.XLSX"
	ex = extract_workbook(p)
	print("META:", ex.meta)
	print("DATA SHEETS:", len(ex.sheets))
	print("TOTAL DATA CELLS:", len(ex.long))
	print(ex.long.head(20).to_string())