from __future__ import annotations

from pathlib import Path
import re

import pandas as pd

from .parsers import parse_pdf
from .parsers.common import infer_mode

HIGH_VALUE_THRESHOLD = 50000.0
LOW_BALANCE_THRESHOLD = 1000.0


def clean_key(value):
    value = re.sub(r"\s+", " ", str(value or "").upper()).strip()
    return re.sub(r"\b\d{8,}\b", "<REF>", value)


def _text(value) -> str:
    return re.sub(r"\s+", " ", str(value or "")).strip()


def _amount(row) -> float:
    return float((row.get("debit") or 0) + (row.get("credit") or 0))


def _direction(row) -> str:
    return "Debit" if pd.notna(row.get("debit")) and float(row.get("debit") or 0) > 0 else "Credit"


def _extract_counterparty(narration: str, direction: str) -> str:
    raw = _text(narration)
    upper = raw.upper()
    if not raw:
        return "Unidentified"
    if "CASH DEP" in upper or "CASH DEPOSIT" in upper:
        return "Cash Deposit"
    if "CASH WITHDRAW" in upper or "ATM WDL" in upper or "ATM/CASH" in upper and direction == "Debit":
        return "Cash Withdrawal"
    if "BANK CHARGE" in upper or "CHARGES" in upper or "COMMISSION" in upper:
        return "Bank Charges"
    if "INTEREST" in upper:
        return "Bank Interest"
    if "/UPI/" in upper or upper.startswith("UPI/"):
        parts = [p.strip() for p in re.split(r"/", raw) if p.strip()]
        ignored = {"UPI", "DR", "CR", "NA", "PAYMENT", "PHONEPE", "PAYTM"}
        for part in parts[1:]:
            cleaned = re.sub(r"^X+\d+$", "", part, flags=re.I).strip()
            if cleaned and cleaned.upper() not in ignored and not re.fullmatch(r"\d{6,}", cleaned):
                return cleaned[:120]
        return "UPI / Mobile"
    for marker in ("NEFT/", "RTGS/", "IMPS/", "TRANSFER TO", "TRANSFER FROM"):
        if marker in upper:
            parts = [p.strip() for p in re.split(r"/", raw) if p.strip()]
            for part in parts[1:]:
                if re.fullmatch(r"[A-Z0-9]{10,}", part.upper()):
                    continue
                if re.fullmatch(r"\d+(?:\.\d+)?", part):
                    continue
                if part.upper() in {"NEFT", "RTGS", "IMPS", "P2A", "P2P", "BRANCH : ATM SERVICE BRANCH"}:
                    continue
                return part[:120]
    if "CTS-CHQ" in upper or "CLEARING" in upper:
        return "Cheque Clearing"
    return raw[:120]


def _classify_row(row) -> tuple[str, str]:
    narration = _text(row.get("narration"))
    upper = narration.upper()
    direction = row.get("direction") or _direction(row)
    mode = str(row.get("mode") or "Other")

    if "CASH DEP" in upper or "CASH DEPOSIT" in upper:
        return "Cash Deposit / Cash Sales", "Cash Deposit"
    if "CASH WITHDRAW" in upper or "ATM WDL" in upper or ("ATM/CASH" in upper and direction == "Debit"):
        return "Cash Withdrawal", "Cash Withdrawal"
    if any(token in upper for token in ("BANK CHARGE", "SERVICE CHARGE", "COMMISSION CHARGE", "IMPS COMMISSION", "SMS CHARGE")):
        return "Bank Charges", "Bank Charges"
    if "INTEREST" in upper:
        return ("Interest Paid" if direction == "Debit" else "Interest Received"), "Bank Interest"
    if any(token in upper for token in ("GST PAYMENT", "GST PMT", "CPIN", "GSTIN")) and direction == "Debit":
        return "GST Payment", _extract_counterparty(narration, direction)
    if any(token in upper for token in ("INCOME TAX", "ITNS", "TDS", "OLTAS", "NSDL TAX")) and direction == "Debit":
        return "Tax / TDS Payment", _extract_counterparty(narration, direction)
    if any(token in upper for token in ("SALARY", "PAYROLL")) and direction == "Debit":
        return "Salary Payment", _extract_counterparty(narration, direction)
    if "RENT" in upper and direction == "Debit":
        return "Rent Expense", _extract_counterparty(narration, direction)
    if any(token in upper for token in ("ELECTRICITY", "TANGEDCO", "EB BILL")) and direction == "Debit":
        return "Electricity Expense", _extract_counterparty(narration, direction)
    if any(token in upper for token in ("PHONEPE", "PAYTM", "RAZORPAY", "ONE 97", "PAYMENT AGGREGATOR", "ESCROW")) and direction == "Credit":
        return "Payment Aggregator Settlement", _extract_counterparty(narration, direction)
    if "REFUND" in upper or "REVERSAL" in upper:
        return "Refund / Reversal", _extract_counterparty(narration, direction)
    if any(token in upper for token in ("SELF", "OWN ACCOUNT", "SELF TRANSFER")) or "TRANSFER FROM" in upper and "MOBILE TRANSFER" in upper:
        return "Self Transfer / Contra", _extract_counterparty(narration, direction)
    if mode == "UPI" or "/UPI/" in upper or upper.startswith("UPI/"):
        return ("UPI Payment / Expense" if direction == "Debit" else "UPI Customer Receipt"), _extract_counterparty(narration, direction)
    if mode == "NEFT" or "NEFT" in upper:
        return ("NEFT Payment" if direction == "Debit" else "NEFT Receipt"), _extract_counterparty(narration, direction)
    if mode == "RTGS" or "RTGS" in upper:
        return ("RTGS Payment" if direction == "Debit" else "RTGS Receipt"), _extract_counterparty(narration, direction)
    if mode == "IMPS" or "IMPS" in upper:
        return ("IMPS Payment" if direction == "Debit" else "IMPS Receipt"), _extract_counterparty(narration, direction)
    if "CTS-CHQ" in upper or "CLEARING" in upper or mode == "Cheque":
        return ("Cheque Payment" if direction == "Debit" else "Cheque Deposit"), "Cheque Clearing"
    if direction == "Debit":
        return "Other Bank Payment / Review", _extract_counterparty(narration, direction)
    return "Other Bank Receipt / Review", _extract_counterparty(narration, direction)


def _review_note(row) -> str:
    notes: list[str] = []
    amount = float(row.get("amount") or 0)
    balance = row.get("balance")
    category = str(row.get("category") or "")
    counterparty = str(row.get("counterparty") or "")
    if amount >= HIGH_VALUE_THRESHOLD:
        notes.append(f"High value transaction >= {HIGH_VALUE_THRESHOLD:,.0f}")
    if category == "Cash Deposit / Cash Sales":
        notes.append("Cash deposit - verify cash book/source")
    elif category == "Cash Withdrawal":
        notes.append("Cash withdrawal - verify cash book/use")
    if category.endswith("/ Review") or counterparty == "Unidentified":
        notes.append("Narration requires manual classification")
    if amount >= 10000 and amount % 10000 == 0:
        notes.append("Round/high amount - verify nature")
    if pd.notna(balance) and float(balance) < LOW_BALANCE_THRESHOLD:
        notes.append("Low balance after transaction")
    if bool(row.get("exact_duplicate")):
        notes.append("Exact duplicate / overlapping statement row")
    elif bool(row.get("possible_duplicate")):
        notes.append("Possible duplicate - review before exclusion")
    return "; ".join(dict.fromkeys(notes))


def enrich(df, classification_enabled: bool = True):
    if df.empty:
        return df
    x = df.copy()
    x["mode"] = x["narration"].map(infer_mode)
    x["amount"] = x["debit"].fillna(0) + x["credit"].fillna(0)
    x["direction"] = x.apply(_direction, axis=1)
    x["narration_key"] = x["narration"].map(clean_key)
    x["exact_key"] = x.apply(
        lambda r: f"{r.transaction_date}|{r.value_date}|{r.debit}|{r.credit}|{r.balance}|{clean_key(r.narration)}",
        axis=1,
    )
    x["exact_duplicate"] = x.duplicated("exact_key", keep=False)
    x["possible_key"] = x.apply(
        lambda r: f"{r.transaction_date}|{r.direction}|{r.amount:.2f}|{r.narration_key}", axis=1
    )
    x["possible_duplicate"] = x.duplicated("possible_key", keep=False) & ~x["exact_duplicate"]
    if classification_enabled:
        classified = x.apply(_classify_row, axis=1, result_type="expand")
        x["category"] = classified[0]
        x["counterparty"] = classified[1]
        x["review_note"] = x.apply(_review_note, axis=1)
    else:
        x["category"] = "Classification disabled"
        x["counterparty"] = x["narration"].map(lambda value: _text(value)[:120] or "Unidentified")
        x["review_note"] = x.apply(_review_note, axis=1)
    return x


def analyze_files(paths, customer_override="", account_override="", bank_hint="auto", classification_enabled=True):
    metas = []
    frames = []
    for path in paths:
        meta, df = parse_pdf(path, bank_hint=bank_hint)
        if customer_override:
            meta.customer_name = customer_override
            df["customer_name"] = customer_override
        if account_override:
            meta.account_number = account_override
            df["account_number"] = account_override
        metas.append(meta)
        frames.append(df)
    all_df = enrich(pd.concat(frames, ignore_index=True) if frames else pd.DataFrame(), classification_enabled)
    unique_df = all_df.drop_duplicates("exact_key", keep="first").copy() if not all_df.empty else all_df.copy()
    return metas, all_df, unique_df


def reconcile(metas, all_df):
    rows = []
    for meta in metas:
        data = all_df[all_df.source_file.eq(meta.source_file)] if not all_df.empty else pd.DataFrame()
        extracted_debit = float(data.debit.sum()) if not data.empty else 0
        extracted_credit = float(data.credit.sum()) if not data.empty else 0
        last_balance = float(data.balance.dropna().iloc[-1]) if not data.empty and data.balance.notna().any() else None
        debit_diff = None if meta.total_debit is None else round(extracted_debit - meta.total_debit, 2)
        credit_diff = None if meta.total_credit is None else round(extracted_credit - meta.total_credit, 2)
        closing_diff = None if meta.closing_balance is None or last_balance is None else round(last_balance - meta.closing_balance, 2)
        rows.append(
            {
                **meta.to_dict(),
                "extracted_transactions": len(data),
                "extracted_debit": extracted_debit,
                "debit_difference": debit_diff,
                "extracted_credit": extracted_credit,
                "credit_difference": credit_diff,
                "extracted_closing_balance": last_balance,
                "closing_difference": closing_diff,
                "status": "Reconciled" if all(value in (None, 0, 0.0) for value in (debit_diff, credit_diff, closing_diff)) else "Review",
            }
        )
    return pd.DataFrame(rows)


def monthly_summary(df):
    if df.empty:
        return pd.DataFrame()
    x = df.copy()
    x["month"] = x.transaction_date.dt.to_period("M").astype(str)
    summary = x.groupby("month", dropna=False).agg(
        transaction_count=("amount", "size"),
        opening_balance=("balance", "first"),
        total_debit=("debit", "sum"),
        total_credit=("credit", "sum"),
        closing_balance=("balance", "last"),
    ).reset_index()
    summary["net_movement"] = summary.total_credit - summary.total_debit
    return summary


def mode_summary(df):
    if df.empty:
        return pd.DataFrame()
    return df.groupby(["mode", "direction"], dropna=False).agg(transaction_count=("amount", "size"), amount=("amount", "sum")).reset_index()


def category_summary(df):
    if df.empty:
        return pd.DataFrame()
    result = df.groupby("category", dropna=False).agg(
        transaction_count=("amount", "size"), debit=("debit", "sum"), credit=("credit", "sum")
    ).reset_index()
    result["net_credit_debit"] = result.credit - result.debit
    return result.sort_values(["transaction_count", "category"], ascending=[False, True])


def party_summary(df):
    if df.empty:
        return pd.DataFrame()
    result = df.groupby(["counterparty", "category"], dropna=False).agg(
        transaction_count=("amount", "size"), debit=("debit", "sum"), credit=("credit", "sum")
    ).reset_index()
    result["net_credit_debit"] = result.credit - result.debit
    return result.sort_values(["transaction_count", "counterparty"], ascending=[False, True])


def duplicate_summary(all_df):
    return pd.DataFrame(
        [
            {"check": "All extracted rows", "count": len(all_df)},
            {"check": "Exact duplicate rows", "count": int(all_df.exact_duplicate.sum()) if not all_df.empty else 0},
            {"check": "Possible duplicate rows", "count": int(all_df.possible_duplicate.sum()) if not all_df.empty else 0},
            {"check": "Unique rows after exact deduplication", "count": int(all_df.exact_key.nunique()) if not all_df.empty else 0},
        ]
    )


def draft_financials(df, metas):
    if df.empty:
        return pd.DataFrame(columns=["particulars", "amount", "treatment_notes", "finalisation_requirement"])
    categories = df.groupby("category", dropna=False).agg(debit=("debit", "sum"), credit=("credit", "sum"))
    receipt_categories = ["Cash Deposit / Cash Sales", "UPI Customer Receipt", "Payment Aggregator Settlement", "NEFT Receipt", "RTGS Receipt", "IMPS Receipt", "Cheque Deposit"]
    payment_categories = ["UPI Payment / Expense", "NEFT Payment", "RTGS Payment", "IMPS Payment", "Cheque Payment"]
    business_receipts = float(sum(categories.loc[c, "credit"] for c in receipt_categories if c in categories.index))
    other_receipts = float(sum(categories.loc[c, "credit"] for c in categories.index if c not in receipt_categories and categories.loc[c, "credit"] > 0))
    classified_payments = float(sum(categories.loc[c, "debit"] for c in payment_categories if c in categories.index))
    other_payments = float(sum(categories.loc[c, "debit"] for c in categories.index if c not in payment_categories and categories.loc[c, "debit"] > 0))
    opening = next((meta.opening_balance for meta in metas if meta.opening_balance is not None), None)
    closing = next((meta.closing_balance for meta in reversed(metas) if meta.closing_balance is not None), None)
    return pd.DataFrame(
        [
            ["Potential business receipts / collections", business_receipts, "Narration-based grouping of customer, cash and settlement credits", "Verify with GST/sales register and cash book"],
            ["Other / unclassified receipts", other_receipts, "May include capital, loans, contra and other receipts", "Classify with books and supporting records"],
            ["Classified bank payments", classified_payments, "Narration-based payment classification", "Verify expense, purchase and drawings treatment"],
            ["Other / unclassified bank payments", other_payments, "Requires review", "Classify before final accounts"],
            ["Opening bank balance", opening, "Per first uploaded statement", "Verify statement continuity"],
            ["Closing bank balance", closing, "Per last uploaded statement", "Agree with bank reconciliation"],
        ],
        columns=["particulars", "amount", "treatment_notes", "finalisation_requirement"],
    )


def _workbook_columns(df):
    preferred = [
        "transaction_date", "value_date", "narration", "counterparty", "category", "mode", "direction",
        "debit", "credit", "balance", "reference_no", "bank_name", "customer_name", "account_number",
        "source_file", "source_page", "parser_name", "exact_duplicate", "possible_duplicate", "review_note",
    ]
    return [column for column in preferred if column in df.columns]


def export_excel(output, metas, all_df, unique_df, financial_year="", selected_bank="auto", classification_enabled=True):
    recon = reconcile(metas, all_df)
    customer = next((meta.customer_name for meta in metas if meta.customer_name), "")
    account = next((meta.account_number for meta in metas if meta.account_number), "")
    banks = ", ".join(sorted({meta.bank_name for meta in metas}))
    exact_count = int(all_df.exact_duplicate.sum()) if not all_df.empty else 0
    possible_count = int(all_df.possible_duplicate.sum()) if not all_df.empty else 0
    opening = next((meta.opening_balance for meta in metas if meta.opening_balance is not None), None)
    closing = next((meta.closing_balance for meta in reversed(metas) if meta.closing_balance is not None), None)
    dashboard = pd.DataFrame(
        [
            ["Customer / Account Holder", customer, "Extracted from statement or user override"],
            ["Account Number", account, "Extracted from statement or user override"],
            ["Bank(s)", banks, "Detected/selected parser result"],
            ["Financial Year", financial_year or "Not specified", "Optional user input"],
            ["Statements Uploaded", len(metas), "PDF files processed"],
            ["Rows Extracted", len(all_df), "Before exact duplicate removal"],
            ["Exact Duplicate Rows", exact_count, "Includes overlapping statement rows"],
            ["Possible Duplicate Rows", possible_count, "Requires manual review"],
            ["Unique Transactions", len(unique_df), "Used for summaries"],
            ["Opening Balance", opening, "From first available statement summary"],
            ["Total Debit (Unique)", float(unique_df.debit.sum()) if not unique_df.empty else 0, "After exact duplicate removal"],
            ["Total Credit (Unique)", float(unique_df.credit.sum()) if not unique_df.empty else 0, "After exact duplicate removal"],
            ["Closing Balance", closing, "From last available statement summary"],
            ["Classification", "Enabled" if classification_enabled else "Disabled", "Generic narration-based classification"],
        ],
        columns=["Metric", "Value", "Remarks"],
    )
    all_export = all_df[_workbook_columns(all_df)].copy() if not all_df.empty else all_df.copy()
    unique_export = unique_df[_workbook_columns(unique_df)].copy() if not unique_df.empty else unique_df.copy()
    review_items = unique_export[unique_export["review_note"].fillna("").ne("")].copy() if not unique_export.empty else unique_export.copy()
    notes = pd.DataFrame(
        {
            "Assumption / Method": [
                "Extraction method",
                "Debit/Credit identification",
                "Exact duplicate detection",
                "Possible duplicate detection",
                "Bank selection",
                "Classification",
                "Important limitation",
                "Files analysed",
            ],
            "Details": [
                "Transactions are extracted using the selected bank parser or automatic parser detection.",
                "Debit and credit fields are taken from the bank-specific parser and reconciled to statement summaries where available.",
                "Exact duplicates use transaction date, value date, debit, credit, balance and normalized narration; the first occurrence is retained.",
                "Possible duplicates use the same date, direction, amount and normalized narration and are not removed automatically.",
                f"Selected option: {selected_bank or 'auto'}. Manual selection still validates that the PDF matches the selected bank format.",
                "Categories, counterparties and review notes are generic narration-based indicators and must be verified against books and source records.",
                "This workbook is an analysis aid, not a substitute for ledger, GST, inventory, receivable/payable, cash-book and supporting records.",
                "; ".join(meta.source_file for meta in metas),
            ],
        }
    )

    with pd.ExcelWriter(output, engine="xlsxwriter", datetime_format="dd-mmm-yyyy") as writer:
        dashboard.to_excel(writer, sheet_name="Dashboard", index=False)
        recon.to_excel(writer, sheet_name="Statement Reconciliation", index=False)
        all_export.to_excel(writer, sheet_name="All Extracted Rows", index=False)
        unique_export.to_excel(writer, sheet_name="Unique Transactions", index=False)
        all_export[all_df.exact_duplicate.to_numpy()].to_excel(writer, sheet_name="Exact Duplicates", index=False)
        all_export[all_df.possible_duplicate.to_numpy()].to_excel(writer, sheet_name="Possible Duplicates", index=False)
        monthly_summary(unique_df).to_excel(writer, sheet_name="Monthly Summary", index=False)
        mode_summary(unique_df).to_excel(writer, sheet_name="Mode Summary", index=False)
        category_summary(unique_df).to_excel(writer, sheet_name="Category Summary", index=False)
        party_summary(unique_df).to_excel(writer, sheet_name="Party Summary", index=False)
        review_items.to_excel(writer, sheet_name="Review Items", index=False)
        draft_financials(unique_df, metas).to_excel(writer, sheet_name="Draft Financials", index=False)
        duplicate_summary(all_df).to_excel(writer, sheet_name="Duplicate Summary", index=False)
        notes.to_excel(writer, sheet_name="Assumptions", index=False)

        workbook = writer.book
        header = workbook.add_format({"bold": True, "bg_color": "#1F4E78", "font_color": "white", "border": 1})
        money = workbook.add_format({"num_format": "#,##0.00"})
        date_format = workbook.add_format({"num_format": "dd-mmm-yyyy"})
        for name, worksheet in writer.sheets.items():
            worksheet.freeze_panes(1, 0)
            worksheet.set_row(0, 22, header)
            max_col = max(0, worksheet.dim_colmax)
            if worksheet.dim_rowmax >= 0:
                worksheet.autofilter(0, 0, worksheet.dim_rowmax, max_col)
            worksheet.set_column(0, max_col, 18)
        writer.sheets["Dashboard"].set_column("A:A", 34)
        writer.sheets["Dashboard"].set_column("B:B", 28)
        writer.sheets["Dashboard"].set_column("C:C", 44)
        for sheet in ("All Extracted Rows", "Unique Transactions", "Exact Duplicates", "Possible Duplicates", "Review Items"):
            ws = writer.sheets[sheet]
            ws.set_column("A:B", 13, date_format)
            ws.set_column("C:C", 58)
            ws.set_column("D:E", 30)
        for sheet in ("Category Summary", "Party Summary", "Monthly Summary", "Mode Summary", "Draft Financials"):
            writer.sheets[sheet].set_column(1, max(1, writer.sheets[sheet].dim_colmax), 18, money)
    return output