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


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

def enrich(df):
    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['debit'].notna().map({True:'Debit',False:'Credit'})
    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)
    # possible duplicate: same date, direction, amount and normalized narration across different source files
    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']
    return x

def analyze_files(paths, customer_override='', account_override=''):
    metas=[]; dfs=[]
    for p in paths:
        meta,df=parse_pdf(p)
        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); dfs.append(df)
    all_df=enrich(pd.concat(dfs,ignore_index=True) if dfs else pd.DataFrame())
    # remove exact overlap duplicates, retaining first source occurrence
    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 m in metas:
        d=all_df[all_df.source_file.eq(m.source_file)] if not all_df.empty else pd.DataFrame()
        ed=float(d.debit.sum()) if not d.empty else 0
        ec=float(d.credit.sum()) if not d.empty else 0
        last=float(d.balance.dropna().iloc[-1]) if not d.empty and d.balance.notna().any() else None
        rows.append({**m.to_dict(),'extracted_transactions':len(d),'extracted_debit':ed,'extracted_credit':ec,'extracted_closing_balance':last,
                     'debit_difference':None if m.total_debit is None else round(ed-m.total_debit,2),
                     'credit_difference':None if m.total_credit is None else round(ec-m.total_credit,2),
                     'closing_difference':None if m.closing_balance is None or last is None else round(last-m.closing_balance,2)})
    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)
    return x.groupby('month',dropna=False).agg(transaction_count=('amount','size'),total_debit=('debit','sum'),total_credit=('credit','sum'),net_movement=('credit','sum')).reset_index().assign(net_movement=lambda z:z.total_credit-z.total_debit)

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

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 export_excel(output,metas,all_df,unique_df):
    recon=reconcile(metas,all_df)
    cust=next((m.customer_name for m in metas if m.customer_name),'')
    acct=next((m.account_number for m in metas if m.account_number),'')
    banks=', '.join(sorted({m.bank_name for m in metas}))
    dashboard=pd.DataFrame([
        ['Customer / Account Holder',cust],['Account Number',acct],['Bank(s)',banks],
        ['Statements Uploaded',len(metas)],['Rows Extracted',len(all_df)],['Unique Transactions',len(unique_df)],
        ['Exact Duplicate Rows',int(all_df.exact_duplicate.sum()) if not all_df.empty else 0],
        ['Possible Duplicate Rows',int(all_df.possible_duplicate.sum()) if not all_df.empty else 0],
        ['Total Debit (Unique)',float(unique_df.debit.sum()) if not unique_df.empty else 0],
        ['Total Credit (Unique)',float(unique_df.credit.sum()) if not unique_df.empty else 0],
    ],columns=['Metric','Value'])
    with pd.ExcelWriter(output,engine='xlsxwriter',datetime_format='dd-mmm-yyyy') as w:
        dashboard.to_excel(w,'Dashboard',index=False)
        recon.to_excel(w,'Statement Reconciliation',index=False)
        all_df.to_excel(w,'All Extracted Rows',index=False)
        unique_df.to_excel(w,'Unique Transactions',index=False)
        all_df[all_df.exact_duplicate].to_excel(w,'Exact Duplicates',index=False)
        all_df[all_df.possible_duplicate].to_excel(w,'Possible Duplicates',index=False)
        monthly_summary(unique_df).to_excel(w,'Monthly Summary',index=False)
        mode_summary(unique_df).to_excel(w,'Mode Summary',index=False)
        duplicate_summary(all_df).to_excel(w,'Duplicate Summary',index=False)
        notes=pd.DataFrame({'Notes':[
            'Exact duplicates use transaction date, value date, debit, credit, balance and normalized narration.',
            'Possible duplicates use same date, direction, amount and normalized narration; review before deletion.',
            'Bank-specific parsers are selected automatically. Customer name/account number can be manually overridden in the app.',
            'The workbook is a bank-statement analysis aid, not a substitute for ledger, GST, inventory, receivable/payable and cash-book records.'
        ]})
        notes.to_excel(w,'Notes',index=False)
        wb=w.book
        head=wb.add_format({'bold':True,'bg_color':'#1F4E78','font_color':'white','border':1})
        money=wb.add_format({'num_format':'#,##0.00'})
        for name,ws in w.sheets.items():
            ws.freeze_panes(1,0); ws.autofilter(0,0,0,max(0,ws.dim_colmax))
            ws.set_row(0,22,head)
            ws.set_column(0,max(0,ws.dim_colmax),18)
        w.sheets['Dashboard'].set_column('A:A',32); w.sheets['Dashboard'].set_column('B:B',28)
    return output